57 minutes
SDS 839: Double Your Data Salary in 11 Months, with Jess Ramos
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Jess Ramos is redefining success in data analytics. As the Founder of Big Data Energy and a Senior Data Analyst at Crunchbase, she’s mastered the art of salary negotiation, built a massive social media following, and turned her passion for data into a thriving personal brand. She reveals how she doubled her salary in under a year, created her own SQL course, and advocates for women in STEM.
Items mentioned in this podcast:
Follow Jess:
Interested in sponsoring a Super Data Science Podcast episode? Email natalie@superdatascience.com for sponsorship information.
About Jess Ramos
Jess is a senior data analyst in tech with a focus on product analytics in the Software as a Service industry and has over 300K followers between LinkedIn and Instagram, where she shares tech & data tips to her community. As the founder of Big Data Energy, she teaches through data analytics courses and partners with brands to amplify their voices. Check out her new intermediate SQL course, Big SQL Energy, designed to practice solving real business problems in a modern tech stack.
Overview
How do you transform a feeling of being undervalued into a catalyst for career growth? Jess Ramos demonstrates how, doubling her salary in under a year through strategic negotiation and a clear understanding of her market value. By advocating for herself and staying composed in high-stakes conversations, she provides actionable steps for professionals aiming to elevate their careers.
Jess also shares her journey from a single viral LinkedIn post to becoming the founder of Big Data Energy, a brand recognized for its impact on data education and empowerment. She explains how her unique voice on social media led to partnerships with industry leaders like IBM and SAP, and how it inspired the creation of her "Big SQL Energy" course—a solution designed to address the lack of intermediate SQL training for data professionals.
Her expertise in SQL underscores why she considers it the essential skill for any data professional. SQL, she argues, is the backbone of data work, offering foundational capabilities in cleaning, merging, and analyzing information. Jess outlines its significance in solving real-world business problems and its versatility in bridging to other tools and technologies.
In a more personal exploration, Jess tackles the subtle and overt challenges faced by women in STEM. She shares compelling stories about navigating workplace biases, confronting societal stereotypes, and advocating for change. Her experiences shed light on the importance of fostering confidence, authenticity, and equity within the tech industry, offering inspiration for a more inclusive future.
In this episode you will learn:
Jess is a senior data analyst in tech with a focus on product analytics in the Software as a Service industry and has over 300K followers between LinkedIn and Instagram, where she shares tech & data tips to her community. As the founder of Big Data Energy, she teaches through data analytics courses and partners with brands to amplify their voices. Check out her new intermediate SQL course, Big SQL Energy, designed to practice solving real business problems in a modern tech stack.
Overview
How do you transform a feeling of being undervalued into a catalyst for career growth? Jess Ramos demonstrates how, doubling her salary in under a year through strategic negotiation and a clear understanding of her market value. By advocating for herself and staying composed in high-stakes conversations, she provides actionable steps for professionals aiming to elevate their careers.
Jess also shares her journey from a single viral LinkedIn post to becoming the founder of Big Data Energy, a brand recognized for its impact on data education and empowerment. She explains how her unique voice on social media led to partnerships with industry leaders like IBM and SAP, and how it inspired the creation of her "Big SQL Energy" course—a solution designed to address the lack of intermediate SQL training for data professionals.
Her expertise in SQL underscores why she considers it the essential skill for any data professional. SQL, she argues, is the backbone of data work, offering foundational capabilities in cleaning, merging, and analyzing information. Jess outlines its significance in solving real-world business problems and its versatility in bridging to other tools and technologies.
In a more personal exploration, Jess tackles the subtle and overt challenges faced by women in STEM. She shares compelling stories about navigating workplace biases, confronting societal stereotypes, and advocating for change. Her experiences shed light on the importance of fostering confidence, authenticity, and equity within the tech industry, offering inspiration for a more inclusive future.
In this episode you will learn:
- (03:42) How Jess got her start in data analytics
- (09:14) Why SQL is the most critical skill for data professionals
- (11:46) How Jess more than doubled her salary in less than a year
- (20:16) Tips for transitioning from a data job to creating your own business
- (31:20) The various routes to a career in data science
- (39:13) How Jess challenges STEM stereotypes
Items mentioned in this podcast:
- Big SQL Energy Intermediate course
- Coupon code for Jess' Big SQL Energy course: superdspod
- Crunchbase
- Freddie Mac
- IBM Think
- Jess’ LinkedIn Learning SQL course
- Power BI
- Tableau
- Weapons of Math Destruction by Cathy O’Neil
- DJI Osmo Pocket 3
- DJI Mic
- Big Data Energy Newsletter
- Big Data Energy Analytics LinkedIn
- SuperDataScience
- Virtual half-day conference on Agentic AI
- SDS special code for a free 30-day trial on O’Reilly: SDSPOD23
- Jon Krohn’s Generative AI with Large Language Models, hands-on training
- The Super Data Science Podcast Team
Follow Jess:
Podcast Transcript
Jon Krohn: 00:00:00
This is episode number 839 with Jess Ramos, founder of Big Data Energy Analytics.
00:00:13
Welcome to the Super Data Science podcast, the most listened to podcast in the Data Science industry. Each week we bring you fun and inspiring people and ideas, exploring the cutting edge of Machine Learning, AI, and related technologies that are transforming our world for the better. I'm your host, Jon Krohn. Thanks for joining me today. And now let's make the complex simple.
00:00:47
Welcome back to the Super Data Science podcast. Today we've got the charismatic, well-spoken and super intelligent Jess Ramos on the show. Jess is founder of Big Data Energy, a company that supports her in-demand courses on SQL and Data Analytics. She's also a Senior Data Analyst at Crunchbase, and previously worked as a Senior Risk Analyst and a Data Analytics Manager. Her popular social media content on SQL, Data Analytics, Data Science, tech advancements, and maximizing professional growth has led her to amassing over 300,000 followers across LinkedIn, Instagram, and TikTok. She holds a Bachelor's in Math, and she also holds a Master's in Business Analytics from the University of Georgia.
00:01:30
Today's episode will appeal especially to folks who are looking to grow their career or grow into a career in Data Analytics or Data Science or a related field. In today's episode Jess details how she more than doubled her data analyst salary in less than a year. She also talks about the questionable value of Data Science bootcamps, her controversial take on "girl math" that made a splash in international mainstream news, the unexpected viral post that launched her into social media fame, and essential advice for anyone starting their data career journey. Are you ready for this fun episode? Let's go.
00:02:14
Jess, welcome to the Super Data Science podcast. It's awesome to have you on the show. Where are you calling in from today?
Jess Ramos: 00:02:20
I'm in Athens, Georgia, so it's like a small-ish town, about an hour from Atlanta.
Jon Krohn: 00:02:25
Probably a pretty exciting time to be a few days before the U.S. election. It's interesting, when our listeners hear this, they will know the outcome that you and I don't.
Jess Ramos: 00:02:33
Yeah, that's crazy. We'll have to see.
Jon Krohn: 00:02:38
Let's dive right into the episode content. You have a math and business analytics educational background, and you've worked in both startups and more established organizations. Currently, your nine-to-five is as a senior data analyst at Crunchbase. But outside of that you've amassed a large following on social media, creating content about Data Science, Data Analytics, tech news and career development, and you have a unique and imaginative voice that obviously has been a hit. Because you've got a crazy number of followers, it's really wild. I've got some of the numbers here, at the time of recording over 200,000 followers on LinkedIn, 90,000 on Instagram, over 20,000 on TikTok. That's pretty wild. So, you've carved out a niche for yourself. It's obviously going very well. It's led to brand partnerships, speaking collaborations, and last year you founded the amazingly named Big Data Energy-
Jess Ramos: 00:03:35
Yes, I did.
Jon Krohn: 00:03:38
... company, and that supports all of these efforts. What's the origin story of your career in data and this Big Data Energy brand?
Jess Ramos: 00:03:48
Yeah, so my origin story for my career in data, I was studying math in school. Math is something that just always came kind of easy or kind of natural for me. I love to think through logic and numbers, but I didn't know what I actually wanted to do with my math degree. So that led me to pursue my master's in business analytics. And luckily I was also able to get an internship in undergrad and then again in grad school. So, I was able to test the waters and data and see if I actually liked the career or not. From there, that internship led to a full-time job. I moved up a few times to a senior data analyst, Data Analytics manager, and then I've switched jobs twice since then. So, it really set me up for a great career in data.
00:04:34
Then my origin story for content, I really just posted on LinkedIn one night on my phone. It was a Sunday night at 10 p.m. I was in bed, just typed up this post, and it went viral. It was about remote work and I was like, "What? This is so unexpected." I wasn't trying to be a content creator. I just found an amazing community on accident. And I was like, "I'm a one-hit wonder." No one is actually going to follow me. I was scared to post again and my coworkers were like, "No, Jess you're going to do fine, you'll be great." And from there I'm just super blessed, even hearing the numbers with you introducing me, I'm like, "I can't believe that many people follow me." I always think about it in football stadiums. I'm like, "That's three or four stadiums of people." And every time I go to a big event that I look around, I'm like, "This many people times three follow me on social media." It's crazy, but I'm super just blessed to have the opportunity to reach so many people, and resonate with so many people as well.
Jon Krohn: 00:05:36
Yeah, it's very cool. Congrats. And so, what was the second post about? Do you remember?
Jess Ramos: 00:05:42
The second one was about SQL, so I think it was top 10 optimization tips, I don't know. It was something to do with SQL, which was very different from my first post, but that one got around 1,000 likes, and this was back in the LinkedIn golden ages. This was two and a half years ago. Views were really good on LinkedIn back then. But yeah, that posted well too, and from there I kind of developed my niche over time. I've even changed it a little bit and reshaped it, depending on what my career goals are, so it's been fun.
Jon Krohn: 00:06:15
Nice. Well, that is exciting that your second-biggest post ever was about SQL, because you have a SQL course coming out right now. It's coming out the day after this episode is out. Tell us about it?
Jess Ramos: 00:06:28
So, I have had this dream of making my own SQL course for a long time. I was really lucky to make two courses with LinkedIn Learning. One of them was in SQL and I just learned how to make a really good course, and I got so much positive feedback from this course. From there it was my dream to have my own course that has my name on it. I have full control over and I can do whatever I want with it. This is something I've been dreaming about for a long time, and my course is called Big SQL Energy. So, we're still sticking with the branding, Big Data Energy, Big SQL Energy, keeping it fun. This course is an intermediate course, so I start off with joins and then go up from there. I do expect that people have a little bit of basic SQL knowledge coming in, but I really noticed that there's not enough intermediate courses out there, and that's why I really wanted to fill in that gap in the market.
00:07:20
There's so many basic SQL courses out there where people just learn SELECT * FROM TABLE, they're doing just super basic things, but they finish these courses and they don't actually know how to use SQL in the real world and how to use it to solve business problems. My course is actually taking those SQL skills to the next level and people are going to work through these business problems and these scenarios. And I even have little stakeholder scenarios in there too, like, "So-and-so just knocked on your door, it's a last-minute request. What are we going to do?" I'm really just trying to take that learner into a real-world environment and to really think critically through more intermediate and advanced concepts.
Jon Krohn: 00:08:02
Sounds really valuable and sounds like the kind of course that I need to be taking with my relatively rudimentary SQL capabilities. Where can this course be found? You say your name's on it. So, this is going to be available through the Big Data Energy website or something?
Jess Ramos: 00:08:17
Ironically, I don't even have a website for my company. I just have a Linktree. But I'm hosting it through Teachable. Teachable has been an amazing partner for me, and you wouldn't even know that it's hosted on Teachable because they do such an amazing job supporting course makers and creators, so it'll look like it really is my website. So, I'm able to plug in all my stuff there and someone is able to access the course through their portal and their platform. And I can give you a link too for the show notes if anyone's interested.
Jon Krohn: 00:08:48
Fantastic, that would be great. But it sounds like it will be easy for people to find you on social media, to find you on LinkedIn or Instagram or wherever they prefer to get their social media content, and then they'll be able to find your course through there. Because that's presumably something you're going to be talking about a lot over the next few months.
Jess Ramos: 00:09:06
Totally. It'll be in my Linktree all over social media. I promise, you cannot miss my course launch, I will find you.
Jon Krohn: 00:09:12
Nice. Yeah. SQL obviously is important in a Data Science career. For our listeners who maybe aren't already a data analyst or data scientist, can you explain why SQL is so critical?
Jess Ramos: 00:09:25
Yeah, so SQL is an older coding language. It's been out a long time, but it's something that is just the backbone of data. It is the bread and butter of really any type of data role, and it's something that companies have depended on for a long time and will continue to depend on for a long time. So, it's a very established skillset that companies are already using, and it's really not going to go anywhere. So SQL, if you're not familiar and you're listening, SQL is a language to query a database. So it's basically the language you use to pool data, make different data sets, reshape it, do data cleaning, everything you need to do to get data out of the database and get it ready for analysis. So, you can do analyses inside of SQL itself, or you can even pull the data into a business intelligence tool and do further analysis there. It's just a really important skill that you're going to use at least some point in your career, no matter what type of data role you're working in.
Jon Krohn: 00:10:25
It's the most popular language in Data Science or Data Analytics. There's often this kind of, although it happens less and less, but there was a time where there was this kind of Python versus R debate. Now it's kind of mostly settled.
Jess Ramos: 00:10:38
It's kind of settled.
Jon Krohn: 00:10:40
But during that whole era it was amusing to me, it was like, what is the language of Data Science? It's not Python or R, it's SQL. And that's still true.
Jess Ramos: 00:10:48
Exactly. People ask all the time, they're like, "Should I be learning Python if I want to be a data analyst?" And yes and no. Python of course is a very versatile programming language. You can do way more in Python, but if you're going for an entry-level role or maybe a company that isn't as modern with their technology, you might not even use Python on the job. So SQL is like the bread and butter.
Jon Krohn: 00:11:15
For sure. All right, so that gives us your SQL take, which obviously-
Jess Ramos: 00:11:22
I love SQL, if you can't tell.
Jon Krohn: 00:11:23
And there are already, as you said, there's also courses on LinkedIn Learning that you've created related to SQL, so people can check those out as well in addition to the new course, Big SQL Energy, obviously I love that name. We have your insights now, Jess, into why SQL is so important, and we've got your course. Again, that name is Big SQL Energy coming out tomorrow, which people can check out. Great. More broadly, in planning a career in Data Analytics or Data Science, you made a splash recently in social media through doubling your salary in 11 months. Do you want to fill us in on how our listeners can do that themselves?
Jess Ramos: 00:12:01
Absolutely. And we got to get the facts straight. I over doubled it in an 11 months time period, but who's counting? Just kidding. Yeah, so I did have an 11-month time period right before I started my role at Crunchbase about two years ago where I did over double my salary. And to walk you through all those steps, I was currently working as a Senior Data Analyst at a smaller fintech startup. And then from there I negotiated an internal raise. So, I basically went to my manager in HR and I was like, "I have a direct report. I'm doing all of this work, I'm leading all of these projects, I'm supporting the data for our capital raise, and I'm making $72,000 a year. I should probably be making a little bit more money for all the responsibilities I'm having."
00:12:53
I really built up that case of all of my accomplishments and responsibilities. I analyzed the market data, and I showed that senior analysts were making about six figures. I went back to them with all of that research and advocated for myself. And I was able to increase my salary to 95,000, which at the time, I mean an over 30% raise from an internal negotiation, I was stoked. I'm so proud of past Jess for doing that. And then from there I knew I needed to go somewhere else to continue learning. I wasn't really growing in my role anymore. And this is like six months later or so. From there I was on the job market, I just held my cards really close to my chest. I did not say any numbers in interviews. You will not get a number out of me ever in an interview.
Jon Krohn: 00:13:42
And that's a key negotiation tactic in anything, whether you're negotiating your salary or any kind of deal where money's involved that it's like negotiations 101. So probably a lot of our listeners are already aware of this, but you should never give your number. Like what's your salary expectation, that kind of thing, or what kind of salary are you on? Sorry, I interrupted you Jess, but.
Jess Ramos: 00:14:04
No, you're good. And I've heard too, whoever speaks first loses. That's what I've always heard about negotiation. And it's really easy to be nervous in an interview and just blurt something out. But I think just recognizing, this is a very uncomfortable conversation. It's the worst part of any interview. And just sit in that uncomfortableness and just be okay with it being awkward and dodging the question. But that's what you really have to do to get those increases and make sure you advocate for yourself. Because when I first entered the job market, again with my 95,000 salary, I was getting interviews for roles ranging from 100K all the way up to 135, 140, and I'm like, "I'm getting interviews for these roles. I would have been excited to even just get 110, 120, which was a big increase from where I was, but I just kept my lips tight, didn't say any numbers.
00:15:01
I let them give me their budget and flip the question back on them every time. And then at the end I negotiated an offer up to 150, which I declined to go to Freddie Mac for less money. And then I quit in four months. It was not a great place for me to work, terrible fit, but I did end up getting an offer for 150, which was well above where I was a few months prior. And then from there, leaving Freddie Mac after four months, I came to Crunchbase making over 150. And I'm going to be vague about my current salary only because I don't want to ruin my future negotiations.
Jon Krohn: 00:15:36
Sure. And I actually really appreciate you being so candid about the numbers on air. I mean, it's great that you are-
Jess Ramos: 00:15:42
I'm super open.
Jon Krohn: 00:15:45
... keeping the current one close to your chest. But to be able to go into concrete numbers like that, it is the kind of openness that you rarely hear people do on air. So thanks very much, Jess. What do you think the impact of the early internships, you mentioned those when we talked about your career background right at the onset of the episode. What do you think the impact of the early internships was on shaping your skills and the opportunities that you had subsequently?
Jess Ramos: 00:16:11
Oh, the impact was ginormous. I think it absolutely shaped my skills-
Jess Ramos: 00:45:53
Yeah, I think what you said, there's so much progress that has been made, but I think there is a way to go to, and I think it's the subtle thing. For example, maybe a guy finishes a presentation and people are like, "That was an excellent presentation. I really loved what you said about X, Y. Z." A girl does a presentation and it's like, "Oh, I loved your outfit. You look so cute. You were so well-spoken." I think sometimes the way that men and women get different types of compliments, or maybe the woman is asked to plan the office pizza party, or is asked to be the note taker, the presentation maker, do the admin kind of homemaking activities. There's these very subtle microaggressions that happen sometimes in the workplace. So yes, I think we've come a long way. But I always like to respectfully call that stuff out and speak on it because yeah, it does kind of hurt women's feelings sometimes when we're not seen as capable, even if the intention isn't to be rude or hurtful in any way.
Jon Krohn: 00:46:57
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00:47:41
All right, Jess, well, thank you for obviously this valuable conversation that we just had around gender and STEM and in data roles. And thanks for this whole episode. I've learned a ton from you and I've really enjoyed being on air recording with you. You have great data energy, it's so big.
Jess Ramos: 00:48:05
I love that.
Jon Krohn: 00:48:10
But yeah, before I let my guests go, I always ask for a book recommendation. Do you have anything for us?
Jess Ramos: 00:48:15
Absolutely, I sure do. One of my favorite books ever written is Weapons of Math Destruction.
Jon Krohn: 00:48:23
Yes.
Jess Ramos: 00:48:23
It's a very popular book in the data world, but that is a book that I read when I first started learning about data, and it's a book that I wish I could reread again for the first time, because it's just so good. It's written by Cathy O'Neill.
Jon Krohn: 00:48:36
Cathy O'Neill.
Jess Ramos: 00:48:37
And she is a female mathematician, which of course, I look up to her so much and think she's amazing. It talks about algorithms and how data can actually be used in an unethical way, even if that's not the intention. So it's really pulling into the soft skills and ethics side of data, which I love. And the book is not a technical book at all. There are no equations, no numbers, so it's a pretty easy and light read. It's not like you're reading a bunch of code or anything. And it's truly something that anybody can enjoy regardless of their background and their technical abilities. So especially if you're new in data, it'll completely open your mind to how data can be manipulated to tell a story or even used in a bad way towards society. So it's definitely a super interesting book and it'll totally change your perspective of data and a lot of societal things that we do.
Jon Krohn: 00:49:30
Awesome. Great recommendation. And I also just took a note down while you were speaking that I've got to ask Cathy O'Neill to be on the show. I haven't had her on.
Jess Ramos: 00:49:37
Do you know her?
Jon Krohn: 00:49:39
I don't know her. Do you?
Jess Ramos: 00:49:41
Okay. No, I was like, wait, are you friends with her?
Jon Krohn: 00:49:44
No, I mean, but yeah-
Jess Ramos: 00:49:4
She's great.
Jon Krohn: 00:49:47
... one of the other great privileges I enjoy in my life is that by hosting what we're pretty sure is the world's most listened to Data Science podcast, you can just kind of cold reach out to people. And not everyone says yes. Not everyone responds, but Cathy O'Neill will be an amazing cast. Would love to have her on.
Jess Ramos: 00:50:03
Oh, I for sure listen to that one.
Jon Krohn: 00:50:05
Nice. All right, great recommendation. You got the gears flowing. Gears flowing, gears moving in my head there. If my gears are flowing, I'm in trouble. I've melted. And very last question before we're kind of done here is. If you could kind of reel off for us the most important places that we can be following you. We know that we've got your course, Big SQL Energy coming out tomorrow, so probably when most people listen to this, it is already out. LinkedIn, we know is probably your primary social medium. You've also got your Instagram account, you've got your TikTok, both of which are super popular. Anywhere else that we should be following you?
Jess Ramos: 00:50:48
Yeah, so I also have my newsletter. I have about 15,000 subscribers. It's called the Big Data Energy Newsletter, of course. You can also find those links on my other social medias. But I send it out, I mean, it's supposed to be weekly, but I honestly get busy. So it's maybe three times a month on average. But I send out data tips. I talk about salary negotiations, SQL skills. I talk about all sorts of things data and tech career. And I promise I don't send any spam or annoying things. It's all just extended longer form content, like the stuff I write about on LinkedIn. So definitely follow me there. I also post any big announcements through there, like my course launch, and I'll probably be pushing some discounts and promos for my course too in there. So definitely stay there if you want to go there if you want to stay up to date with that kind of stuff too.
00:51:41
And then I'm also on YouTube. I only have a couple of long form videos up as of right now, but that's an area I want to grow into so much in 2025. So if you want to be one of my first subscribers there, subscribe to me like my videos and give me some comments to help me get started.
Jon Krohn: 00:51:57
Nice. I look forward to seeing how that YouTube develops. I'm sure you're going to overtake my YouTube numbers in months. You're really talented at this stuff. I've been taking notes through this episode and I'm going to be taking more notes afterward as well. It's really amazing what you're doing, Jess. Great work.
Jess Ramos: 00:52:16
I mean, we'll see if I can get all my camera equipment set up for YouTube. That's been the biggest barrier, but we'll see.
Jon Krohn: 00:52:22
Yeah, and so actually Jess had some tips for me. Why don't we tell them quickly, kind of these tips you were telling me about this camera before we started recording. Which I'm going to buy to up my ... So for me, the long format stuff, obviously these are long podcast episodes, long YouTube videos, that's been my bread and butter for years. I have been terrible at, I don't make or even view shorts or reels on Instagram or YouTube. Well, actually we do pay people to make YouTube shorts at least. And I've tried to have a TikTok channel, but nobody follows me on TikTok. It's really embarrassing. But this kind of thing that you recommended, so specifically it was this, I'm going to put it in the show notes from a company DJI, which is famous for big, when you have big movie cameras, DJI makes this thing called the Gimbal.
00:53:13
So you can be moving around with it and the camera stays steady. And so you told me about this Osmo Pocket 3. O-S-M-O Pocket 3. It's a little pocket camera, it's just a couple of hundred bucks and you can run around and get great quality video anywhere you are.
Jess Ramos: 00:53:29
Yeah, it's great. I have had this camera for maybe almost a year, but I love it because the quality is insane. It's a super small camera. It's like, for people not watching the video right now, the camera's maybe the size of an egg with a stick on it. It fits in a purse easily, so it's really great for traveling. But the quality is amazing, even though it's so small. It films in 4K horizontally and then I think 3K vertically. So all my videos, they just look so professional. And then it also has that gimbal, which is great for me, because I'm always literally on the run and I'm a very chaotic person, so it helps stabilize all my videos. It's just an amazing camera. And then of course I have the mics. They're the DJI mics with the charging case. That pair really well, nicely with it.
00:54:19
It's great for on-site interviews at conferences, or I even use them for filming short form at my desk. So yeah, it's just a really great camera. I had my first one stolen accidentally, and I went and bought it again. That's how much I loved it.
Jon Krohn: 00:54:32
It was stolen accidentally.
Jess Ramos: 00:54:33
Yeah, well, it was actually purposefully stolen by someone. I lost it on a train from Budapest to Vienna. Yeah, it was a sad day. Yeah, the Vienna police did not care my camera was stolen, which is fair. They have bigger problems, but.
Jon Krohn: 00:54:49
The saddest thing is the content that was on it that you lost.
Jess Ramos: 00:54:52
Luckily, like DJI does have an app, so I had most of my videos backed up to the cloud.
Jon Krohn: 00:54:58
Wow, that's wild.
Jess Ramos: 00:54:59
So most is backed up to the cloud and downloaded on my phone, which has a terabyte of storage. So most was backed up, but I did lose some videos from that trip, which was sad. I was like, "Wait, you can keep the camera, but send me back my memory card."
Jon Krohn: 00:55:13
Right? Yeah. All right, Jess. Well, thank you for these hardware tips now as well. I have literally made a note in my shopping cart to be purchasing these things. And maybe I will have some great social media presence someday in terms of my video content. Thanks Jess so much for agreeing to be on this episode, I've enjoyed it so much. And yeah, hopefully we can catch up with you again in a couple of years and see how things are coming along.
Jess Ramos: 00:55:39
Yeah, that'd be awesome. Thanks so much for having me.
Jon Krohn: 00:55:47
Awesome episode. In it Jess filled us in on how she increased her salary from $72,000 a year to over $150,000 a year. So more than doubled it in less than a year through building a strong case based on responsibilities and market data, and then during a company change by not revealing her current compensation. She also talked about how her content creation journey began with a viral LinkedIn post about remote work leading to partnerships with major companies like IBM and SAP. She talked about how for those entering data careers, how she recommends starting with basic data visualization and statistics, then focusing on SQL as it provides transferable and really in high-demand skills. She also talked about how she advocates for women in STEM by challenging stereotypes and addressing subtle workplace biases like receiving appearance based compliments rather than technical feedback. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Jess's social media profiles, as well as my own at superdatascience.com/839
00:56:54
And beyond social media, another way we can interact is coming up on December 4th when I'll be hosting a virtual half day conference on Agentic AI. It'll be interactive, practical, and it'll feature some of the most influential people in the AI agents space as speakers. You don't want to miss this one. It'll be live in the O'Reilly platform, which many employers and universities provide access to. Otherwise, you can grab a free 30-day trial of O'Reilly using our special code SDSPOD23. We've got a link to that code ready for you in the show notes. All thanks of course to everyone on the Super Data Science podcast team for producing another fun episode for us today for enabling that super team to create this free podcast for you we are deeply grateful to our sponsors. You can support this show by checking out our sponsors links, which are in the show notes, and if you yourself are interested in sponsoring an episode, you can get the details on how to do that by making your way to jonkrone.com/podcast.
00:57:53
Otherwise, share this episode with people who'd like to hear it. Review the episode wherever you listen to your episodes. Subscribe obviously if you're not a subscriber, but most importantly, I just hope you'll keep on tuning in. I'm so grateful to have you listening, and hope I can continue to make episodes you love for years and years to come. Until next time, keep on rocking out there, and I'm looking forward to enjoying another round of the Super Data Science podcast with you very soon.
This is episode number 839 with Jess Ramos, founder of Big Data Energy Analytics.
00:00:13
Welcome to the Super Data Science podcast, the most listened to podcast in the Data Science industry. Each week we bring you fun and inspiring people and ideas, exploring the cutting edge of Machine Learning, AI, and related technologies that are transforming our world for the better. I'm your host, Jon Krohn. Thanks for joining me today. And now let's make the complex simple.
00:00:47
Welcome back to the Super Data Science podcast. Today we've got the charismatic, well-spoken and super intelligent Jess Ramos on the show. Jess is founder of Big Data Energy, a company that supports her in-demand courses on SQL and Data Analytics. She's also a Senior Data Analyst at Crunchbase, and previously worked as a Senior Risk Analyst and a Data Analytics Manager. Her popular social media content on SQL, Data Analytics, Data Science, tech advancements, and maximizing professional growth has led her to amassing over 300,000 followers across LinkedIn, Instagram, and TikTok. She holds a Bachelor's in Math, and she also holds a Master's in Business Analytics from the University of Georgia.
00:01:30
Today's episode will appeal especially to folks who are looking to grow their career or grow into a career in Data Analytics or Data Science or a related field. In today's episode Jess details how she more than doubled her data analyst salary in less than a year. She also talks about the questionable value of Data Science bootcamps, her controversial take on "girl math" that made a splash in international mainstream news, the unexpected viral post that launched her into social media fame, and essential advice for anyone starting their data career journey. Are you ready for this fun episode? Let's go.
00:02:14
Jess, welcome to the Super Data Science podcast. It's awesome to have you on the show. Where are you calling in from today?
Jess Ramos: 00:02:20
I'm in Athens, Georgia, so it's like a small-ish town, about an hour from Atlanta.
Jon Krohn: 00:02:25
Probably a pretty exciting time to be a few days before the U.S. election. It's interesting, when our listeners hear this, they will know the outcome that you and I don't.
Jess Ramos: 00:02:33
Yeah, that's crazy. We'll have to see.
Jon Krohn: 00:02:38
Let's dive right into the episode content. You have a math and business analytics educational background, and you've worked in both startups and more established organizations. Currently, your nine-to-five is as a senior data analyst at Crunchbase. But outside of that you've amassed a large following on social media, creating content about Data Science, Data Analytics, tech news and career development, and you have a unique and imaginative voice that obviously has been a hit. Because you've got a crazy number of followers, it's really wild. I've got some of the numbers here, at the time of recording over 200,000 followers on LinkedIn, 90,000 on Instagram, over 20,000 on TikTok. That's pretty wild. So, you've carved out a niche for yourself. It's obviously going very well. It's led to brand partnerships, speaking collaborations, and last year you founded the amazingly named Big Data Energy-
Jess Ramos: 00:03:35
Yes, I did.
Jon Krohn: 00:03:38
... company, and that supports all of these efforts. What's the origin story of your career in data and this Big Data Energy brand?
Jess Ramos: 00:03:48
Yeah, so my origin story for my career in data, I was studying math in school. Math is something that just always came kind of easy or kind of natural for me. I love to think through logic and numbers, but I didn't know what I actually wanted to do with my math degree. So that led me to pursue my master's in business analytics. And luckily I was also able to get an internship in undergrad and then again in grad school. So, I was able to test the waters and data and see if I actually liked the career or not. From there, that internship led to a full-time job. I moved up a few times to a senior data analyst, Data Analytics manager, and then I've switched jobs twice since then. So, it really set me up for a great career in data.
00:04:34
Then my origin story for content, I really just posted on LinkedIn one night on my phone. It was a Sunday night at 10 p.m. I was in bed, just typed up this post, and it went viral. It was about remote work and I was like, "What? This is so unexpected." I wasn't trying to be a content creator. I just found an amazing community on accident. And I was like, "I'm a one-hit wonder." No one is actually going to follow me. I was scared to post again and my coworkers were like, "No, Jess you're going to do fine, you'll be great." And from there I'm just super blessed, even hearing the numbers with you introducing me, I'm like, "I can't believe that many people follow me." I always think about it in football stadiums. I'm like, "That's three or four stadiums of people." And every time I go to a big event that I look around, I'm like, "This many people times three follow me on social media." It's crazy, but I'm super just blessed to have the opportunity to reach so many people, and resonate with so many people as well.
Jon Krohn: 00:05:36
Yeah, it's very cool. Congrats. And so, what was the second post about? Do you remember?
Jess Ramos: 00:05:42
The second one was about SQL, so I think it was top 10 optimization tips, I don't know. It was something to do with SQL, which was very different from my first post, but that one got around 1,000 likes, and this was back in the LinkedIn golden ages. This was two and a half years ago. Views were really good on LinkedIn back then. But yeah, that posted well too, and from there I kind of developed my niche over time. I've even changed it a little bit and reshaped it, depending on what my career goals are, so it's been fun.
Jon Krohn: 00:06:15
Nice. Well, that is exciting that your second-biggest post ever was about SQL, because you have a SQL course coming out right now. It's coming out the day after this episode is out. Tell us about it?
Jess Ramos: 00:06:28
So, I have had this dream of making my own SQL course for a long time. I was really lucky to make two courses with LinkedIn Learning. One of them was in SQL and I just learned how to make a really good course, and I got so much positive feedback from this course. From there it was my dream to have my own course that has my name on it. I have full control over and I can do whatever I want with it. This is something I've been dreaming about for a long time, and my course is called Big SQL Energy. So, we're still sticking with the branding, Big Data Energy, Big SQL Energy, keeping it fun. This course is an intermediate course, so I start off with joins and then go up from there. I do expect that people have a little bit of basic SQL knowledge coming in, but I really noticed that there's not enough intermediate courses out there, and that's why I really wanted to fill in that gap in the market.
00:07:20
There's so many basic SQL courses out there where people just learn SELECT * FROM TABLE, they're doing just super basic things, but they finish these courses and they don't actually know how to use SQL in the real world and how to use it to solve business problems. My course is actually taking those SQL skills to the next level and people are going to work through these business problems and these scenarios. And I even have little stakeholder scenarios in there too, like, "So-and-so just knocked on your door, it's a last-minute request. What are we going to do?" I'm really just trying to take that learner into a real-world environment and to really think critically through more intermediate and advanced concepts.
Jon Krohn: 00:08:02
Sounds really valuable and sounds like the kind of course that I need to be taking with my relatively rudimentary SQL capabilities. Where can this course be found? You say your name's on it. So, this is going to be available through the Big Data Energy website or something?
Jess Ramos: 00:08:17
Ironically, I don't even have a website for my company. I just have a Linktree. But I'm hosting it through Teachable. Teachable has been an amazing partner for me, and you wouldn't even know that it's hosted on Teachable because they do such an amazing job supporting course makers and creators, so it'll look like it really is my website. So, I'm able to plug in all my stuff there and someone is able to access the course through their portal and their platform. And I can give you a link too for the show notes if anyone's interested.
Jon Krohn: 00:08:48
Fantastic, that would be great. But it sounds like it will be easy for people to find you on social media, to find you on LinkedIn or Instagram or wherever they prefer to get their social media content, and then they'll be able to find your course through there. Because that's presumably something you're going to be talking about a lot over the next few months.
Jess Ramos: 00:09:06
Totally. It'll be in my Linktree all over social media. I promise, you cannot miss my course launch, I will find you.
Jon Krohn: 00:09:12
Nice. Yeah. SQL obviously is important in a Data Science career. For our listeners who maybe aren't already a data analyst or data scientist, can you explain why SQL is so critical?
Jess Ramos: 00:09:25
Yeah, so SQL is an older coding language. It's been out a long time, but it's something that is just the backbone of data. It is the bread and butter of really any type of data role, and it's something that companies have depended on for a long time and will continue to depend on for a long time. So, it's a very established skillset that companies are already using, and it's really not going to go anywhere. So SQL, if you're not familiar and you're listening, SQL is a language to query a database. So it's basically the language you use to pool data, make different data sets, reshape it, do data cleaning, everything you need to do to get data out of the database and get it ready for analysis. So, you can do analyses inside of SQL itself, or you can even pull the data into a business intelligence tool and do further analysis there. It's just a really important skill that you're going to use at least some point in your career, no matter what type of data role you're working in.
Jon Krohn: 00:10:25
It's the most popular language in Data Science or Data Analytics. There's often this kind of, although it happens less and less, but there was a time where there was this kind of Python versus R debate. Now it's kind of mostly settled.
Jess Ramos: 00:10:38
It's kind of settled.
Jon Krohn: 00:10:40
But during that whole era it was amusing to me, it was like, what is the language of Data Science? It's not Python or R, it's SQL. And that's still true.
Jess Ramos: 00:10:48
Exactly. People ask all the time, they're like, "Should I be learning Python if I want to be a data analyst?" And yes and no. Python of course is a very versatile programming language. You can do way more in Python, but if you're going for an entry-level role or maybe a company that isn't as modern with their technology, you might not even use Python on the job. So SQL is like the bread and butter.
Jon Krohn: 00:11:15
For sure. All right, so that gives us your SQL take, which obviously-
Jess Ramos: 00:11:22
I love SQL, if you can't tell.
Jon Krohn: 00:11:23
And there are already, as you said, there's also courses on LinkedIn Learning that you've created related to SQL, so people can check those out as well in addition to the new course, Big SQL Energy, obviously I love that name. We have your insights now, Jess, into why SQL is so important, and we've got your course. Again, that name is Big SQL Energy coming out tomorrow, which people can check out. Great. More broadly, in planning a career in Data Analytics or Data Science, you made a splash recently in social media through doubling your salary in 11 months. Do you want to fill us in on how our listeners can do that themselves?
Jess Ramos: 00:12:01
Absolutely. And we got to get the facts straight. I over doubled it in an 11 months time period, but who's counting? Just kidding. Yeah, so I did have an 11-month time period right before I started my role at Crunchbase about two years ago where I did over double my salary. And to walk you through all those steps, I was currently working as a Senior Data Analyst at a smaller fintech startup. And then from there I negotiated an internal raise. So, I basically went to my manager in HR and I was like, "I have a direct report. I'm doing all of this work, I'm leading all of these projects, I'm supporting the data for our capital raise, and I'm making $72,000 a year. I should probably be making a little bit more money for all the responsibilities I'm having."
00:12:53
I really built up that case of all of my accomplishments and responsibilities. I analyzed the market data, and I showed that senior analysts were making about six figures. I went back to them with all of that research and advocated for myself. And I was able to increase my salary to 95,000, which at the time, I mean an over 30% raise from an internal negotiation, I was stoked. I'm so proud of past Jess for doing that. And then from there I knew I needed to go somewhere else to continue learning. I wasn't really growing in my role anymore. And this is like six months later or so. From there I was on the job market, I just held my cards really close to my chest. I did not say any numbers in interviews. You will not get a number out of me ever in an interview.
Jon Krohn: 00:13:42
And that's a key negotiation tactic in anything, whether you're negotiating your salary or any kind of deal where money's involved that it's like negotiations 101. So probably a lot of our listeners are already aware of this, but you should never give your number. Like what's your salary expectation, that kind of thing, or what kind of salary are you on? Sorry, I interrupted you Jess, but.
Jess Ramos: 00:14:04
No, you're good. And I've heard too, whoever speaks first loses. That's what I've always heard about negotiation. And it's really easy to be nervous in an interview and just blurt something out. But I think just recognizing, this is a very uncomfortable conversation. It's the worst part of any interview. And just sit in that uncomfortableness and just be okay with it being awkward and dodging the question. But that's what you really have to do to get those increases and make sure you advocate for yourself. Because when I first entered the job market, again with my 95,000 salary, I was getting interviews for roles ranging from 100K all the way up to 135, 140, and I'm like, "I'm getting interviews for these roles. I would have been excited to even just get 110, 120, which was a big increase from where I was, but I just kept my lips tight, didn't say any numbers.
00:15:01
I let them give me their budget and flip the question back on them every time. And then at the end I negotiated an offer up to 150, which I declined to go to Freddie Mac for less money. And then I quit in four months. It was not a great place for me to work, terrible fit, but I did end up getting an offer for 150, which was well above where I was a few months prior. And then from there, leaving Freddie Mac after four months, I came to Crunchbase making over 150. And I'm going to be vague about my current salary only because I don't want to ruin my future negotiations.
Jon Krohn: 00:15:36
Sure. And I actually really appreciate you being so candid about the numbers on air. I mean, it's great that you are-
Jess Ramos: 00:15:42
I'm super open.
Jon Krohn: 00:15:45
... keeping the current one close to your chest. But to be able to go into concrete numbers like that, it is the kind of openness that you rarely hear people do on air. So thanks very much, Jess. What do you think the impact of the early internships, you mentioned those when we talked about your career background right at the onset of the episode. What do you think the impact of the early internships was on shaping your skills and the opportunities that you had subsequently?
Jess Ramos: 00:16:11
Oh, the impact was ginormous. I think it absolutely shaped my skills-
Jon Krohn: 00:16:16
That's big.
Jess Ramos: 00:16:17
I think it shaped my skills, but I think almost more than that, it shaped my interest. Which is so important while you're in school and you're not really sure what you want to do. When I was studying math in undergrad I was like, "How am I going to apply this to the real world?" And I stumbled upon a pretty casual summer Data Analytics type of internship. And I was basically developing insights from enrollment data to show who's enrolling in the school, who do we think might enroll. And I was doing a little bit of predictive work. It was very novice level work. So, is it the work I'm most proud of at this point? No, but it was a great learning opportunity for me to learn R and a little bit of Machine Learning and predictive analytics.
00:17:04
So, that's what I use to talk about in my grad school interviews, because that internship taught me enough to where I knew I liked working with data and that's why I applied to grad school. And then I was able to talk about that internship in grad school, which was super important, because a lot of people in my grad program already had full-time experience. And I was a little baby undergrad. I didn't have any real full-time experience yet, but I was at least able to show that I'm a math major, I've had this internship and it got me into grad school. And then in grad school I got a local Data Analytics internship, which was just a huge growth opportunity for me. I was using SQL in a real database, PowerBI, leading meetings with the sales team, providing updates in front of the whole company. So, it just was the biggest learning platform I ever could have imagined.
Jon Krohn: 00:17:55
Amazing. That sounds really great. Another thing in addition to internships that people often find helpful in their career is mentorship or managerial support. In what ways has managerial support or mentorship influenced your career development? And I guess more generally, if we could maybe extrapolate from that, what you think the great qualities are in a data manager?
Jess Ramos: 00:18:18
Yeah, I mean, I've had so many amazing mentors just in the past and even now too. But my first manager at this first job is probably the best manager I've ever had, if I'm allowed to say that. But he was just so supportive of me, not only as a data analyst, but also as a person. He genuinely cared about me as a person. He gave me the time and space to work through problems. Even though I was brand new and those problems were very difficult at the time, he gave me that space to go figure it out on my own and then come back with questions. He never made me feel stupid if I made a mistake or didn't know how to do something. So, I think he was a really good balance of giving me the time and space to go stubbornly figure it out on my own. But then he was also there to help me out when I needed it.
00:19:07
And of course, he gave me so many growth opportunities. Like leading these sales calls I was leading, doing our company-wide updates. I mean, he really grew me into that senior analyst. And then when he moved to a different department a little bit, I was able to step up and be a manager and hire two people under me, which was great. It's an ideal scenario for a manager.
Jon Krohn: 00:19:28
Very cool. I mean, that sounds like it was all positives. Are you able to say this person's name and the company?
Jess Ramos: 00:19:33
Yeah, so the company was FormFree. His name is Caleb Wuethrich. I know I just said his last name wrong, and it's just a really hard last name to say. So, he's going to listen to this and be like, "Jess still doesn't know how to say my last name." And then he also recently got married too, so he might have ... I know he was thinking about changing his name, so he might not even exist as that name anymore on LinkedIn, but-
Jon Krohn: 00:19:57
Cool. So, thanks Caleb. It sounds like a great manager, and we at least have some information on that great positive influence as a manager in your career beyond the jobs that you've had, you're increasingly entrepreneurial. So when was the moment that you decided, "You know what? I need to start Big Data Energy. I need to start this company." What was the event that allowed you to go from being in the U.S. we say just like a W-2 employee where you're just, you're earning that salary to income and go out and do your own thing?
Jess Ramos: 00:20:37
Well, to be quite blunt, I started making money through just different brand deals, my LinkedIn learning courses, and I realized I was bringing an income outside of my regular job. So, I'm like, "Shoot, I need to go find an accountant. I know I'm going to have to pay taxes on all of this later. I need to get my ducks in a row." And I just didn't know anything about it. Going to my accountant, he was super helpful for me to know what I needed to do to save enough for taxes later on and get things squared away. From there I developed the LLC. Then Q4 of last year, 2023, was like a very growth year for me. I was making my first dollars in my business working with all these amazing companies and brands, but it wasn't until Q4 that it really exploded for me.
00:21:27
I was getting noticed by companies like SAP. They flew me out to New York to come to a super tight-knit event with their top leaders, top partners, because they wanted media coverage for it, and they knew I was the person to help with that. I got asked to go to IBM Think in May. IBM flew me out to Boston to report on their event and create Instagram reels for them. Seeing these big companies notice me is like, it still shocks me every time that they even want to work with me, but it's an amazing opportunity for me to share cool things with my audience and also be a partner to these really cool brands that I've looked up to for a long time. So really, 2024 was my year of like, "Oh, I'm actually making it on my own." My business is sustaining itself. I've hired an intern, a video editor, I have a adult big girl expenses here, and I'm like, "How am I doing all of this?" I have no idea.
Jon Krohn: 00:22:22
Yeah, congrats, Jess. That is great. It sounds like clearly developing your audience, developing your social media following was part of the key to those examples that you IBM flying you out, asking you to create Instagram Reels and give exposure to their event. Would you recommend to data professionals, data analysts, data scientists, maybe software developers, our listeners in general, who should be creating social media content? Should everyone be, what value does it create in their career?
Jess Ramos: 00:22:56
Yeah, I mean, at minimum I think everybody should be active, at least on LinkedIn. I know not everybody is comfortable being on camera and making Instagram Reels, it's a very vulnerable and humbling experience, especially when you first start. But I think at minimum, everybody should be on LinkedIn. There's so many professionals out there that want to connect with you, that you can learn from, that you can teach, get job opportunities from. So, being active on LinkedIn, making at least a little bit of content and engaging with other people and doing coffee chats, that is going to be just so good for your career longterm, and it's just a good way to network and get your name out there. Anytime I go to a city, I'm like, "Oh, I know so-and-so there maybe we can meet up." Anytime I've ever needed career advice or been looking for a job, I've had at least a list of people that I knew I could reach out to for guidance or a potential job opportunity.
00:23:52
So, building those connections on LinkedIn I think is the minimum for a good, strong network for your career. But I would encourage anyone who's interested to take it a step further and post on other social media platforms and be more of a content creator. Because there's so much opportunity to start a side gig from that. And there's actually real money with working on social media. It sounds silly just making these little videos, and it's even cringey sometimes, especially if I have to film in public, but it's an actual career in business that you can do on top of your nine to five at whatever involvement level you want to, and actually have real side income from it.
Jon Krohn: 00:24:35
Nice. Great summary there. Let's move now onto some of the work that has come out of all that social media work that you've done in the past. Does make a lot of sense to me. Obviously, you're preaching to the choir a bit with me about creating content. Obviously, I do think that it's also useful, but in terms of your career, obviously things like not just going to IBM Think and doing Instagram reels, but also things like having a serious LinkedIn learning course now, your own course, your own branded teachable course on Big SQL Energy. Not on, but I guess that's the name of it.
Jess Ramos: 00:25:15
Yeah.
Jon Krohn: 00:25:15
I've probably, people understood what I said, even if it wasn't the best grammatically ordered sentence. The point is that your social media presence has led to these serious opportunities, including more than 50,000 people taking your LinkedIn Learning course on SQL. So yeah, so it's clear that you have this ability, this talent at translating complex data problems into teachable content that resonates with professionals at a wide range of skill levels.
00:25:55
There's an interesting journey there that I want to dig into a bit. It's creating social media content, which obviously you've been able to create stuff that really resonates with people. And then when you had the opportunity to be creating courseware, you were similarly able to connect with people, as I just said, translate complex data problems into teachable content that resonates with people, that makes sense to people. Do you think that there's anything that you can tell us about that skillset so that we can become better at it ourselves? Because it sounds like this kind of ability, it's not just useful when you're making course ware or when you're making Instagram reels, it's useful in everyone's workplace.
Jess Ramos: 00:26:40
Yeah, totally. And I think for me, I don't take anything too seriously. I mean, I do, but I don't at the same time. I'm very serious about my work, especially when I get into work mode. But I don't take things too seriously all the time, I like to have fun. I never want to gate keep information and make things super smart and technical and confuse people so they feel badly about themselves and see how smart I am. I'm not that kind of person. When I'm explaining things on social media or in my courses, I'm speaking to people like they're real people. I make these videos like I'm just talking to a friend, and I even use silly metaphors. For example, in my course when I teach unions in SQL, I talk about making a sandwich. You're building all of these pieces and stacking them together. I like to use those fun metaphors to make things easy to understand for anybody. My biggest advice to grow that skillset would be to take down the technical jargon and just pretend like you're explaining to a friend.
Jon Krohn: 00:27:39
Nice. That is great actionable insight. I thought it might be a tricky question, but you nailed it. Obviously, being able to communicate complex problems to people in the kind of relatable way that you're describing is key to people's career success. When people are starting in a new career like Data Analytics, or it could be Data Science, we've obviously talked about SQL as being an important skill. Do you think that that's the first skill that people should be learning, or amongst the first skills, I guess in broadening from just SQL, what are the other things that people should be learning first? What should people be prioritizing, and yeah, is SQL the first thing or one of the first things amongst that set?
Jess Ramos: 00:28:22
If you're brand new to data, you truly don't have any foundation at all, I would say definitely start with a little bit of data visualization. Maybe play around and Power BI or Tableau or even Excel and just understand some very basics. Like columns and rows, different data types, how you can visualize data differently. Maybe like bar charts, line graphs, a little bit of basic statistics like max, min, average, things like that. If you're brand new, I'd definitely start there. But I actually tell people they should really start in SQL, as long as you have a little bit of a data foundation, because I think SQL just broadens your data experience so well. You start to learn how data sets merge together. You get to learn a lot about data cleaning and how to reshape and transform data.
00:29:12
I think once you learn those skills, you can easily pick up anything else. If you already know how to do it well in SQL, you can go into Power BI or Tableau or whatever BI tool you want to use and apply those same concepts and transfer that knowledge over. I do think SQL is probably one of the best investments, because that's a skill that all companies are going to use, regardless of where you work. And then it also gives you good knowledge that you can transfer over to whichever BI tool your companies work again, and then also Python or R later down the road too.
Jon Krohn: 00:29:45
Nice. A lot of people, when they're starting in their careers, they would be thinking about educational programs that they could follow. So you just give a great list there of the kinds of skills that people would want to be learning, but a lot of people, they want to learn amongst other people or alongside other people, having a teacher giving them guidance and mentorship as we talked about earlier in this episode, the importance of mentorship. So, there are lots of online courses and boot camps out there on Data Analytics, business analytics, Data Science. You yourself have gone along what I guess we could call the more traditional route of getting an undergraduate degree in math and then a graduate degree in business analytics. So what do you think about those two different kinds of paths of, I mean, actually I guess there's three. There's three to consider because there's, what you were just talking about in your last answer.
00:30:44
It actually is something that somebody could do completely unstructured. Where you're chatting with something like ChatGPT, even just kind of your own guided, self-guided path of education. One step more formal could be doing a bootcamp or a collection of online courses that you curate for yourself. And then, the most formal would be to get degrees, to go to a university and get a formal education, formal diploma showing that you have, say an analytics skillset or a Machine Learning or Data Science skillset. What do you think about those various routes, and what would you recommend to different types of listeners?
Jess Ramos: 00:31:25
Yeah, so I think that choice really does come down to the individual, as cheesy as that sounds. But I think everyone has different goals. Everyone has different financial means and different time restrictions. If someone has a full-time job, they might not have as much flexibility with certain options. But I would say that I'm glad I went to grad school. For me, it was a personal goal to go to grad school. I actually dreamed of that as a kid. I was like, "I'm going to get a PhD or a Master's degree when I grow up." So for me, it was very much like an educational goal that I had, and it was obviously a very structured way of learning Data Analytics. And by the time I graduated I had all the skills I needed to go and apply for jobs plus the added credibility of having an extra degree.
00:32:10
So I think that was a huge plus. But I know realistically financially, not everybody is going to be able to go to grad school, and a lot of people aren't going to be able to pause their life for a year or two and do a full-time grad program. So, by no means am I saying that everybody should go to grad school. I think when it comes down to learning on your own, I think the two routes are a structured way, like a bootcamp, or there's your self-learning path where you can pick your own courses and stuff. I think if you have the discipline and the motivation, absolutely curate your own learning path, learn all the right skills, take a few courses, maybe spend a few hundred bucks on a few different courses that are going to be really good and set you up.
00:32:58
And that is totally enough to get a job in data. But it of course takes a lot of discipline, a lot of time. You have to build projects on your own and really practice and get those skills up to here so you can pass your interviews. Because you're not going to have the same credibility as putting a master's on your resume, but you're going to save a lot of money and probably a lot of time too. I think the bootcamp path, it depends on the bootcamp. I do not like seeing some boot camps charging $10,000, $20,000. I mean, my grad program was $20,000, so I'm like, "If you're going to spend 10 or 20,000 or take out loans, you might as well just get a master's, because if you're going to make that kind of financial investment, you should at least get a diploma for your wall and put some letters on your resume." I'm not a huge fan of predatory boot camps, they're very expensive for what they offer, but I do also think that there are very good boot camps out there.
00:34:00
So, shout out to Zach Wilson, his data engineering bootcamp. He's obviously very credible. His prices are very affordable. That's something that I would buy into versus one of these big corporations that's preying on newbies.
Jon Krohn: 00:34:13
Do you ever feel isolated, surrounded by people who don't share your enthusiasm for data science and technology? Do you wish to connect with more like-minded individuals? Well, look no further, Super Data Science community is the perfect place to connect, interact, and exchange ideas with over 600 professionals in data science, machine learning, and AI. In addition to networking, you can get direct support for your career through the mentoring program, where experienced members help beginners navigate. Whether you're looking to learn, collaborate, or advance your career, our community is here to help you succeed. Join Kirill, Hadelin and myself, and hundreds of other members who connect daily. Start your free 14-day trial today at superdatascience.com and become a part of the community.
00:34:58
That is great guidance. Some more real talk from you in this episode. I appreciate your openness and telling it like you see it. Earlier in the episode going into specific numbers on your salary, now talking about these specific numbers and value that you get on boot camps. And I totally agree with you. There are definitely predatory prices out there where yeah, you could be getting a lot better value on either of the paths that you described. Getting that graduate degree for the 10 or $20,000, or curating your own path. And as you said, that requires some more motivation, although it just occurred to me off the top of my head that potentially a way that you could vary inexpensively if you can somehow find even just a handful of other people that are also interested in developing a career in Data Analytics, Data Science, Machine Learning. It could be people that you met online, you could literally post about it on LinkedIn and say, "Hey-
Jess Ramos: 00:35:57
Totally.
Jon Krohn: 00:35:58
... I'm thinking about going into a career in Data Analytics. I come from this background. Here's some of the resources I was thinking of maybe learning or let's together come up with a course plan and hold each other accountable." You could, like you said, for hundreds of dollars you could develop all the same kinds of skills in a 10 or $20,000 bootcamp or even a 10 or $20,000 Master's potentially. And that independence showing that independence, you're going to be developing a lot of skills there yourself that are either employable skills, showing that you're able to organize a group, or independently as an individual be able to curate the right resources to succeed. I mean, that is a highly employable skill, but simultaneously, those are the same kinds of skills that allow you to be a great entrepreneur and to be making money on your own.
Jess Ramos: 00:36:46
Totally.
Jon Krohn: 00:36:47
I don't know. So, a number of different ideas there for people to sink their teeth into.
Jess Ramos: 00:36:53
Yeah, I hope I didn't talk too badly about boot camps.
Jon Krohn: 00:36:57
I mean, there's also, there is probably also, while I would think carefully about it, there probably also are scenarios where you think, "Okay, you know what? I have this career break right now. I've got three months or six months because I'm on gardening leave from, I was working at a big bank and they've given me gardening leave for six months." Money's not a problem for this person, because they've just left an investment bank or whatever. They left a software developer job at an investment bank and they're like, "I want to be a data scientist. I want to get into Machine Learning." You don't want to take that year or two years to get a master's, especially if you'd be pursuing it part-time. So you think, "Okay, well this bootcamp, even though it's a bit more expensive, I can get immersed in this right away." And often with that kind of price they do put a lot of effort into partnerships with industry, which is something that-
Jess Ramos: 00:37:50
True.
Jon Krohn: 00:37:51
... that's kind of a big part of I think what you're buying with that price tag.
Jess Ramos: 00:37:54
Yeah, I agree. I think the right bootcamp can be really good for somebody, especially because some people do want that structure. They want to be told exactly what to learn, how to learn it and when to learn it, so I think that's great along with the industry connections. But I think once you get into the 10,000, $20,000 range, that's when I'm a little like, "Is it really that much value? I don't know." But that's just my take, I wouldn't spend that much unless it were for a master's.
Jon Krohn: 00:38:20
We've talked a lot about generally what people can be doing to further themselves in a data career. Something that we haven't talked about that you advocate for a lot, is stereotypes in the data industry or the tech industry more broadly. Particularly for women you've been advocating, so you've demonstrated that women can thrive while staying true to their confidence selves in a data career. And you had a viral TikTok video last year, which was featured by mainstream news organizations like the BBC, and that viral TikTok highlighted the problematic nature of something called the "girl math" trend, which actually isn't something I am aware of. So, you can explain that to not just our listeners, but literally to me. But you highlighted in this viral post how "girl math" perpetuates negative stereotypes. Do you want to tell us about that?
Jess Ramos: 00:39:20
Yeah. So man, y'all really pulled out the receipts for this podcast episode. Y'all are going, I love it.
Jon Krohn: 00:39:25
That's big.
Jess Ramos: 00:16:17
I think it shaped my skills, but I think almost more than that, it shaped my interest. Which is so important while you're in school and you're not really sure what you want to do. When I was studying math in undergrad I was like, "How am I going to apply this to the real world?" And I stumbled upon a pretty casual summer Data Analytics type of internship. And I was basically developing insights from enrollment data to show who's enrolling in the school, who do we think might enroll. And I was doing a little bit of predictive work. It was very novice level work. So, is it the work I'm most proud of at this point? No, but it was a great learning opportunity for me to learn R and a little bit of Machine Learning and predictive analytics.
00:17:04
So, that's what I use to talk about in my grad school interviews, because that internship taught me enough to where I knew I liked working with data and that's why I applied to grad school. And then I was able to talk about that internship in grad school, which was super important, because a lot of people in my grad program already had full-time experience. And I was a little baby undergrad. I didn't have any real full-time experience yet, but I was at least able to show that I'm a math major, I've had this internship and it got me into grad school. And then in grad school I got a local Data Analytics internship, which was just a huge growth opportunity for me. I was using SQL in a real database, PowerBI, leading meetings with the sales team, providing updates in front of the whole company. So, it just was the biggest learning platform I ever could have imagined.
Jon Krohn: 00:17:55
Amazing. That sounds really great. Another thing in addition to internships that people often find helpful in their career is mentorship or managerial support. In what ways has managerial support or mentorship influenced your career development? And I guess more generally, if we could maybe extrapolate from that, what you think the great qualities are in a data manager?
Jess Ramos: 00:18:18
Yeah, I mean, I've had so many amazing mentors just in the past and even now too. But my first manager at this first job is probably the best manager I've ever had, if I'm allowed to say that. But he was just so supportive of me, not only as a data analyst, but also as a person. He genuinely cared about me as a person. He gave me the time and space to work through problems. Even though I was brand new and those problems were very difficult at the time, he gave me that space to go figure it out on my own and then come back with questions. He never made me feel stupid if I made a mistake or didn't know how to do something. So, I think he was a really good balance of giving me the time and space to go stubbornly figure it out on my own. But then he was also there to help me out when I needed it.
00:19:07
And of course, he gave me so many growth opportunities. Like leading these sales calls I was leading, doing our company-wide updates. I mean, he really grew me into that senior analyst. And then when he moved to a different department a little bit, I was able to step up and be a manager and hire two people under me, which was great. It's an ideal scenario for a manager.
Jon Krohn: 00:19:28
Very cool. I mean, that sounds like it was all positives. Are you able to say this person's name and the company?
Jess Ramos: 00:19:33
Yeah, so the company was FormFree. His name is Caleb Wuethrich. I know I just said his last name wrong, and it's just a really hard last name to say. So, he's going to listen to this and be like, "Jess still doesn't know how to say my last name." And then he also recently got married too, so he might have ... I know he was thinking about changing his name, so he might not even exist as that name anymore on LinkedIn, but-
Jon Krohn: 00:19:57
Cool. So, thanks Caleb. It sounds like a great manager, and we at least have some information on that great positive influence as a manager in your career beyond the jobs that you've had, you're increasingly entrepreneurial. So when was the moment that you decided, "You know what? I need to start Big Data Energy. I need to start this company." What was the event that allowed you to go from being in the U.S. we say just like a W-2 employee where you're just, you're earning that salary to income and go out and do your own thing?
Jess Ramos: 00:20:37
Well, to be quite blunt, I started making money through just different brand deals, my LinkedIn learning courses, and I realized I was bringing an income outside of my regular job. So, I'm like, "Shoot, I need to go find an accountant. I know I'm going to have to pay taxes on all of this later. I need to get my ducks in a row." And I just didn't know anything about it. Going to my accountant, he was super helpful for me to know what I needed to do to save enough for taxes later on and get things squared away. From there I developed the LLC. Then Q4 of last year, 2023, was like a very growth year for me. I was making my first dollars in my business working with all these amazing companies and brands, but it wasn't until Q4 that it really exploded for me.
00:21:27
I was getting noticed by companies like SAP. They flew me out to New York to come to a super tight-knit event with their top leaders, top partners, because they wanted media coverage for it, and they knew I was the person to help with that. I got asked to go to IBM Think in May. IBM flew me out to Boston to report on their event and create Instagram reels for them. Seeing these big companies notice me is like, it still shocks me every time that they even want to work with me, but it's an amazing opportunity for me to share cool things with my audience and also be a partner to these really cool brands that I've looked up to for a long time. So really, 2024 was my year of like, "Oh, I'm actually making it on my own." My business is sustaining itself. I've hired an intern, a video editor, I have a adult big girl expenses here, and I'm like, "How am I doing all of this?" I have no idea.
Jon Krohn: 00:22:22
Yeah, congrats, Jess. That is great. It sounds like clearly developing your audience, developing your social media following was part of the key to those examples that you IBM flying you out, asking you to create Instagram Reels and give exposure to their event. Would you recommend to data professionals, data analysts, data scientists, maybe software developers, our listeners in general, who should be creating social media content? Should everyone be, what value does it create in their career?
Jess Ramos: 00:22:56
Yeah, I mean, at minimum I think everybody should be active, at least on LinkedIn. I know not everybody is comfortable being on camera and making Instagram Reels, it's a very vulnerable and humbling experience, especially when you first start. But I think at minimum, everybody should be on LinkedIn. There's so many professionals out there that want to connect with you, that you can learn from, that you can teach, get job opportunities from. So, being active on LinkedIn, making at least a little bit of content and engaging with other people and doing coffee chats, that is going to be just so good for your career longterm, and it's just a good way to network and get your name out there. Anytime I go to a city, I'm like, "Oh, I know so-and-so there maybe we can meet up." Anytime I've ever needed career advice or been looking for a job, I've had at least a list of people that I knew I could reach out to for guidance or a potential job opportunity.
00:23:52
So, building those connections on LinkedIn I think is the minimum for a good, strong network for your career. But I would encourage anyone who's interested to take it a step further and post on other social media platforms and be more of a content creator. Because there's so much opportunity to start a side gig from that. And there's actually real money with working on social media. It sounds silly just making these little videos, and it's even cringey sometimes, especially if I have to film in public, but it's an actual career in business that you can do on top of your nine to five at whatever involvement level you want to, and actually have real side income from it.
Jon Krohn: 00:24:35
Nice. Great summary there. Let's move now onto some of the work that has come out of all that social media work that you've done in the past. Does make a lot of sense to me. Obviously, you're preaching to the choir a bit with me about creating content. Obviously, I do think that it's also useful, but in terms of your career, obviously things like not just going to IBM Think and doing Instagram reels, but also things like having a serious LinkedIn learning course now, your own course, your own branded teachable course on Big SQL Energy. Not on, but I guess that's the name of it.
Jess Ramos: 00:25:15
Yeah.
Jon Krohn: 00:25:15
I've probably, people understood what I said, even if it wasn't the best grammatically ordered sentence. The point is that your social media presence has led to these serious opportunities, including more than 50,000 people taking your LinkedIn Learning course on SQL. So yeah, so it's clear that you have this ability, this talent at translating complex data problems into teachable content that resonates with professionals at a wide range of skill levels.
00:25:55
There's an interesting journey there that I want to dig into a bit. It's creating social media content, which obviously you've been able to create stuff that really resonates with people. And then when you had the opportunity to be creating courseware, you were similarly able to connect with people, as I just said, translate complex data problems into teachable content that resonates with people, that makes sense to people. Do you think that there's anything that you can tell us about that skillset so that we can become better at it ourselves? Because it sounds like this kind of ability, it's not just useful when you're making course ware or when you're making Instagram reels, it's useful in everyone's workplace.
Jess Ramos: 00:26:40
Yeah, totally. And I think for me, I don't take anything too seriously. I mean, I do, but I don't at the same time. I'm very serious about my work, especially when I get into work mode. But I don't take things too seriously all the time, I like to have fun. I never want to gate keep information and make things super smart and technical and confuse people so they feel badly about themselves and see how smart I am. I'm not that kind of person. When I'm explaining things on social media or in my courses, I'm speaking to people like they're real people. I make these videos like I'm just talking to a friend, and I even use silly metaphors. For example, in my course when I teach unions in SQL, I talk about making a sandwich. You're building all of these pieces and stacking them together. I like to use those fun metaphors to make things easy to understand for anybody. My biggest advice to grow that skillset would be to take down the technical jargon and just pretend like you're explaining to a friend.
Jon Krohn: 00:27:39
Nice. That is great actionable insight. I thought it might be a tricky question, but you nailed it. Obviously, being able to communicate complex problems to people in the kind of relatable way that you're describing is key to people's career success. When people are starting in a new career like Data Analytics, or it could be Data Science, we've obviously talked about SQL as being an important skill. Do you think that that's the first skill that people should be learning, or amongst the first skills, I guess in broadening from just SQL, what are the other things that people should be learning first? What should people be prioritizing, and yeah, is SQL the first thing or one of the first things amongst that set?
Jess Ramos: 00:28:22
If you're brand new to data, you truly don't have any foundation at all, I would say definitely start with a little bit of data visualization. Maybe play around and Power BI or Tableau or even Excel and just understand some very basics. Like columns and rows, different data types, how you can visualize data differently. Maybe like bar charts, line graphs, a little bit of basic statistics like max, min, average, things like that. If you're brand new, I'd definitely start there. But I actually tell people they should really start in SQL, as long as you have a little bit of a data foundation, because I think SQL just broadens your data experience so well. You start to learn how data sets merge together. You get to learn a lot about data cleaning and how to reshape and transform data.
00:29:12
I think once you learn those skills, you can easily pick up anything else. If you already know how to do it well in SQL, you can go into Power BI or Tableau or whatever BI tool you want to use and apply those same concepts and transfer that knowledge over. I do think SQL is probably one of the best investments, because that's a skill that all companies are going to use, regardless of where you work. And then it also gives you good knowledge that you can transfer over to whichever BI tool your companies work again, and then also Python or R later down the road too.
Jon Krohn: 00:29:45
Nice. A lot of people, when they're starting in their careers, they would be thinking about educational programs that they could follow. So you just give a great list there of the kinds of skills that people would want to be learning, but a lot of people, they want to learn amongst other people or alongside other people, having a teacher giving them guidance and mentorship as we talked about earlier in this episode, the importance of mentorship. So, there are lots of online courses and boot camps out there on Data Analytics, business analytics, Data Science. You yourself have gone along what I guess we could call the more traditional route of getting an undergraduate degree in math and then a graduate degree in business analytics. So what do you think about those two different kinds of paths of, I mean, actually I guess there's three. There's three to consider because there's, what you were just talking about in your last answer.
00:30:44
It actually is something that somebody could do completely unstructured. Where you're chatting with something like ChatGPT, even just kind of your own guided, self-guided path of education. One step more formal could be doing a bootcamp or a collection of online courses that you curate for yourself. And then, the most formal would be to get degrees, to go to a university and get a formal education, formal diploma showing that you have, say an analytics skillset or a Machine Learning or Data Science skillset. What do you think about those various routes, and what would you recommend to different types of listeners?
Jess Ramos: 00:31:25
Yeah, so I think that choice really does come down to the individual, as cheesy as that sounds. But I think everyone has different goals. Everyone has different financial means and different time restrictions. If someone has a full-time job, they might not have as much flexibility with certain options. But I would say that I'm glad I went to grad school. For me, it was a personal goal to go to grad school. I actually dreamed of that as a kid. I was like, "I'm going to get a PhD or a Master's degree when I grow up." So for me, it was very much like an educational goal that I had, and it was obviously a very structured way of learning Data Analytics. And by the time I graduated I had all the skills I needed to go and apply for jobs plus the added credibility of having an extra degree.
00:32:10
So I think that was a huge plus. But I know realistically financially, not everybody is going to be able to go to grad school, and a lot of people aren't going to be able to pause their life for a year or two and do a full-time grad program. So, by no means am I saying that everybody should go to grad school. I think when it comes down to learning on your own, I think the two routes are a structured way, like a bootcamp, or there's your self-learning path where you can pick your own courses and stuff. I think if you have the discipline and the motivation, absolutely curate your own learning path, learn all the right skills, take a few courses, maybe spend a few hundred bucks on a few different courses that are going to be really good and set you up.
00:32:58
And that is totally enough to get a job in data. But it of course takes a lot of discipline, a lot of time. You have to build projects on your own and really practice and get those skills up to here so you can pass your interviews. Because you're not going to have the same credibility as putting a master's on your resume, but you're going to save a lot of money and probably a lot of time too. I think the bootcamp path, it depends on the bootcamp. I do not like seeing some boot camps charging $10,000, $20,000. I mean, my grad program was $20,000, so I'm like, "If you're going to spend 10 or 20,000 or take out loans, you might as well just get a master's, because if you're going to make that kind of financial investment, you should at least get a diploma for your wall and put some letters on your resume." I'm not a huge fan of predatory boot camps, they're very expensive for what they offer, but I do also think that there are very good boot camps out there.
00:34:00
So, shout out to Zach Wilson, his data engineering bootcamp. He's obviously very credible. His prices are very affordable. That's something that I would buy into versus one of these big corporations that's preying on newbies.
Jon Krohn: 00:34:13
Do you ever feel isolated, surrounded by people who don't share your enthusiasm for data science and technology? Do you wish to connect with more like-minded individuals? Well, look no further, Super Data Science community is the perfect place to connect, interact, and exchange ideas with over 600 professionals in data science, machine learning, and AI. In addition to networking, you can get direct support for your career through the mentoring program, where experienced members help beginners navigate. Whether you're looking to learn, collaborate, or advance your career, our community is here to help you succeed. Join Kirill, Hadelin and myself, and hundreds of other members who connect daily. Start your free 14-day trial today at superdatascience.com and become a part of the community.
00:34:58
That is great guidance. Some more real talk from you in this episode. I appreciate your openness and telling it like you see it. Earlier in the episode going into specific numbers on your salary, now talking about these specific numbers and value that you get on boot camps. And I totally agree with you. There are definitely predatory prices out there where yeah, you could be getting a lot better value on either of the paths that you described. Getting that graduate degree for the 10 or $20,000, or curating your own path. And as you said, that requires some more motivation, although it just occurred to me off the top of my head that potentially a way that you could vary inexpensively if you can somehow find even just a handful of other people that are also interested in developing a career in Data Analytics, Data Science, Machine Learning. It could be people that you met online, you could literally post about it on LinkedIn and say, "Hey-
Jess Ramos: 00:35:57
Totally.
Jon Krohn: 00:35:58
... I'm thinking about going into a career in Data Analytics. I come from this background. Here's some of the resources I was thinking of maybe learning or let's together come up with a course plan and hold each other accountable." You could, like you said, for hundreds of dollars you could develop all the same kinds of skills in a 10 or $20,000 bootcamp or even a 10 or $20,000 Master's potentially. And that independence showing that independence, you're going to be developing a lot of skills there yourself that are either employable skills, showing that you're able to organize a group, or independently as an individual be able to curate the right resources to succeed. I mean, that is a highly employable skill, but simultaneously, those are the same kinds of skills that allow you to be a great entrepreneur and to be making money on your own.
Jess Ramos: 00:36:46
Totally.
Jon Krohn: 00:36:47
I don't know. So, a number of different ideas there for people to sink their teeth into.
Jess Ramos: 00:36:53
Yeah, I hope I didn't talk too badly about boot camps.
Jon Krohn: 00:36:57
I mean, there's also, there is probably also, while I would think carefully about it, there probably also are scenarios where you think, "Okay, you know what? I have this career break right now. I've got three months or six months because I'm on gardening leave from, I was working at a big bank and they've given me gardening leave for six months." Money's not a problem for this person, because they've just left an investment bank or whatever. They left a software developer job at an investment bank and they're like, "I want to be a data scientist. I want to get into Machine Learning." You don't want to take that year or two years to get a master's, especially if you'd be pursuing it part-time. So you think, "Okay, well this bootcamp, even though it's a bit more expensive, I can get immersed in this right away." And often with that kind of price they do put a lot of effort into partnerships with industry, which is something that-
Jess Ramos: 00:37:50
True.
Jon Krohn: 00:37:51
... that's kind of a big part of I think what you're buying with that price tag.
Jess Ramos: 00:37:54
Yeah, I agree. I think the right bootcamp can be really good for somebody, especially because some people do want that structure. They want to be told exactly what to learn, how to learn it and when to learn it, so I think that's great along with the industry connections. But I think once you get into the 10,000, $20,000 range, that's when I'm a little like, "Is it really that much value? I don't know." But that's just my take, I wouldn't spend that much unless it were for a master's.
Jon Krohn: 00:38:20
We've talked a lot about generally what people can be doing to further themselves in a data career. Something that we haven't talked about that you advocate for a lot, is stereotypes in the data industry or the tech industry more broadly. Particularly for women you've been advocating, so you've demonstrated that women can thrive while staying true to their confidence selves in a data career. And you had a viral TikTok video last year, which was featured by mainstream news organizations like the BBC, and that viral TikTok highlighted the problematic nature of something called the "girl math" trend, which actually isn't something I am aware of. So, you can explain that to not just our listeners, but literally to me. But you highlighted in this viral post how "girl math" perpetuates negative stereotypes. Do you want to tell us about that?
Jess Ramos: 00:39:20
Yeah. So man, y'all really pulled out the receipts for this podcast episode. Y'all are going, I love it.
Jon Krohn: 00:39:25
Serg Masis is our researcher and he's unreal. I am so grateful to him, and I hope he listens to the episodes. Also, he gives me say, every second or third episode I'm like, "Yeah. Guests, you're like, wow, I can't believe you pulled on this."
Jess Ramos: 00:39:41
I forgot I did that. Yeah, no. So first I'll explain "girl math" to you and for anyone who doesn't know what it is. So it was a very silly TikTok trend, and it kind of spoke to the way that women kind of think through shopping. So an example is like, I just bought this shirt from the store, it was $30, now I'm going to go return it. So now I'm $30 richer. It's girl math, but obviously you're not $30 richer. You're just getting money back that you already spent. Or maybe you have a 20% off coupon for something so you can go spend that money that you've saved on something else. It's basically empowering you to do something else with your money, but it doesn't exactly add up. So it's kind of poking fun at the way that sometimes girls think through shopping and money in that way.
00:40:30
So there's all these videos out there about girl math and yes, I do see the humor in it, and I do think it's funny. I laugh at them because I think the exact same way. I think about money I spend that way is a joke. But I think the way that some of the girls were making videos on it, I think it just kind of made girls look silly, financially irresponsible. And there's definitely two sides to this argument. When I first posted my video about how it makes girls look financially irresponsible, it makes us look like we're not able to handle money and perpetuates that stereotype. A lot of women came in my comments and were like, "No, this is building community. This is girlhood." So I totally see that side too, because us girls, we have to stand together and have that community.
00:41:19
But I also got a math degree. So sitting here as someone who went through this math program, I didn't get fully respected by the other students or my professors. I am a very outspoken and girly girl, and I got to the point to where I would plan my outfits around my 400 level math classes. If I had a really smart math class, you bet I wasn't going to come in wearing my heeled boots and my makeup. And no, I would change the way that I looked because I wanted to be respected more. And thinking back to that, you're probably like Jess did that. There's no way Jess is unapologetically herself, but I wasn't, back then. I had to fit the mold of what it was like to be smart and be a math major. And then of course, there's all this subjective grading in the math department, and I'm not going to say that things happened because I was a woman or because the way that I looked or the way that I acted.
00:42:18
But I think women have to fight twice as hard to get that respect. Women have to prove themselves, whereas a lot of times men already have that respect and confidence coming into it. And then on top of that, women are more likely to undersell themselves and not advocate for themselves, which just snowballs. And seeing that in my career and also as a math major in undergrad, seeing girls, I have chills talking about this right now, but seeing girls talk about, "Girl math, we're so silly. We can't manage money. We're spending thousands of dollars on clothes," which no hate, I also spend a ton of money on shopping and clothes and pink stuff and designer purses, so no hate at all. But seeing it poked fun at and then seeing all the men in the comments being like, this is why I control my wife's spending.
00:43:08
This is why I control the money in my household. I was just seeing a lot of that kind of backlash from that trend, which kind of broke my heart in a way. I made that video, I just hopped on TikTok, posted it, boom, went viral on TikTok and Instagram, because obviously it was a controversial take on the subject. And I actually had girls turn against me for that, and I was like, "What are y'all doing to help women in STEM and help these stereotypes? I have a math degree and I'm working in Data Analytics. I feel like that's doing more to help the stereotypes than making little TikTok videos poking fun at them. That was a really long rant, but.
Jon Krohn: 00:43:47
That was an outstanding rant. I don't feel like I have ... This is something that, I don't know, it's kind of like I don't feel super comfortable weighing in on I do about bootcamps. It's like, yeah, but I loved everything that you said. I was like, you talked about having chills. Me too. I mean, it was the way that you delivered that, the argument. Was super compelling to me, and it seems really obvious, and it seems like there is a huge amount of opportunity still. I think in many ways in society, we have come a long way on bias against lots of socio demographic groups, including women. But there's still so much further to go than we've come is I guess now I am kind of giving an opinion anyway, but that's what I see. And so when you tell stories like that dressing differently for 400 level math class than other classes that you're taking, I have never thought of anything like that.
00:45:01
And yeah, I guess it's like I enjoy the privilege of not having to think of that, and I have my whole life. So yeah. So thank you for everything that you just said on this topic. And yeah, I really appreciate the work that you're doing and the message that you're telling, and it really resonates with me. So thank you. And I hope that it nudges my thoughts and my behaviors to think when I'm speaking to a woman in the workplace around a tech topic or math topic, to try to be more mindful about the perspective that you just relayed. And maybe that'll have some impact on the way that I convey things or what I convey that it's more supportive and more thoughtful or appreciative.
Jess Ramos: 00:39:41
I forgot I did that. Yeah, no. So first I'll explain "girl math" to you and for anyone who doesn't know what it is. So it was a very silly TikTok trend, and it kind of spoke to the way that women kind of think through shopping. So an example is like, I just bought this shirt from the store, it was $30, now I'm going to go return it. So now I'm $30 richer. It's girl math, but obviously you're not $30 richer. You're just getting money back that you already spent. Or maybe you have a 20% off coupon for something so you can go spend that money that you've saved on something else. It's basically empowering you to do something else with your money, but it doesn't exactly add up. So it's kind of poking fun at the way that sometimes girls think through shopping and money in that way.
00:40:30
So there's all these videos out there about girl math and yes, I do see the humor in it, and I do think it's funny. I laugh at them because I think the exact same way. I think about money I spend that way is a joke. But I think the way that some of the girls were making videos on it, I think it just kind of made girls look silly, financially irresponsible. And there's definitely two sides to this argument. When I first posted my video about how it makes girls look financially irresponsible, it makes us look like we're not able to handle money and perpetuates that stereotype. A lot of women came in my comments and were like, "No, this is building community. This is girlhood." So I totally see that side too, because us girls, we have to stand together and have that community.
00:41:19
But I also got a math degree. So sitting here as someone who went through this math program, I didn't get fully respected by the other students or my professors. I am a very outspoken and girly girl, and I got to the point to where I would plan my outfits around my 400 level math classes. If I had a really smart math class, you bet I wasn't going to come in wearing my heeled boots and my makeup. And no, I would change the way that I looked because I wanted to be respected more. And thinking back to that, you're probably like Jess did that. There's no way Jess is unapologetically herself, but I wasn't, back then. I had to fit the mold of what it was like to be smart and be a math major. And then of course, there's all this subjective grading in the math department, and I'm not going to say that things happened because I was a woman or because the way that I looked or the way that I acted.
00:42:18
But I think women have to fight twice as hard to get that respect. Women have to prove themselves, whereas a lot of times men already have that respect and confidence coming into it. And then on top of that, women are more likely to undersell themselves and not advocate for themselves, which just snowballs. And seeing that in my career and also as a math major in undergrad, seeing girls, I have chills talking about this right now, but seeing girls talk about, "Girl math, we're so silly. We can't manage money. We're spending thousands of dollars on clothes," which no hate, I also spend a ton of money on shopping and clothes and pink stuff and designer purses, so no hate at all. But seeing it poked fun at and then seeing all the men in the comments being like, this is why I control my wife's spending.
00:43:08
This is why I control the money in my household. I was just seeing a lot of that kind of backlash from that trend, which kind of broke my heart in a way. I made that video, I just hopped on TikTok, posted it, boom, went viral on TikTok and Instagram, because obviously it was a controversial take on the subject. And I actually had girls turn against me for that, and I was like, "What are y'all doing to help women in STEM and help these stereotypes? I have a math degree and I'm working in Data Analytics. I feel like that's doing more to help the stereotypes than making little TikTok videos poking fun at them. That was a really long rant, but.
Jon Krohn: 00:43:47
That was an outstanding rant. I don't feel like I have ... This is something that, I don't know, it's kind of like I don't feel super comfortable weighing in on I do about bootcamps. It's like, yeah, but I loved everything that you said. I was like, you talked about having chills. Me too. I mean, it was the way that you delivered that, the argument. Was super compelling to me, and it seems really obvious, and it seems like there is a huge amount of opportunity still. I think in many ways in society, we have come a long way on bias against lots of socio demographic groups, including women. But there's still so much further to go than we've come is I guess now I am kind of giving an opinion anyway, but that's what I see. And so when you tell stories like that dressing differently for 400 level math class than other classes that you're taking, I have never thought of anything like that.
00:45:01
And yeah, I guess it's like I enjoy the privilege of not having to think of that, and I have my whole life. So yeah. So thank you for everything that you just said on this topic. And yeah, I really appreciate the work that you're doing and the message that you're telling, and it really resonates with me. So thank you. And I hope that it nudges my thoughts and my behaviors to think when I'm speaking to a woman in the workplace around a tech topic or math topic, to try to be more mindful about the perspective that you just relayed. And maybe that'll have some impact on the way that I convey things or what I convey that it's more supportive and more thoughtful or appreciative.
Jess Ramos: 00:45:53
Yeah, I think what you said, there's so much progress that has been made, but I think there is a way to go to, and I think it's the subtle thing. For example, maybe a guy finishes a presentation and people are like, "That was an excellent presentation. I really loved what you said about X, Y. Z." A girl does a presentation and it's like, "Oh, I loved your outfit. You look so cute. You were so well-spoken." I think sometimes the way that men and women get different types of compliments, or maybe the woman is asked to plan the office pizza party, or is asked to be the note taker, the presentation maker, do the admin kind of homemaking activities. There's these very subtle microaggressions that happen sometimes in the workplace. So yes, I think we've come a long way. But I always like to respectfully call that stuff out and speak on it because yeah, it does kind of hurt women's feelings sometimes when we're not seen as capable, even if the intention isn't to be rude or hurtful in any way.
Jon Krohn: 00:46:57
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00:47:41
All right, Jess, well, thank you for obviously this valuable conversation that we just had around gender and STEM and in data roles. And thanks for this whole episode. I've learned a ton from you and I've really enjoyed being on air recording with you. You have great data energy, it's so big.
Jess Ramos: 00:48:05
I love that.
Jon Krohn: 00:48:10
But yeah, before I let my guests go, I always ask for a book recommendation. Do you have anything for us?
Jess Ramos: 00:48:15
Absolutely, I sure do. One of my favorite books ever written is Weapons of Math Destruction.
Jon Krohn: 00:48:23
Yes.
Jess Ramos: 00:48:23
It's a very popular book in the data world, but that is a book that I read when I first started learning about data, and it's a book that I wish I could reread again for the first time, because it's just so good. It's written by Cathy O'Neill.
Jon Krohn: 00:48:36
Cathy O'Neill.
Jess Ramos: 00:48:37
And she is a female mathematician, which of course, I look up to her so much and think she's amazing. It talks about algorithms and how data can actually be used in an unethical way, even if that's not the intention. So it's really pulling into the soft skills and ethics side of data, which I love. And the book is not a technical book at all. There are no equations, no numbers, so it's a pretty easy and light read. It's not like you're reading a bunch of code or anything. And it's truly something that anybody can enjoy regardless of their background and their technical abilities. So especially if you're new in data, it'll completely open your mind to how data can be manipulated to tell a story or even used in a bad way towards society. So it's definitely a super interesting book and it'll totally change your perspective of data and a lot of societal things that we do.
Jon Krohn: 00:49:30
Awesome. Great recommendation. And I also just took a note down while you were speaking that I've got to ask Cathy O'Neill to be on the show. I haven't had her on.
Jess Ramos: 00:49:37
Do you know her?
Jon Krohn: 00:49:39
I don't know her. Do you?
Jess Ramos: 00:49:41
Okay. No, I was like, wait, are you friends with her?
Jon Krohn: 00:49:44
No, I mean, but yeah-
Jess Ramos: 00:49:4
She's great.
Jon Krohn: 00:49:47
... one of the other great privileges I enjoy in my life is that by hosting what we're pretty sure is the world's most listened to Data Science podcast, you can just kind of cold reach out to people. And not everyone says yes. Not everyone responds, but Cathy O'Neill will be an amazing cast. Would love to have her on.
Jess Ramos: 00:50:03
Oh, I for sure listen to that one.
Jon Krohn: 00:50:05
Nice. All right, great recommendation. You got the gears flowing. Gears flowing, gears moving in my head there. If my gears are flowing, I'm in trouble. I've melted. And very last question before we're kind of done here is. If you could kind of reel off for us the most important places that we can be following you. We know that we've got your course, Big SQL Energy coming out tomorrow, so probably when most people listen to this, it is already out. LinkedIn, we know is probably your primary social medium. You've also got your Instagram account, you've got your TikTok, both of which are super popular. Anywhere else that we should be following you?
Jess Ramos: 00:50:48
Yeah, so I also have my newsletter. I have about 15,000 subscribers. It's called the Big Data Energy Newsletter, of course. You can also find those links on my other social medias. But I send it out, I mean, it's supposed to be weekly, but I honestly get busy. So it's maybe three times a month on average. But I send out data tips. I talk about salary negotiations, SQL skills. I talk about all sorts of things data and tech career. And I promise I don't send any spam or annoying things. It's all just extended longer form content, like the stuff I write about on LinkedIn. So definitely follow me there. I also post any big announcements through there, like my course launch, and I'll probably be pushing some discounts and promos for my course too in there. So definitely stay there if you want to go there if you want to stay up to date with that kind of stuff too.
00:51:41
And then I'm also on YouTube. I only have a couple of long form videos up as of right now, but that's an area I want to grow into so much in 2025. So if you want to be one of my first subscribers there, subscribe to me like my videos and give me some comments to help me get started.
Jon Krohn: 00:51:57
Nice. I look forward to seeing how that YouTube develops. I'm sure you're going to overtake my YouTube numbers in months. You're really talented at this stuff. I've been taking notes through this episode and I'm going to be taking more notes afterward as well. It's really amazing what you're doing, Jess. Great work.
Jess Ramos: 00:52:16
I mean, we'll see if I can get all my camera equipment set up for YouTube. That's been the biggest barrier, but we'll see.
Jon Krohn: 00:52:22
Yeah, and so actually Jess had some tips for me. Why don't we tell them quickly, kind of these tips you were telling me about this camera before we started recording. Which I'm going to buy to up my ... So for me, the long format stuff, obviously these are long podcast episodes, long YouTube videos, that's been my bread and butter for years. I have been terrible at, I don't make or even view shorts or reels on Instagram or YouTube. Well, actually we do pay people to make YouTube shorts at least. And I've tried to have a TikTok channel, but nobody follows me on TikTok. It's really embarrassing. But this kind of thing that you recommended, so specifically it was this, I'm going to put it in the show notes from a company DJI, which is famous for big, when you have big movie cameras, DJI makes this thing called the Gimbal.
00:53:13
So you can be moving around with it and the camera stays steady. And so you told me about this Osmo Pocket 3. O-S-M-O Pocket 3. It's a little pocket camera, it's just a couple of hundred bucks and you can run around and get great quality video anywhere you are.
Jess Ramos: 00:53:29
Yeah, it's great. I have had this camera for maybe almost a year, but I love it because the quality is insane. It's a super small camera. It's like, for people not watching the video right now, the camera's maybe the size of an egg with a stick on it. It fits in a purse easily, so it's really great for traveling. But the quality is amazing, even though it's so small. It films in 4K horizontally and then I think 3K vertically. So all my videos, they just look so professional. And then it also has that gimbal, which is great for me, because I'm always literally on the run and I'm a very chaotic person, so it helps stabilize all my videos. It's just an amazing camera. And then of course I have the mics. They're the DJI mics with the charging case. That pair really well, nicely with it.
00:54:19
It's great for on-site interviews at conferences, or I even use them for filming short form at my desk. So yeah, it's just a really great camera. I had my first one stolen accidentally, and I went and bought it again. That's how much I loved it.
Jon Krohn: 00:54:32
It was stolen accidentally.
Jess Ramos: 00:54:33
Yeah, well, it was actually purposefully stolen by someone. I lost it on a train from Budapest to Vienna. Yeah, it was a sad day. Yeah, the Vienna police did not care my camera was stolen, which is fair. They have bigger problems, but.
Jon Krohn: 00:54:49
The saddest thing is the content that was on it that you lost.
Jess Ramos: 00:54:52
Luckily, like DJI does have an app, so I had most of my videos backed up to the cloud.
Jon Krohn: 00:54:58
Wow, that's wild.
Jess Ramos: 00:54:59
So most is backed up to the cloud and downloaded on my phone, which has a terabyte of storage. So most was backed up, but I did lose some videos from that trip, which was sad. I was like, "Wait, you can keep the camera, but send me back my memory card."
Jon Krohn: 00:55:13
Right? Yeah. All right, Jess. Well, thank you for these hardware tips now as well. I have literally made a note in my shopping cart to be purchasing these things. And maybe I will have some great social media presence someday in terms of my video content. Thanks Jess so much for agreeing to be on this episode, I've enjoyed it so much. And yeah, hopefully we can catch up with you again in a couple of years and see how things are coming along.
Jess Ramos: 00:55:39
Yeah, that'd be awesome. Thanks so much for having me.
Jon Krohn: 00:55:47
Awesome episode. In it Jess filled us in on how she increased her salary from $72,000 a year to over $150,000 a year. So more than doubled it in less than a year through building a strong case based on responsibilities and market data, and then during a company change by not revealing her current compensation. She also talked about how her content creation journey began with a viral LinkedIn post about remote work leading to partnerships with major companies like IBM and SAP. She talked about how for those entering data careers, how she recommends starting with basic data visualization and statistics, then focusing on SQL as it provides transferable and really in high-demand skills. She also talked about how she advocates for women in STEM by challenging stereotypes and addressing subtle workplace biases like receiving appearance based compliments rather than technical feedback. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Jess's social media profiles, as well as my own at superdatascience.com/839
00:56:54
And beyond social media, another way we can interact is coming up on December 4th when I'll be hosting a virtual half day conference on Agentic AI. It'll be interactive, practical, and it'll feature some of the most influential people in the AI agents space as speakers. You don't want to miss this one. It'll be live in the O'Reilly platform, which many employers and universities provide access to. Otherwise, you can grab a free 30-day trial of O'Reilly using our special code SDSPOD23. We've got a link to that code ready for you in the show notes. All thanks of course to everyone on the Super Data Science podcast team for producing another fun episode for us today for enabling that super team to create this free podcast for you we are deeply grateful to our sponsors. You can support this show by checking out our sponsors links, which are in the show notes, and if you yourself are interested in sponsoring an episode, you can get the details on how to do that by making your way to jonkrone.com/podcast.
00:57:53
Otherwise, share this episode with people who'd like to hear it. Review the episode wherever you listen to your episodes. Subscribe obviously if you're not a subscriber, but most importantly, I just hope you'll keep on tuning in. I'm so grateful to have you listening, and hope I can continue to make episodes you love for years and years to come. Until next time, keep on rocking out there, and I'm looking forward to enjoying another round of the Super Data Science podcast with you very soon.
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