Why You Should Become a Data Scientist

Why You Should Become a Data Scientist

What do you look for in a career? Chances are, you’re looking for a way to make use of your particular talents, a field that’s secure and reliable, a work/life balance, and good compensation. For the right people, data science offers all of that and more! It was LinkedIn’s #1 Most Promising Job in 2019, and Glassdoor’s 2nd Best Job of 2021! Actually, Data Scientists topped that list from 2016 to 2019 before being dethroned by developers, which we also train at Codeup. So why all the hype? What makes it the best? Keep reading to learn why you should become a Data Scientist!


Use Your Talents

Do your colleagues hate you for overanalyzing things? Have you found yourself turning everything into a spreadsheet? Do you make your life decisions based on careful calculations using past data? You might thrive as a data scientist! 

This is the first section on our list because data science isn’t for everyone, and it’s important to consider where your strengths and interests actually lie. If you don’t have a penchant for numbers and historical patterns, or if careful analysis bores you, this may not be for you. For some people, though, data science is fascinating, rewarding, and a great way to make use of their talents and natural thought processes. Check out how much our students love what they do every week in this blog post


The Field is BOOMING

The field is booming, the demand is high, and the job is the best. 2020 was the first year since 2016 that Data Scientist was not the number one job in America, according to Glassdoor. Now it sits at Number 2, but rest assured, the growth and demand show no signs of slowing down. Since 2012, there has been a 650% increase in data science positions. However, the supply of data scientists remains relatively low. Sure, it’s been the number 1 job for a while now and people are catching on, but more and more positions are also opening, so supply isn’t actually catching up to the demand. At least, for qualified data scientists.

One reason for the continued low supply is that prospective data scientists who go traditional routes are often stuck in school for 2-4 years. Meanwhile, the demands and technologies are changing rapidly, so what they are learning risks being outdated by the time they graduate. Another reason is that a Master’s Degree does not in and of itself prepare grads with the skills needed for the job, or even just to nail their technical interviews. They might learn about data science for years, but if they don’t practice it, don’t have a repository that they push to regularly, haven’t built a data pipeline, don’t have project experience, etc., they aren’t ready to work as a data scientist. This lack of post-grad readiness is another contributor to the low supply, despite many graduate students thinking they’re ready for the job search. (Thinking of getting a Master’s Degree? Learn more, here.) Altogether, this makes a trained data scientist all the more valuable, relatively rare, and in very high demand.

Job Security

So, you know that demand is high now and that properly qualified data scientists (with flexible demands) will have no trouble finding a job. But you don’t just want a job now, you want a career for the long haul. Is this just a trend that’s going to fizzle out? Unlikely. Will this still be a secure path in the future, say 10 or 20 years down the road? All the data say yes!

The U.S. Bureau of Labor Statistics lists Data Scientists as one of the fastest-growing occupations. They project a 31% growth rate with nearly 12 million new jobs between 2019 and 2029. More and more companies will have no choice but to start using data-driven decision making, lest they risk being put out of business by their data savvy competitors. Artificial intelligence (AI), a subset of data science, alone is creating millions of jobs! However, it’s also wiping out over a million jobs. Want to make sure your skillset stays in-demand? Start a career with job security. Employers will always need humans to program the machines, understand the data pipeline, write the algorithms used by AI, and continue maintaining the tech and the data.

Work/Life Balance

Many of Codeup’s career transitioners know what it’s like to work in oilfields, call centers, food service, sales, retail, and teaching. The ones that don’t require manual labor or being on your feet all day still require odd hours, lots of emotional exertion, or giving up weekends. Some people love this sort of work, but others are missing that work/life balance. Work/life balance is something that’s completely subjective based on where you are in your life, how much you enjoy your work, how much is required of you, and so on.

With a career in tech, you will very likely be working at a desk from 8 or 9am to 5 or 6pm with weekends off. You won’t be manual laboring, rushing to get someone to cover your shift, stressing about getting in hours, or speaking to customers or children all day long. Will some days be more stressful than others? Are you going to have to stay late sometimes to meet deadlines? Will you spend some of your “free” time learning the newest technologies? Yes. But for the most part, you can relax. You’ll have a steady work schedule, an average stress level, above-average flexibility should you need to adjust your schedule or take time off, and you will almost always have evenings and the weekends to yourself or to spend time with family and friends.

Great Compensation and Benefits

Interested in boosting your earning potential? Data science is a lucrative field with a highly valuable skillset. You could potentially double your current salary with your very first in-field job. Your employer might also cover health insurance, provide retirement contributions, and offer flexible paid time off. Entry-level data scientists have some of the highest starting salaries around, with averages of:

And it’s only upward from there as your career progresses. In San Antonio, which is where our headquarters are, we’re seeing salary averages of:

Want to start earning six figures within a few years? Become a data scientist. With just a few years of experience, your talent becomes much more valuable. Keep reading to see how long it takes to advance.

Room for Growth

In the tech field, room for growth is limitless. There are always new technologies to learn and adapt to, new skills to perfect along the data pipeline, and career opportunities abound. The next step after beginning your career as a Junior Data Scientist is often Senior Data Scientist, which is the middle level. Typically, this requires 3-5 years of relevant work experience and the abilities to write reusable code, formulate machine learning algorithms, and build strong data pipelines in cloud environments. 

After senior is the top level, the most experienced member of the data science team. You may see them referred to with different titles, such as Vice President, Director, Head, or Chief. They lead the data science team, have over 5 years of experience, and are fluent or very well-versed along the data science pipeline. This person knows best practices for building and deploying a predictive model, can efficiently write code, and will know the highest impact projects to work on (TowardsDataScience).


So, you’ve got a booming industry, a secure field, a skill set that’s in high demand, the ability to advance your career, a lucrative salary, a solid work/life balance, and to top it off, you get to do what you’re good at and passionate about. No wonder it’s the best job out there! If you enjoy working with data, this could be an incredibly fulfilling career path. Did you know it only takes 6 months to actually become a data scientist? Codeup will take you from “never heard of it” to your first job in-field. Interested? Apply now, then we’ll set up a call to discuss it further!

What are the Math and Stats Principles You Need for Data Science?

Is there math in data science? Read our blog to learn how much math you need!

Coming into our Data Science program, you will need to know some math and stats. However, many of our applicants actually learn in the application process – you don’t need to be an expert before applying! Data science is a very accessible field to anyone dedicated to learning new skills, and we can work with any applicant to help them learn what they need to know. But what “skills” do we mean, exactly? Just what exactly are the data science math and stats principles you need to know?

What are the main math principles you need to know to get into Codeup’s Data Science program?


Do you know PEMDAS and can you solve for x? You will need to be or become comfortable with the following: 

  • Variables (x, y, n, etc.)
  • Formulas, functions, and variable manipulations (e.g. x^2 = x + 6, solve for x).
  • Order of evaluation: PEMDAS: parentheses, exponents, then multiplication, division, addition, and subtraction
  • Commutativity where a + b = b + a
  • Associativity where a + (b + c) = (a + b) + c
  • Adding and subtracting matrices
  • A conceptual understanding of exponential growth/decay- things can increase at an increasing rate

Descriptive Statistics

Know what a min, max, mode, median, and average are. Have a conceptual understanding that stats/probability is about trying to quantify uncertainty.

Data Visualization

Know what a scatterplot is and how to read a barplot.

How to Learn and Expand on These Concepts

There are a number of great resources out there to teach you these and similar concepts. Khan Academy is a great starting place for data science math! If you want to know what exactly we assign our applicants, you’ll just have to apply!


What about once you’re in Codeup?

What You Won’t Do

Do we do any mathematical proofs for concepts or perform derivations? No. 

Do we do any calculus and probability calculating by hand? No.

Are we transforming equations, where we cancel out units or terms and do lots of algebraic gymnastics? No

What You Will Do

Will we have Python solve our linear algebra problems for us? Yes

Will we have Python calculate probabilities, the area under a curve, and the slope of a line for us? Yes

Will we have Python do all of the calculus for us? Yes


See, the data science math and stats slice of the pie is certainly doable. If you like problem-solving and are ready to challenge yourself, you’ll love data science! If you are interested in learning about data science, just apply! Our Admissions Manager can work with you to get you where you need to be starting from where you are now. Let us help you get there so you can launch a great new career.

What is the Transition into Data Science Like?

What is the transition into data science like? Our alumni Katy Salts and Brandi Reger share their experience!

Alumni Katy Salts and Brandi Reger joined us at a public panel event to discuss their Transition into Data Science! Keep reading to hear what stands out to them about Codeup, how Codeup helped them launch a career in data science, and what they are up to now.

The Panelists

Katy Salts graduated from Codeup’s first Data Science cohort back in June of 2019. She has since started working as a Data Scientist at Cyber Fortress, where she has been for about a year. Prior to joining Codeup, Katy studied and worked in biology. 

Brandi Reger graduated from Codeup in July of 2020 with our third Data Science cohort. She has since been hired as a Data Engineer at HEB. Prior to joining Codeup, Brandi had just gotten a degree in Geology in pursuit of becoming an Archeologist after serving in the Army for 5 years. She minored in Statistics, which she wanted to pursue further after realizing archeology wasn’t for her.

Why did you choose Codeup over other routes?

“I didn’t have the ability to take off work and pursue a Masters Degree for 2 years or an undergrad degree for 4 years, and this was the perfect crash course. Also the cost is a lot cheaper than graduate programs and I got a lot of help with grants and scholarships.” – Katy

What sets Codeup’s curriculum apart?

“It’s very applied. You put what you learn into use and you learn why you learned it that way. In collegiate, you’ll take a test but it’s not quite the same thing as getting your hands dirty and figuring it out. Your mind can drift during lecture and you don’t really “get” it until you do a project. But since you’re doing a project on almost every topic at Codeup, that really solidified the topics we were learning for me.” – Brandi

How much programming did you know coming into Codeup?

“I could not name one programming language. I didn’t know anything. The prework was the first time I wrote a line of code. With Codeup, they will start you from the very beginning of learning programming, it’s just fast-paced.” – Katy

How did you address fears or doubt about whether or not you can do it?

“Just get your hands dirty and do it! Once you’re working on it, it turns out it’s not as bad or as hard as you thought it was. Because you don’t know it, it seems like this amazing, glorious thing in the sky but when you actually work on it you’re like ‘oh, it was just that, that’s no big deal.’ All you have to do is try and apply yourself and you’ll figure it out.” – Brandi

What was one of your favorite lessons?

“The first project we had, our instructor had to go away for the weekend so we couldn’t contact her. The whole class collectively could not come up with an answer to the problem. We were freaking out thinking like ‘We did something wrong. Why can we not figure it out? She left us!’ We were so scared. When she came back she said ‘you learned a really important lesson, and that’s that sometimes there is no answer to the question. You just need to be able to look at the data and take it for what it is.’ You can’t make things up based on the data you find. You have to come up with real conclusions. That one hit me pretty hard and I never forgot it.” – Katy

What advice would you give to someone in the admissions process?

The prework is the best thing you can do for yourself coming into the program. We didn’t go in knowing everything and memorizing it all, and they didn’t expect us to either. You just have to know basic concepts. Like understand that there’s a way to do something and maybe not necessarily know how yet but know that there’s a way. So just being exposed to it ahead of time is helpful.” – Katy

Brandi, you had the unique experience of being in-person and virtual. What was that like?

“I really liked being remote. Being in class, it was distracting. My classmates, we were best friends basically while we were going through the program. So we were constantly chatting and everything. But once it was remote it was all quiet. There was nobody distracting me, and I could focus on what I was doing.We could still chat when I wanted to but I could turn off chat when I needed to. Also when the teachers were talking, I had my dual monitors, so I was able to look up what they were talking about and it really solidified what I was learning. So, I got a lot out of it.” – Brandi

How was your experience with our student placement team?

“They are not messing around! I mean, they start early and they leave no stone unturned! They come in and they’re completely revamping your Linkedin, email, photos, what you should and should not post. One-on-one meetings about what you’re looking for in a company and how they can help you get there, what connections they have, who to talk to, who to connect with. How to answer phone calls. Interview questions in class, like answering them together, answering them one-on-one. Mock interviews, resume building. Beginning to end, I mean, that alone was worth it to go through Codeup because no other place gives you that much attention on one single student, and everybody got that attention, and everybody got jobs! And that’s because of all the work they put into getting you ready not just technically, but professionally. They will get you there if you just listen to them and don’t fight back, don’t argue. Just do what they’re saying because they’ve done this enough times and they know what works. Just trust them.” – Katy

What does working in the field look like? 

“My job is awesome! I get to work from home. It’s definitely constant learning. I think Codeup really prepared me for that because they didn’t just teach me how to code- they taught me how to learn and how to learn quickly. I carried that into my job and my job taught me not only to code but also to be able to apply that to a business model. Learning doesn’t stop after Codeup.” – Katy

What’s a typical day in the life for you?

“I just started a couple of weeks ago so right now a day in the life is reading things and trying to figure out what they are. There are multiple data engineering teams so we all get together and talk about the projects we’re working on and new projects coming down the line. Or we’ll talk to the Data Scientists and they’ll tell us how they’re using our products and what they need from us, and we get to see what happens with the data we’re cleaning and putting together. It’s a constant moving forward and checking in with each other. At this job, you use every programming language you learn at Codeup and 10 others that I don’t even know yet. Everything you learn is useful for something. Codeup teaching you how to learn is very useful because there are still a lot of things I need to learn now.” – Brandi

What distinguishes a Data Engineer from a Data Scientist?

“As a Data Engineer, I clean up the data and put it together in a way that makes sense. We take the data in a nasty, messy state, it’s all over the place, and make it nice and neat so that somebody else can figure something out from it.” – Brandi

Is there anything that surprised you in terms of what was considered “data science” during your job search?

 “You have to think of data science as a spectrum of things and on that spectrum you can specialize in different areas, like just gathering data, or just cleaning, just modeling, just making presentations or dashboards. A lot of businesses don’t know what they actually need. They’ll say they need one thing but really what they’re asking for is something else. So you need to be able to sort through those and ask clear questions in interviews. That was a tough lesson to learn in the job search.” -Katy


More personal quotes include Katy saying that this was “the best thing I’ve done” and Brandi saying that the experience “made me a better person.” Thank you both for joining us! At Codeup, we don’t just teach code – we are in the business of empowering life change!

If you’re ready to pursue a new, fulfilling career, check out our blog “What Data Science Career is For You?” to continue leaning into this exciting field.

What Data Science Career is For You?

What data science career is for you? Read our blog to learn about roles available throughout the data science pipeline.

If you’re struggling to see yourself as a data science professional, it may be because the field of data science can be rather broad. While many positions in data science work across the entire data science pipeline, some are more active in a particular area. Codeup prepares you to take any number of data science career paths throughout this pipeline. To help explain the differences, we’ll discuss where some of our alumni have ended up. Keep reading to see what appeals to you!

Data: Plan and Acquire

First up is the data portion of the data science pipeline, where we plan and acquire the data. 

Business Analysts like Jesse Ruiz and Jeff Hutchins, as well as People Analytics Specialists like Tim Sotirhos help their companies or departments identify what questions need to be answered, and what data they need to answer those questions. This often involves researching or reviewing existing processes and strategizing how they can be improved. They may work closely with project managers to create an action plan.

Information: Process and Prepare

Then, in the information portion, we process and prepare the data for future exploration.

Data Analysts like Matthew Zapata aggregate and manage, that is, combine, process, prepare, and store, the data for easy access. They research and improve on data quality and safeguard against data loss or errors. They also explore the data for potential relationships between variables, usefulness, and outliers. Data Analysts may also acquire the data and/or analyze the data for potential insights, but typically do not build and deploy machine learning models.

Knowledge: Explore and Analyze

In the knowledge portion, data is analyzed and turned into meaningful statistics

This stage is typically combined with Information or Intelligence rather than existing on its own. In a given company, their Data Analysts may both prepare and explore data, or the Machine Learning Engineers will both explore and model the data. Data Scientists, the broadest career in data science, typically will do all three, like Ednalyn De Dios. Unlike other data professionals (unless otherwise trained), Data Scientists can work anywhere along this continuum. 

Intelligence: Model

Once we have that knowledge, we use the intelligence stage to turn it into future predictions with machine learning models.

When the data is ready, Machine Learning Engineers like Michael Moran and Eric Escalante write the algorithms necessary to build, deploy, and continue to improve upon machine learning models. A machine learning model is a file that uses an algorithm to identify patterns and trends from data.

Decision Science Analysts, like Jeff Roeder, use math, stats, and machine learning models to convert intelligence-driven insights to actionable recommendations. They answer the questions asked by Business Analysts earlier in the timeline to draw conclusions for their company.

Action: Delivery

Finally, the action stage lets us integrate these insights into tests, then continue to monitor and evaluate new data.

Security Data Analysts like Sara Pena need to be able to identify trends from automated models that they or someone else created. They meet with stakeholders to discuss their analysis and help develop tests and prototypes of their recommendations. They then own and execute projects related to continuous improvement of their company’s processes.

Where Do You Fit In?

It’s totally possible for YOU to take on any of these job titles! Maybe it’s time to explore these careers even further, especially if one excited you. While Codeup does prepare and train Data Scientists, you may find your niche and prefer one stage to another. Whatever you choose, Codeup will help you land the job you want not only with our expert, employer-driven curriculum but also with professional help and placement services. Click here to learn how we help you land a job you love.

Apply today to become a Data Scientist.

What is Python?

Another installment of our What is it Wednesday - this week answering the question what is Python?

If you’ve been digging around our website or researching tech tools, you may have heard of Python. Python is a programming language that can be used by software developers, accountants, mathematicians, and especially, data scientists. Python is actually the dominant programming language in data science, often used for data wrangling, analysis, visualization, and machine learning.

What is Python Used For?

Python is an object-oriented language, meaning that the focus is on data and procedures rather than functions and logic. By writing out procedures, you can actually modify the data you have. It can help you automate mundane, repetitive tasks, such as downloading websites, changing the format of those websites, renaming those files, and uploading them to servers. With Python, you can program all of these tasks to be done automatically every time you download a website. This automation saves data scientists a lot of time. 

Is it Good for Beginners?

Yes! One of the key features of Python is the short, easy to understand syntax in which you write your code. Its minimalistic nature allows you to focus on solving your problem rather than solving the code itself. It reads and feels more like English as compared to other common programming languages. Sometimes, what would take you 4 lines of code in Java can be done with 1 line of code in Python. Because it is a high-level language, meaning it is largely independent of your computer specifications, you can get started using Python on whatever device you have – even phones, tablets, or PlayStation!

How is it Used in Data Science?

Python can be used for data wrangling, data analysis, data visualizations, and applying machine learning algorithms. Data wrangling involves preparing raw data for use by parsing it, meaning to convert each data point to a standard format, and by cleaning it, which means to separate useful data from erroneous or missing data points. This is relatively easy if you have only a few data points, but parsing Big Data by hand, like a thousands-of-terabytes large list of every transaction a retail store has ever had, is out of the question. So, Python is used to automate these tasks. It’s also used for data analysis – helping you find trends, correlations, variations, and outliers; data visualizations – helping you chart or graph your findings to show to others; and machine learning, as the complex algorithms and workflows that it’s capable of make it great for Artificial Intelligence.

While this all sounds quite complex, remember, every data scientist started out knowing nothing about Python. Want to try it out? Check out our events page to see when our next free, beginner-friendly, Python Workshop is! If you don’t see one listed, check again soon – we usually have one every other month. 

What is Machine Learning?

What is machine learning? Check out our blog to learn more about machine learning!

There’s a lot we can learn about machines, and there’s a lot machines can learn about us, too! Ever wonder how virtual personal assistants or dating apps work? Or how Netflix picks the perfect show for you? They’re learning from you by optimizing and building upon human-created algorithms. Each text you type, person you swipe, or show you pick is stored as data, and through a process called machine learning, that data is plugged into algorithms to streamline your next texting, dating, or show-picking experience.

“Shows You Might Like”

Let’s take any entertainment streaming service. It stores information about the titles you’ve watched, like genre, actors, year, category, and how (if) you rated them, in order to recommend other titles you may also enjoy. It also considers what other titles members with similar preferences have enjoyed. All of this data is stored and used as input in algorithms, which are sets of rules that solve a problem. The problem, in this case, is that you might like to have a new show to watch.

As you continue interacting with your entertainment streaming service, the better the recommendations will be, because the more data it has on what you choose to watch. 

Is the machine really learning?

Yes! Well, it’s learning what humans tell it to learn. The same way we might read through a book given to us to study and memorize course material, the computer gathers and stores the data we tell it to store. However, we can choose to go beyond that book and learn from other sources by running an internet search or asking others. The machine lacks the intelligence to do this on its own, but if it was programmed to do so by a human, it could.

So, what is machine learning?

Wikipedia defines machine learning as “the study of computer algorithms that improve automatically through experience.” The key word here is “automatically.” A human manually sets up an algorithm that the machine uses to come to conclusions, but the more data the machine has, the more it has to “learn” from to improve its conclusions, without the need for a human programmer. However, the machine is only improving upon the algorithm, optimizing the output it gives with the more data it has, and not creating its own algorithms. Humans will always be needed to tell the machines what to do.

Want to teach a machine how to learn?

If this sounds cool to you, have you ever considered a career in data science? There are a number of fascinating things to learn about data science and machine learning, and the best way to learn is with hands-on experience through numerous projects and fully-immersive learning. Click here to learn about how Codeup provides all that and more in order to set you up in one of the most interesting tech careers out there. Applying is absolutely free, takes only 5 minutes or less, and if you don’t get a job in-field, we refund your tuition.

Codeup Grads Win CivTech Datathon

CivTech Datathon

Many Codeup alumni enjoy competing in hackathons and similar competitions. Now that we train Data Scientists, recent alumni have been competing in datathons, too. At the 2020 CivTech Datathon, teams from two Data Science cohorts, Bayes and Curie, took 1st and 2nd place! The Codeup community is killin’ it at these events and we can’t wait to highlight their achievements!


What is CivTech Datathon?

CivTechSA is a partnership between Geekdom, a coworking space that every current Codeup student has complimentary access to, and San Antonio’s Office of Innovation. In an effort to connect local communities, ideas, and data to help improve the City’s services, the CivTech Datathon competition was born! Using public datasets, competitors look for missing data and areas for improvement and identify impactful solutions to current civic problems. Then, they present their insights to City of San Antonio departments like San Antonio Water System (SAWS) and Via Metropolitan Transit.

Codeup Alumni Take First and Second Place in 2020

Boasting first place was the Curie team We Came, We SAWS, We Conquered with Ryan McCall, David Wederstrandt Sr., Chase Thompson, Cameron Taylor, and Jeremy Cobb. They used data from SAWS regarding sanitary sewer overflow (SSO) events and weather data from the National Oceanic and Atmospheric Association to predict the root causes of SSO events. They generated a system that SAWS could use to prioritize maintenance of the sewers to limit the risk of these events, with the potential to save hundreds of millions of city dollars while keeping gastrointestinal health risks at bay.

Taking second place was team Get on the Bus with Sean Oslin, Sara Pena, Fredrick Lambuth, Misty Garcia, Kevin Eliasen, and Faith Kane. With their project, they aim to increase ridership of public transportation. Using open data from VIA and the Census Bureau Data API, this team identified areas of improvement to make our community more diverse and equitable by making buses more accessible.

We Won in 2019, Too

As a special shoutout, at the 2019 CivTech Datathon, a team from our very first Data Science cohort competed and won the “Most Solvable” award. The team members were Ednalyn C. De Dios, Joseph Burton, and Sandra Graham. Their project was similar to our Curie team – they trained a model to predict which pipes would overflow.

Want to Compete Next Year?

If you’re passionate about improving civic issues and want to present your findings to city stakeholders too someday, Codeup can teach you how! You might actually inspire lasting change (or at least get thousands of dollars in prize money)! If these Codeup students could win first and second place at the datathon, what’s stopping you? Click here to learn more about our Data Science program. Then, we’ll see you at next year’s CivTech Datathon!

From Slacker to Data Scientist: Journey to Data Science Without a Degree

data science without a degree

Butterflies in my belly; my stomach is tied up in knots. I know I’m taking a risk by sharing my story, but I wanted to reach out to others aspiring to be a data scientist. I am writing this with hopes that my story will encourage and motivate you. 


I don’t have a PhD. Heck, I don’t have any degree. Still, I am very fortunate to work as a data scientist in a ridiculously good company. Here’s how I did it (with a lot of help).


Formative Years

It was 1995 and I had just gotten my very first computer. It was a 1982 Apple IIe. It didn’t come with any software but it came with a manual. That’s how I learned my very first computer language: Apple BASIC.

My love for programming was born.

In Algebra class, I remember learning about the quadratic equation. I had a cheap graphic calculator then, a Casio, that’s about half the price of a TI-82. It came with a manual too, so I decided to write a program that will solve the quadratic equation for me without much hassle.

My love for solving problems was born.

In my senior year, my parents didn’t know anything about financial aid but I was determined to go to college so I decided to join the Navy so that I could use Montgomery GI Bill to pay for my college. After all, four years of service didn’t seem that long.

My love for adventure was born.

Later in my career in the Navy, I was promoted as the ship’s financial manager. I was in charge of managing multiple budgets. The experience taught me bookkeeping.

My love for numbers was born.

After the Navy, I ended up volunteering for a non-profit. They eventually recruited me to start a domestic violence crisis program from scratch. I had no social work experience but I agreed anyway.

My love for saying “Why not?” was born.


Rock Bottom

After a few successful years, my boss retired and the new boss fired me. I was devastated. I fell into a deep state of clinical depression and I felt worthless.

I recall crying very loudly at the kitchen table. It had been more than a year since my non-profit job and I was nowhere near close to having a prospect for the next one. I was in a very dark space.

Thankfully, the crying fit was a cathartic experience. It gave me a jolt to do some introspection, stop whining, and come up with a plan.

“Choose a Job You Love, and You Will Never Have To Work a Day in Your Life.” — Anonymous


Falling in Love, All Over Again

To pay the bills, I was working as a freelance web designer/developer but I wasn’t happy. Frankly, the business of doing web design bored me. It was frustrating working with clients who think and act like they’re the expert on design.

So I started thinking, “what’s next?”

Searching the web, I stumbled upon the latest news in artificial intelligence. It led me to machine learning which in turn led me to the subject of data science.

I signed up for Andrew Ng’s machine learning course on Coursera. I listened to TwitML, Linear Digression, and a few other podcasts. I revisited Python and got reacquainted with git on Github.

My love for data science was born.

It was at this time that I made the conscious decision to be a data scientist.


Leap of Faith

Learning something new was fun for me. But still, I had that voice in my head telling me that no matter how much I study and learn, I will never get a job in data science without a degree.

So, I took a hard look in the mirror and acknowledged that I needed help. But I wasn’t sure where to look.

Then one day out of the blue, my girlfriend asked me what data science is. I jumped off my feet and started explaining right away. Once I stopped explaining to catch a breath, I managed to ask her why she asked. And that’s when she told me that she’d seen a sign on a billboard. We went for a drive and I saw the sign for myself. It was a curious billboard with two big words “data science” and a smaller one that says “Codeup.” I went to their website and researched their employment outcomes.

I was sold.



Before the start of the class, we were given a list of materials to go over.

Given that I had only about two months to prepare, I was not expected to finish the courses. But, I did them anyway. I spent day and night going over the courses and materials, did the tests, and got the certificates! I knew then that I could get into data science without a degree.



Codeup was a blur. We had a saying in the Navy about the bootcamp experience: “the days drag on but the weeks fly by.” This was definitely true for the Codeup bootcamp as well.

We were coding in Python, querying the SQL database, and making dashboards in Tableau. We did projects after projects. We learned about different methodologies like regression, classification, clustering, time-series, anomaly detection, natural language processing, and distributed machine learning.

More important than the specific tools, I learned: 

  • Real data is messy; deal with it.
  • If you can’t communicate with your stakeholders, you’re useless.
  • Document your code.
  • Read the documentation.
  • Always be learning.


Job Hunting

Our job hunting process started from day one. We updated our LinkedIn profile and made sure that we were pushing to Github almost every day. I even spruced up my personal website to include the projects we did during class. And of course, we made sure that our resumé was in good shape.

Codeup helped me with all of these.

In addition, Codeup also helped prepare me for both technical and behavioral interviews. The student placement team taught me how to optimize answers to highlight my strengths as a high-potential candidate.



My education continued even after graduation. In between filling out applications, I wrote code every day and tried out different Python libraries. I regularly read the news for the latest developments in machine learning. While doing chores, I would listen to a podcast, a TedTalk, or a LinkedIn learning video. When bored, I listened to or read books about data or professional development.

I’ve had a lot of rejections. The first one was the hardest but after that, it kept getting easier. I developed a thick skin and learned to keep moving.



It took me 3 months after graduating from Codeup to get a job. When I got the job offer, I felt very grateful, relieved, and excited.

I could not have done it without Codeup and my family’s support. It is possible to start a career in data science without a degree!


This blog post was written by Ednalyn C. De Dios for Towards Data Science: A Medium publication sharing concepts, ideas, and codes. An edited version is being shared on Codeup with permission from the author. You can reach them on Twitter or LinkedIn.


If you’d like to learn more about how Codeup can help you launch your career in data science without a degree (or with!), schedule a call with our team today or reach out to admissions@codeup.com!

Students Discuss Their Transition into Data Science

Event Recap: Jada Shipp, Daniel Guerrero, and Ryan McCall share their Data Science Student Experience

Our Transition into Data Science Panel event was full of passion. The audience met three of our current students: Jada Shipp, formerly a Newborn Hearing Specialist, Daniel Guerrero, who was a Vaccine Product Manager, and Ryan McCall, who was an overnight Surveillance Agent at Walmart. They discussed what led them to this career path, how their Data Science bootcamp experience has been so far, and what they plan to do after Codeup. Read on to learn about the Codeup Data Science bootcamp experience and their key takeaways and advice!


Why data science?

The students began by discussing their “whys” and “hows” of pursuing data science at Codeup.

Jada: I was going to apply to medical school but decided it wasn’t for me. A friend suggested Codeup, which is how I learned about data science. I realized it’s something I was already interested in. I was really big on solving all my problems in school on Excel and coming up with the best graphs and digging around in numbers. I just didn’t know there was a name for this, and certainly didn’t know it was a career path.

Daniel: At a new job as a Vaccine Product Manager, they started asking me to do research about where we’re selling our product and who’s buying. I quickly discovered this was a massive, billion dollar company and there was no one there that actually knew how to use Excel or how to look at sales. It was mind blowing to me because I thought this was standard and everyone was doing it. Then I realized my company is not the only one that has this gap.

Ryan: I went to college for chemistry, then started working security but wasn’t satisfied with it. I wanted to use my brain more to think and actively solve problems at my job. So, I started looking at programming as a job career. I found data science and absolutely fell in love with it and spent two years trying to teach it to myself on the side of my security job. It was very hard, I spent most of that time figuring out what resources to trust. It’s a lot easier being taught than searching in the dark.


What is the learning process like at Codeup?

Lots of us know how it feels to come out of a full semester of class feeling like we didn’t absorb anything and not knowing how it applies to real life. Not here.

Jada: I thought getting admitted was a mistake and they’d kick me out at any time. I had zero coding experience and I really didn’t think I could do it. But I went step by step. ‘Maybe I can’t do this but I can probably do this one tiny part of it.’ They’ll explain it in as many different ways as they need to until your lightbulb goes on. 

Daniel: They’ll give you questions that force you to think. They won’t just give you an answer. You have to figure it out yourself but they give you the tools to be able to do that. And because of the collaborative environment, we’re learning from each other as a cohort and not just instructors. Now, I’m able to branch out into new territories and not be scared to do so because I’m well equipped.

Ryan: I’m honestly amazed by what we can do in such a short amount of time. I can do things that I thought it would take me years to do.


How are you liking Data Science at Codeup?

In such an immersive, fast-paced environment, it was a joy to hear how much our students have loved their experience so far.

Jada: Everything we do every week, I’m like “This is so cool! Last week was nothing!”

Daniel: Every week I have the same conversation with my parents about the program. “What are you doing this week?” “Oh it’s my favorite thing!” “You say that every week!” But other than everything, the projects are the coolest thing for me.

Ryan: We could talk about this for hours, we love this stuff! Aside from loving the material, you become like a little family. We’ve had all the same bad experiences, all the same good experiences. You’re speaking a similar language and you can figure it out together.


How are we helping you get the job you want?

In between lessons, students work closely with our placement team, where we help students land awesome jobs.

Jada: I want to use data in health care. I have a degree in Public Health and worked as an EMT, a scribe, and a hearing screener. Now, I want to merge my passion for public health with data science skills. I’m pretty confident I can get a job like this because the Codeup placement team helps you through every step– resume, Linkedin, interview skills. They are not gonna let you fall by the wayside. They remember you and who you are and what you’re looking for. It’s not a generic cookie cutter process where you get what you get. They know you and it’s customized for you.

Daniel: I told the placement team that I want to work with data that’s business to customer. Two days later they sent me applications and they’re actually what I asked for, in industries I actually told them about, with companies I actually mentioned. I was mind blown! The placement team will open as many doors as they possibly can to get you the right opportunity, I am more than impressed with the placement team.

Ryan: I want to be a data scientist and I don’t know what kind of data scientist, I just want to be one! And they can work with that, too!


Any tips for incoming students?

All three panelists said the same thing: DO THE PREWORK! 


Here are some resources they used:


We want to thank Jada, Daniel, and Ryan for sharing their passion with us and acting as mentors for prospective and current students for the night. They are each blossoming into data scientists that couldn’t hide how much they love what they do even if they tried. Do you want a similar transition? Start your own Data Science bootcamp experience today by learning more. Scared it’s too risky and you won’t get a job? That’s okay, we’ll refund your tuition if you don’t. Any more excuses? There won’t be once you give us a call.

The Best Path to a Career in Data Science

best path to a career in data science

In our blog, “The Best Path To A Career In Software Development,” we looked at how bootcamps provide a more direct path to a career than traditional undergraduate CS degree programs. Today we’re here to talk to you about how bootcamps provide a more direct path to a career in data science than a Master’s Degree. 

“But don’t I need a degree to get a job as a data scientist?” As a non-traditional educational model, we hear this concern a lot. The answer is yes and no. Yes, because most jobs in data science require some form of higher education. No, because that degree doesn’t have to be in something related to data science – data scientists have degrees in many different areas of study! So if the concern that lacking a piece of paper will prevent you from accelerating your career, fear no more.

While a Master’s Degree provides a lot of value, it isn’t the most direct path for a job. With the exponential growth in data generation and the race to keep up with storing and processing that data, data science no longer sits at the fringe of an ultra-specialized workforce. Companies now need much larger teams to analyze, model, and leverage the data they’ve collected. So while the field of data science might have once only been available to those skilled in highly academic algorithm development, it’s now a playground for those with some Python skills who know how to find valuable insights in a mess of data. 


Now let’s get a bit more specific. If modern roles in data science demand more practical skills, why is a bootcamp a better path?


#1: Responsive curriculum: Barely 10 years ago nobody had heard of data science. But in that short amount of time, the tools and technologies in the field have grown exponentially. Each year sees the introduction of new packages, visualization tools, and cutting edge technologies. With such a rapidly evolving landscape, it’s hard for traditional learning environments to keep pace. With our ears tuned directly to employers, we’re able to adapt quickly and ensure we’re teaching what hiring managers need.


#2 Hands-on Project-Based Learning: Have you ever watched Top Chef or The Great British Bake Off? You were probably pretty entertained, but how did that seared Ahi Tuna with orange mint avocado salsa and balsamic vinegar reduced amuse-bouche turn out? The sad reality is, watching experts do their thing doesn’t make you an expert. Nor does listening to lectures. Our program is built around the concept of praxis, which is essentially the practical application of theory, or the blending of theory and practice. Half of your 670 program hours are spent actually writing code, so you develop the muscle memory and experience of programming. A career in data science is like an old-time trade, like becoming a blacksmith: you have to learn from masters and practice, practice, practice. 


#3 Progressive Curriculum Structure: In a traditional degree, students study by taking several classes at a time. You may begin with data structures and algorithms, then move to SQL, then take Python, and so on. But real-world data science doesn’t work so neatly. You will never face a project where you’re only working with one of those tools, so this pedagogical method is misaligned with career demands. Our program focuses on real-world deliverables at every step of the journey, while exposing you to increasingly complex problems and projects. You start off applying basic tools to simple challenges. Then, we begin varying the data sets, the way you access that data, the type of methodology you use, and the deliverable you’re responsible for. To put it simply, the structure of a traditional degree teaches you how to use a hammer, a saw, and a chisel. Codeup teaches you how to build a stool, a birdbox, and a sculpture with those tools, and when to use which. 


#4 Job Placement Services: If education is your goal, stop reading now. If a career is your goal, then you’re in the right place. Most graduate institutions have career service offices where you can get advice on your resume and attend job fairs. But Codeup makes you a promise: get a job after graduation or get 100% of your money back. There are no two ways about that: our singular focus is your outcome. Unlike traditional institutions, we sell jobs, not education. 


#5 Messy Data: This is probably the most important difference between us and traditional degrees. We use real, messy, misleading, broken data so you learn how to draw insights from the real thing. Unfortunately, that is not the norm. Because of the segmented class structure, traditional degrees have to focus on using data that teach one specific skill. At Codeup, you’re always applying your tools to a real deliverable, so we’re able to use real data sets that intersect the challenges of multiple skills. 


Lastly, we encourage you to think about the return on your investment in your education.

Codeup* Private Master’s
  • $29,500
  • 6 months
  • 85% graduation rate
  • 88%  employment rate
  • $67,500 median starting salary
  • $62,280
  • 18-24 months
  • 61% graduation rate
  • 72.5% employment rate
  • $59,866 median starting salary

*read more on our outcomes


Most importantly, the opportunity cost of pursuing a master’s degree is equal to 13-19 months of employment. At a median salary of $67,500 from Codeup, that’s between $67,500-$101,250 in foregone earnings. 

So, you want a career in data science? A career accelerator like Codeup is the path for you. Still not convinced? We’re here to hear your concerns – contact us and let’s talk it through.