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.

Getting Hired in a Remote Environment

Graphic that explains the purpose of blog; How you can get hired in a remote environment

As a career accelerator with a tuition refund guarantee, we have always been focused on employment outcomes for our students. Going remote hasn’t changed that! Two months into this remote world, we thought we’d pause today to explain how we prioritize your success and ensure you get hired.


Your Professional Development

First of all, our career placement services are built on one-on-one relationships. Our Placement team works with students individually to develop a professional portfolio, define a strategy, and conduct a job search. They’ll help perfect your resume, conduct individual career coaching, and prepare you with mock interviews. And they don’t let off until you’ve signed that offer letter! Since we’ve gone remote, our placement team has digitized their curriculum so it’s accessible to all our students, and they’ve continued working one-on-one over Zoom. 


Our Professional Network

Those one-on-one relationships aren’t exclusive to students. It’s the same approach our team takes with their network of hiring managers and recruiters. From curriculum advisory panels to guest speaker lunchtime talks, we involve employers as often as we can. We forge a personal relationship that encourages repeat hiring, open communication, and trust.


Start with a bang!

Lastly, your job search kicks off with a bang in our staple Developer Days and Data Scientist Days. Normally, these are in-person demonstrations of capstone projects that end in a reverse job fair with employers. On April 16th, we hosted our first-ever virtual Developer Day. Over 160 people tuned into it live! Not only did we maintain the quality of the event, but we increased attendance and visibility. That event, especially while remote, kick starts your job search, connects you with employers, and increases your visibility as a candidate.


In person or remote, we remain committed to empowering life change and helping our students land jobs in new career fields. If you’ve been affected by COVID-19 in any way (layoffs, health, family, etc), check out our recently announced COVID-19 Relief Scholarship.

5 Common Excuses Keeping You From Breaking Into the Tech Field

Just a few months before starting at Codeup in the Redwood cohort, I was sitting in the football stadium at the University of Colorado at Boulder, pondering what I would do after graduation. The commencement speaker that year was Kate Fagan, a sports reporter and commentator at ESPN. In her speech, something she said stuck out to me: “Try replacing ‘should’ with ‘want’ and, as frequently as you are able, make decisions with that rubric. Life is best when your ‘should’ and your ‘want’ are aligned.” Sitting there in that stadium, I realized that I knew exactly what I should be doing after graduating, which was applying to attend graduate school for the next five years. But the actual truth was, I didn’t know what I truly wanted. Did I really want to jump into something for five years that I wasn’t completely sure about?

With this in mind I moved to San Antonio after graduation, mostly to be closer to my family. One night at the dinner table, my brother-in-law mentioned several eye-catching billboards around town promoting a local coding bootcamp named Codeup. I had dabbled a bit in coding when I was in college, so my interest was immediately piqued. However, there were doubts nagging at the back of my mind. Am I even capable enough to attend an intensive coding bootcamp like this? I’m not really a super logical person… Am I cut out for this? etc, etc. Despite having a ton of reservations about my capabilities and the usefulness of attending a boot camp, I decided to take a leap of faith. And just a year-and-a-half later, I celebrated my one year as a software developer at Armor in Richardson, TX. In some ways, it feels like a dream. The hard work I put in, the days and nights of impassioned coding, pushing through all the excuses… and finally landing a dream job?! It’s a colorful blur.

So that’s why in this post, I want to address five common excuses that may be keeping you from considering a career in technology. At the end of the day, it doesn’t really make sense to let your fears and nagging doubts keep you from the job of your dreams.

1. “I’m not cut out for a career in tech”

This was one of the primary fears at the forefront of my mind when thinking about doing a complete shift to a technology career. And, as I went through Codeup, I heard this many times from my peers. To be honest, it doesn’t ever fully go away. There are days even now at work where I think I’m in over my head and that I don’t belong there (Imposter Syndrome, anyone?). This fear completely disregards the fact that I’m already doing it. The truth is, it isn’t always easy. Technology is constantly changing, creating new problems and forcing those within the field to continuously find new solutions. At my company, even our most senior developers are learning something new every day. We all have our doubts sometimes, but those self-limiting beliefs shouldn’t keep you from pursuing anything you set your mind to.

2. “I wouldn’t fit in with engineers”

Let me ask you something. What does a veteran, electrical technician, and college music teacher have in common? Well, there was at least one of each in my cohort at Codeup, and all of them excelled and went on to become software developers. Other characters in my cohort included a stay-at-home-mom, barista, marketing professional, and a chef. All of these, however, are just arbitrary labels. None of these people told themselves “I’m just going to be a barista forever, because that’s who I am” or “My personality only suits being in a teacher, so I’m not going to try something different.” The reality is, our self-concept is always constantly shifting. There was such a colorful diversity of backgrounds, personalities, and skill sets at Codeup, proving that there’s no one type or mold of individual that can pursue a technology career.

3. “I don’t want to work alone all day staring at a computer screen”

There are days where indeed this is the case for me, just “heads down coding”, but more often than not my days are filled with collaboration and communication with my teammates. When someone runs into a problem they don’t have the knowledge to solve, they track down someone who does. When a few of us are working in the same codebase, we make sure to frequently communicate to make sure we’re not stepping on each other’s toes. On top of that, we get to be a part of producing the product, providing feedback and suggestions. There are very few days where I just sit at my desk all day, boring holes into my computer screen. Although my experience may certainly be atypical, the main point I’m trying to make is that there is a large range of positions and cultures within the technology field. There are also other roles within the technology field beyond coding and data analytics, such as evangelists and solutions consultants. Both of these have lots of interaction with people and clients! Don’t be afraid to try a few different things until you find your fit.

4. “I don’t have enough experience”

Most of us at Codeup did not come in with prior experience in coding. The great thing about coding bootcamps is that they typically take you from 0 to 100 in a condensed period of time. They guide you through the entire process, allowing you to maximize your success, with everything from technical skills, networking, portfolio-building, and resume review. Even with bootcamps aside, there is a plethora of both paid and free resources online that give you the ability to learn a lot of the preliminary skills you would need. There are communities (e.g. Chingu) with the sole purpose of learning and building projects in new technologies. Experience can be gained, so seek out those resources. They’re only a few keystrokes away.

One thing to note about the technology field is that it’s becoming more and more heavily based on experience and not your formal education. Many companies will see the value in someone who has practical experience. The reality is that many companies are shifting towards seeking out individuals that can come in and hit the ground running with practical know-how instead of purely theoretical education.

5. “I’m not tech savvy enough.”

Although basic computer skills are necessary for success, it’s probably not as much as you think. And like I mentioned above, being able to excel in this field is all about embracing change and learning to learn. You may think you’re not tech savvy because you always have issues with your radio or you can’t get your apps to work right or you get frustrated with your computer software for not doing what you want it to – all of these things are valid struggles. Trust me, I’ve been there. The reality is this: Many of these skills can be learned.

As you look into pursuing a career in the technology field, don’t let these thought patterns keep you from getting where you want to be. Instead, ask yourself the real questions: Why do I want to do this? What kind of lifestyle do I see for myself? What am I passionate about? Excuses are excuses, not truths about you and your life. Set a vision and relentlessly pursue it, letting all these limiting beliefs slide off of you. They don’t have to define your journey.

Joyce Ling is a software developer at a cloud security company based in Richardson, TX. In her free time, she sings in a women’s chorus, rock climbs, plays guitar, and currently runs an organization to bring queer women together in the Dallas/Ft. Worth metroplex. 

Follow her on Instagram @ironicsushi or read more of her work at The Luscious Word.

Everyday Encounters with Data Science

You come home from work, tired to the bone and groaning as you realize you forgot to prep dinner tonight. So, what do you do? You plop down on the couch, whip out your phone and Google “restaurants near me”. You scroll down the list of places to eat and take into account a variety of factors: how high the ratings are, how many people have rated that restaurant, how far it is from you, whether or not it’s busy at that moment, the keywords mentioned in reviews, how long it’s open… the list goes on. You carefully bookmark each prospective restaurant so you can find it later. All of a sudden, you notice that without your knowledge, three hours have slipped by. You realize with a feeling of dread in your stomach that nearly all the restaurants around you are closed… and you head over to the nearest Whataburger in defeat.

A week later, life finds you in the same supine position on the couch, scrolling through restaurants. This time, though, you realize there’s a little compatibility rating next to each restaurant listing. As you scroll, you stumble upon a sushi restaurant that apparently is 90% compatible with your preferences. Without hesitation, you rush there with glee to find that it is everything you could have hoped for! Wow! Data science saves the day!

This very relatable experience is a prime example of how many of us have experienced data science in our lives without realizing it. Let’s take a quick moment to analyze how data science played the protagonist in this story. It is important to note that data science is a broad subject that encompasses a variety of things, including first gathering and shaping data, storing that data, then analyzing and visually presenting that data.


How Google Does It

Before we can start analyzing data, Google data scientists must first be able to gather information about everything from restaurant locations, hours, pricing, customer reviews, etc. Next, this massive influx of data must be efficiently manipulated and organized so it can quickly be retrieved, Marie Kondo style. Data scientists are then able to take and analyze this data and visually translate it into something that makes sense, such as the graph that displays the busiest hours at the restaurant, or the tidy list of restaurants you see in the app that is ordered by distance from you.

A simple action such as filtering by “Open Now” or “Price” requires a tight coordination of all of these steps in order to actually produce the output expected by the user. Amazingly, Google Maps is able to do this analysis real-time to constantly change its results based on your current location. Not only that, it is simultaneously analyzing every single person’s GPS data real-time. This allows them to generate components like the graph of how busy an establishment is at the moment.


Google Maps, A Man’s Best Friend

Another important factor in data science is using analysis to drive predictions (Read more here about the difference between data analytics and data science). For example, in calculating compatibility ratings, the program takes into account Google location history, search history, types of cuisine/restaurants you typically visit or avoid, whether you’ve saved/rated/visited a place or somewhere similar, or simply data that a user inputs about their dietary preferences. It also allows for constant feedback from the user if a compatibility rating seems off, with a “Not Right?” link attached to every rating. In other words, this rating is a prediction of how much you would like a restaurant based on your past history and behavior.

So how exactly has Google mastered this compatibility rating? Most likely, data scientists at Google have created algorithms to take the pieces of information listed above to produce a prediction. Essentially, it’s a way for a computer program to act more human. For example, think about how you recommend a show to your best friend. Most likely, you know your friend’s personality and history of shows that they’ve enjoyed, so you generally know what kind of show they may like watching.

Similarly, data scientists can use algorithms and statistical models to write code that continuously “learns” to make better predictions, creating an adaptable learning machine. With machine learning and artificial intelligence, Google is able to generate friendly human-like recommendations for over a billion people every single month, which is probably better than what your best friend could do.

When Robots Take Over the World

As the data science field continues to progress and we get closer and closer to true artificial intelligence, we will most likely see the effects of that trickle down into the parts of our everyday life that we hardly consider. We already trust the algorithms and generated recommendations that suggest where or when to eat. Eventually, even something as vital as a life-altering surgery will be dictated by recommendations generated by algorithms for doctors. Understanding and learning about data science will become more significant as it gradually becomes enmeshed with society in ways we may not even be able to fully comprehend. Read more here about Codeup’s 18-week Data Science program which covers most of the topics mentioned in this article, including data visualization, analytics, and machine learning.

Joyce Ling is a software developer at a cloud security company based in Richardson, TX. In her free time, she sings in a women’s chorus, rock climbs, plays guitar, and currently runs an organization to bring queer women together in the Dallas/Ft. Worth metroplex. 

Follow her on Instagram @ironicsushi or read more of her work at The Luscious Word.

Data Science Myths

By Dimitri Antoniou and Maggie Giust

Data Science, Big Data, Machine Learning, NLP, Neural Networks…these buzzwords have rapidly spread into mainstream use over the last few years. Unfortunately, definitions are varied and sources of truth are limited. Data Scientists are in fact not magical unicorn wizards who can snap their fingers and turn a business around! Today, we’ll take a cue from our favorite Mythbusters to tackle some common myths and misconceptions in the field of Data Science.


Myth #1: Data Science = Statistics

At first glance, this one doesn’t sound unreasonable. Statistics is defined as, “A branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data.” That sounds a lot like our definition of Data Science: a method of drawing actionable intelligence from data.

In truth, statistics is actually one small piece of Data Science. As our Senior Data Scientist puts it, “Statistics forces us to make assumptions about the nature of the relationship between variables, the distribution of the data, etc.” In the traditional Data Science venn diagram, you’ll see that math/stats make up ⅓ of a working professional. These are tools and skills to leverage, but data science itself is about drawing intelligence from data.




Myth #2: Data Scientist = Business/Data Analyst

This one is so common that we wrote a whole post about it! These are separate and different roles within the data field. While a data scientist will often do analytics, their spectrum of work is wider. A data analyst will use structured data to create dashboards and KPIs, while a Data Scientist deals with unstructured and messy data for a range of outputs. If they’re interested, business analysts will often progress to data scientists.




Myth #3: Data Science = Data Science

This one’s tricky, because it’s impossible to either confirm or bust! The ‘myth’ is that one person or company using the term Data Science is not necessarily the same as another person or company using the same term. Depending on organizational capacity, individual experience, educational background, and many other variables, we might be using the same name for different animals.

Tl;dr: don’t assume a common understanding across hiring managers, recruiters, and practitioners. Look instead for specifics of tools, techniques, methodologies, and outputs. That being said, this one falls in the “plausible” category, because it may actually be true in some circumstances, while false in others.




Myth #4: Data Science curricula are well-defined and consistent.

We recommend checking this one out for yourself! A quick google search for bootcamps, master’s degree programs, and online courses will reveal that different organizations teach different things. There is no commonly accepted framework for teaching data science! Some focus more on the engineering, others focus more on machine learning, some think deep learning is foundational, and some prefer to use R.

Our curriculum was built through employer interviews, practitioner interviews, market research, and company partnerships. But we’re based in Texas! A bootcamp in New York might follow the same process and end up with a different syllabus. Keep in mind, whatever your learning path, that there will be gaps in your learning. The most important thing is to recognize those gaps.




Myth #5: If I want to be a data scientist, I just need to learn Python or R.

This one is common and dangerous! Just like statistics, programming languages like Python and R are tools. They’re just pieces of a larger puzzle! Knowing Python without understanding the data science pipeline is like knowing how to build a floor without having a floor plan. Of course, these are valuable technical skills that give you a leg up, but they’re second in importance to asking the right questions, knowing what tools to use when, and communicating your findings.



Still have questions? Reach out to us! We’re always here to help.

Don’t Be Scared of Coding


Halloween Dancing GIF
When you’re scared to run your code, and it works the first try (Halloween style)!

When I first started as a developer I ran into some scary scenarios. My code was very error prone and I created some functions that I was expecting a string as the output but I got an object. It was very frustrating and I wasn’t even sure I would be able to understand software development let alone have to go and fix my code. My nightmare scenario was to be hired on as a developer and then have to troubleshoot, a.k.a. debug, some other developers code😱😱😱. I think most junior developers have had shared my fear at some point in their career. I was taught to heavily rely upon outputting your results to the console whether its good or bad to help troubleshoot.

In this post I will do my best to help calm the fears of my fellow junior developers with helpful debugging tools and hopefully transition away from relying upon console.log(). While my focus will be on JavaScript these tools can easily be translated to other languages.

Logging output to the console isn’t always helpful

For the longest time console.log() was my go-to when I needed to debug JavaScript code. It was my developer friend who told me what was wrong with my code and was never judgemental . I will admit that there are few scenarios where it is “OK” to output the results of a function to the console.

Except more recently, I have found using console.log() to be less useful when debugging and only making things worse by expecting a good outcome and then disappointing me. Like an expired $5 Amazon Gift Card (Thanks, grandma). Take for example, this gem:

Object doesn't support property or method 'from'

That lovely error is from IE 11 which does not support Array.from(node List) prototype without a shim/poly fill that is available here.

However, I have come to find out that logging errors to the console is either inefficient or completely irrelevant to what may actually be causing the problem in your code. I will admit that some errors are easily resolved by reading the output of the error message and fixing the typo or following the instructions of the error message to help resolve the problem.

So in the image above we are shown a node item which has so many properties that you might not be aware of, which renders console.log useless. You have to know ahead of time what value or specific attribute you are trying to output which requires some abstract thinking or referencing documentation to identify this property. Now using the console.log will output all the entire properties of the node item but its an unnecessary step to add in this line of code while you debug and then go back and remove the line of code.

“You don’t know what you don’t know” -Bill Parcel (Head Coach New England Patriots)

The error the console outputs might provide you with the location of the error, but you may not know why are you getting the error in the first place. Maybe you’re passing the incorrect value to the function or you are working with event handling and bubbling events.

Either way, chances are that you aren’t thinking about that one property that is actually passing the value your expecting because you weren’t aware of how to call it or pass it along.

I’m going to piggy back off of Mozilla’s Debug Playground as an example for why you shouldn’t use the console and instead use the Browsers built-in debugger or equally as helpful is an IDE’s built-in debugger.

cough cough Visual Studio Code cough cough

 Now, whether you prefer Chrome over FireFox is a whole different can-o-worms.

IDE Tools and Online REPLs

“Hey man, I’m a back-end developer and so I need to console.log”

-Fictional Backend Developer

So maybe you don’t have a fully fleshed out web form or you’re trying to get data from some API or JSON file. You need to see data from the function you’re writing or better yet you want to test a function that someone else wrote without having to clone the entire project. Using console.log() seems like the go to. In fact, around ¾ of Node.js developers report using it (in 2017) for finding errors in their applications.

I’m here to tell you of a few better ways to debug your code.

IDE Tools

The first tool I have come to love and trust is one made by a company called Wallaby.js

QuokkaJS (Integrated Scratchpad for JavaScript)

With this plug-in you can write your code and get immediate results. No need to throw in a bunch of console.log’s in your function and check the output. It has an integration for VSCode (💖💖💖 ), JetBrainsIDE and Atom. I personally have purchased a pro license but for the most part the community version will work just fine.

SonarLint ( Like spellcheck but for your code)

I think all developers should have some sort of code linting tool added into their IDE. This helps review your code in real-time and “should” prevent you from compiling or running code with errors in it. I’ve chosen SonarLint because its given me better results than ES6 linter or some other linting tools that i’ve tried. I’m not saying its the best or that the others don’t work but I haven’t had to configure much other than just installing the extension.

Scratchpad (Built-in Browser Javascript sandbox)

Mozilla also offers a feature in their DevTools that allows you to write, run, and view the output of JavaScript code that can interact with the current web page or not.


I have a background in .NET development and I have fallen in love with Microsoft’s F# Language along with the fsharp community. While going through the documentation I was introduced to a new term, REPL.

REPL stands for: Read-Evaluate-Print-Loop.

“ In a REPL, the user enters one or more expressions (rather than an entire compilation unit) and the REPL evaluates them and displays the results.” -Wikipedia

So I thought, this was cool and I used my Googliness™️ to search for an online version or service and so I came upon THIS.


I don’t need to console.log my output or assign the function to var result = greeting() and then console.log(greeting("Jessica")) . I just write my function, invoke it and BOOM! I get the output without the need of console.log() . supports many languages!

I can even create a Repl, write some code and share the link with someone that can give me feedback, Brilliant!

Coincidentally, uses Monaco, which is the same Editor that VSCode uses.

Finally, for the developer on a budget (including myself) most popular IDEs have a built-in debugger that works the same was as the debugger in Chrome or Firefox.

I’ve kindly added links to How-Tos: For a few IDEs (don’t get mad if your IDE is not listed, I’m looking at you Atom IDE community).

Debugging in VSCode 

Debugging in JetBrains IntelliJ

Debugging in Visual Studio

Final Thoughts

So why have I gone through the long winded trouble of explaining / trying to convince you why you should wean yourself off of using the console.log and embrace debugging tools? How was this suppose to make you feel better / more confident in writing code?

The short answer is: I believe having more information and proper tools “should” make coding, and for that matter debugging, less daunting / scary.

Use of widely available, open-source debugging tools is key to be more familiar with software development and to grow as a developer.

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Where Do Data Scientists Come From?

By Dimitri Antinou

Over the last few blog posts, we’ve answered a lot of questions around Data Science: What is it? What’s the difference from data analytics? Which type of program is right for me? If you’re interested in becoming a data scientist, you might be wondering how other people got into the field. Given how new the profession is, most of today’s practitioners probably didn’t study data science formally as undergraduate or graduate students. So today we’re asking: where do data scientists come from?

Let’s start broadly by defining the possible pathways into this career. If you’re a Data Scientist, you probably followed one or more of these paths:

  • Learning on the job: You ‘did it live’ and hacked your way into a data science skillset.
  • Universities: You studied Data Science, Analytics, Statistics, Programming, or Business formally in a university setting.
  • MOOCs (Massive Open Online Courses): You learned through an online resource like Udemy or Codecademy.
  • On-site or corporate training: You were trained by a learning & development department, internal academy, or contracted provider.
  • Immersive programs/bootcamps: You went to coding bootcamp  and learned Data Science through an immersive, hands-on career accelerator (like Codeup, perhaps?)

Each of these pathways has unique advantages and disadvantages across variables like cost, formal credential, length, and pace. A free online program is free and accessible, but takes a lot of dedication to follow through and is harder to change careers with. A bootcamp specializes in quick and efficient job outcomes, but is a big investment. A university offers a formal degree and dives deeper, but is more expensive and takes longer.

Each of these pathways also leaves gaps against the complete picture of a data scientist. From your training you might be missing components like: working with real data sets, understanding industry and company demands, using up-to-date technologies, or even just knowing what you don’t know! What’s important to understand here is that different pathways yield strengths and gaps. Your job is to find, acknowledge, and improve your gap areas!

Now that we have a framework for understanding potential pathways, let’s look at some data. In preparing to launch Codeup’s immersive Data Science program, we researched over 250 data scientist profiles on LinkedIn and analyzed their educational and career histories. Here’s what we found!


95% have a Bachelor’s Degree, 70% have a Master’s Degree, and 27% have a PhD. Of those degrees, the most represented areas of study are: math, stats, business, engineering, and CS.

Degree's by area of study

Career History

Data Scientists come from an incredibly diverse range of professional backgrounds: psychology research, software development, business analyst, mechanical engineering, and more! We saw a few prominent patterns in our data:

Data Scientist Career History


A third component of our research was to interview practicing Data Scientists. We asked questions like: What was your path to the field? What did you study? Is there a need for programs like Codeup? What are the most important skills to learn? After conducting these interviews, we had three valuable lenses to understand the paths into Data Science: educational histories, career histories, and first-hand qualitative research. From these three, we compiled 5 archetypal Data Scientist personas!


Codeup Data Science Personas

If you see yourself in any of this research, you might be on the path to becoming a Data Scientist! Still have questions? Reach out to us at (210) 802-7289 or! Wondering which of Codeup’s programs is right for you? We’ve got you covered. And of course, if data science gets you excited, get started with us today at!


Data Science VS Data Analytics: What’s The Difference?

By Dimitri Antoniou

A week ago, Codeup launched our immersive Data Science career accelerator! With our first-class kicking off in February and only 25 seats available, we’ve been answering a lot of questions from prospective students. One, in particular, has come up so many times we decided to dedicate a blog post to it. What is the difference between data science and data analytics?

First, let’s define some of our terms! Take a look at this blog to understand what Data Science is. In short, it is a method of turning raw data into action, leading to the desired outcome. Big Data refers to data sets that are large and complex, usually exceeding the capacity of computers and normal processing power to deal with. Machine Learning is the process of ‘learning’ underlying patterns of data in order to automate the extraction of intelligence from that data.



Now, let’s look at the data pipeline that data scientists work through to reach the actionable insights and outcomes we mentioned:

  1. We start by collecting data, which may come from social media channels, network logs, financials, employee records, or more.
  2. We then process that data into usable information stored in databases or streamed.
  3. Next, we look back on the history of that data to summarize, describe, and explain, turning the data into meaningful knowledge. Here we’re primarily using mathematics, statistics, and visualization methods.
  4. Now we convert that knowledge into intelligence, seeking to predict future events so that we can make decisions in the present. This is where practitioners will introduce mathematical/statistical modeling through machine learning to their data.
  5. Finally, we enable action by building automations, running tests, building visualizations, monitoring new data, etc.

Data professionals work at different stages of the spectrum to move data through the pipeline. On the left, Big Data Engineers specialize in collecting, storing, and processing data, getting it from Data to Information. In the middle, analysts work to understand and convert that information to knowledge. Lastly, a Machine Learning Engineer utilizes machine learning algorithms to turn intelligence into action by building automations, visualizations, recommendations, and predictions.

Data Scientists span multiple stages of this pipeline, from information to action. They will spend about 70% of their time wrangling data in the information stage. They will conduct a statistical analysis to derive knowledge. Lastly, they predict future events and build automations using machine learning.

For those technical folk out there, data science is to data engineering or machine learning engineering as full-stack development is to front-end or back-end development. For the non-technical folk, data science is the umbrella term that houses data analytics, machine learning, and other data professions.

So what’s the biggest difference between a data analyst and a data scientist? Data scientists utilize computer programming and machine learning in addition to mathematics and statistics.

Still have questions? Reach out to us. Wondering which of Codeup’s programs is right for you? We’ve got you covered. And of course, if data science gets you excited, get started with us today!

Codeup’s Data Science Career Accelerator is Here!

The rumors are true! The time has arrived. Codeup has officially opened applications to our new Data Science career accelerator, with only 25 seats available! This immersive program is one of a kind in San Antonio, and will help you land a job in Glassdoor’s #1 Best Job in America.

Data Science is a method of providing actionable intelligence from data. The data revolution has hit San Antonio, resulting in an explosion in Data Scientist positions across companies like USAA, Accenture, Booz Allen Hamilton, and HEB. We’ve even seen UTSA invest $70 M for a Cybersecurity Center and School of Data Science. We built a program to specifically meet the growing demands of this industry.

Our program will be 18 weeks long, full-time, hands-on, and project-based. Our curriculum development and instruction is led by Senior Data Scientist, Maggie Giust, who has worked at HEB, Capital Group, and Rackspace, along with input from dozens of practitioners and hiring partners. Students will work with real data sets, realistic problems, and the entire data science pipeline from collection to deployment. They will receive professional development training in resume writing, interviewing, and continuing education to prepare for a smooth transition to the workforce.

We focus on applied data science for immediate impact and ROI in a business, which is how we can back it all up with a 6 month tuition refund guarantee – just like our existing Web Dev program. We’re focusing on Data Science with Python, SQL, and ML, covered in 14 modules: 1) Fundamentals; 2) Applied statistics; 3) SQL; 4) Python; 5) Supervised machine learning – regression; 6) Supervised machine learning – classification; 7) Unsupervised machine learning – clustering; 8) Time series analysis; 9) Anomaly detection; 10) Natural language processing; 11) Distributed machine learning; 12) Advanced topics (deep learning, NoSQL, cloud deployment, etc.); 13) Storytelling with data; and 14) Domain expertise development.

Applications are now open for Codeup’s first Data Science cohort, which will start class on February 4, 2019. Hurry – there are only 25 seats available! To further our mission of cultivating inclusive growth, scholarships will be available to women, minorities, LGBTQIA+ individuals, veterans, first responders, and people relocating to San Antonio.

If you want to learn about joining our program or hiring our graduates, email!

Which Program is Right for Me?

To Web Develop or to Data Science?
That is the question.

With our recent program launch, Codeup now offers two technical career tracks: “Full Stack Web Development – Java” and “Data Science.” If you’re a prospective student, you might be wondering which program is right for you! First, we recommend understanding what data science is and what full-stack web development is. Second, ask yourself the following three questions:


One key difference between our programs is the prerequisite background knowledge. Our web development program doesn’t have any required skills! Some students enter with no tech experience, and others enter with a lot. Having programming experience is always a plus, but not a must. However, Data Science relies on experience in math, statistics, and basic programming for all incoming students. You’ll need concepts like working with matrices, writing Python functions, and solving systems of equations. That means that you either need coursework in those subjects, self-teaching experience, or on the job training.

Your answer to this question isn’t a simple yes/no, but it should help you determine the ramp-up period to one of our programs and which one fits you better now. If you don’t have any math or programming background, web development may be a better fit. If you have a Math or CS degree, data science may be.


Do numbers get you hyped up? Do you love or hate excel? Do you really like programming? Do massive data sets feel intimidating or exciting? Do you enjoy statistics and math? Do you like being visually creative? Do you want to build web applications? Do you want to focus just on technical work or mix technical and business work?

This list isn’t exhaustive, but it should kickstart your thinking to explore your intrinsic interest in the content of our programs. Try to understand what each profession does day-to-day, and then ask yourself: which gets me more excited? And make sure your answer is brutally honest! Our programs have the same structures, and both career paths are in demand with great opportunity. You’re in great shape either way, but you’ll be much happier with the content that makes you happy.


When you graduate from Codeup, we’ll help you land your first job. From the Web Development program, that likely means a job as a software developer, web developer, or programmer. From the Data Science program, that likely means a job as a data scientist, data engineer, or machine learning engineer. But that’s just the first job! As you move through your tech career, you’ll discover new interests and opportunities, like the following.

Web Development: web developer (alternative titles: web designer, UI/UX designer, front-end developer, front-end engineer, full-stack developer, software developer), programming, quality assurance technician, technical sales, product/project manager, etc.

Data Science: data scientist, analysts of all kinds (data, business, risk, fraud, marketing, web, competitive), customer intelligence, business intelligence, data engineer, dashboard/data visualization developer, machine learning engineer, etc.


You now know what data science and full-stack web development are. You have compared your background skills with our program prerequisites. You have thought about what content gets you more excited! And lastly, you’ve considered what future opportunities you’ll want to open for yourself.

Did you decide which program is a better fit? Awesome, congrats! You can apply here and begin your journey to a career you love!

Still not sure? Let us help! Codeup’s mission is to help you launch your career, and or staff is dedicated to helping you find your fit.