What is Codeup’s Application Process?

Codeup's application process

Curious about the application process? Wondering why you need to apply so far in advance? You’ve come to the right blog post! From pre-work to technical assessments, to financial aid and interviews, there are a number of reasons you need to apply 1-2 months ahead of your start date. To explain Codeup’s application process, let’s start by working backward.


Web Development & Data Science

Note: The tilde (squiggly) means “approximately”.


Pre-work (~1 month)

After being accepted, every incoming student is given a set of pre-work to do to familiarize themselves with some coding basics. You won’t be expected to have everything memorized, but some exposure will be required for you to hit the ground running on your first week of class. Many have likened our bootcamp to drinking out of a firehose- there’s a lot of ground to cover in 5 months! Completing the pre-work is the best gift you can give yourself in order to soften the blow. Take the start date you are considering, and subtract a month. You should be accepted by that date in order to finish the prework. 


Tuition Planning (~1 month, concurrent with pre-work)

Once you’re accepted, you’ll meet with our Financial Aid and Enrollment Manager to discuss your financial aid options. She’s also Codeup’s School Certifying Official for VA Benefits, which take roughly 2 weeks to be processed. Scholarships are awarded about 2 weeks before the first class day, so you will need to have applied for any scholarships before then. Grants can range from 2-8 weeks to be processed and secured, but usually take about a month. You will need to be accepted before applying for grants. The tuition process is finalized in the month leading up to the start date, while you’re also working on prework.


Behavioral Interview (~30 min)

Think of this as a casual job interview. We want to make sure you are culturally and behaviorally cut out for Codeup. The intensity and rigor is not for everyone. In this step of the application process, we’re looking for motivated, positive, hard-working, and dedicated character traits. Bonus points if you read our blog post about tips for nailing a video interview!  


Technical Assessments (~1-2 weeks for Web Development, ~1-4 weeks for Data Science)

For Web Development, we will give a couple of technical assessments to test your problem-solving skills. They are not necessarily pass/fail tests, but rather, they help us see how we can best prepare you for class. Those who do not do well on the first challenge have additional opportunities to develop their problem-solving skills. We want to be sure you are set up for success in class! 

For Data Science, the technical assessments may take much longer as there are more prerequisites, but it varies from person to person. We require a basic level of Python, math, and stats prior to being accepted. How well versed you are in those subjects determines how long this process takes. Could be a week if you were a math major in college, could be months if you’ve never heard of Applied Statistics. Plenty of our students knew nothing about stats or Python before applying but worked through our recommended resources (tutorials and workshops) in order to get to the level they need to be on. Don’t count yourself out! But do be realistic about how long it will take you to learn. If you were to apply for our Data Science program a couple weeks before class starts, knowing nothing about Python or stats, our Admissions Manager will be happy to provide all the necessary resources you need for a future start date, but you may not be qualified in time for the next class start, considering you’d also have to finish pre-work in that limited time. 

For both programs, we have weekly study sessions with our Teaching Assistants to help you with any material that’s causing you trouble!


Campus Visit (~1 hour, same week as application)

We may be virtual right now, but we’re still doing virtual campus visits! You can think of this as a program overview. We’ll discuss the admissions process, the curriculum, the schedule, how things are looking now that we’re remote, and any questions you might have.


Phone Call (~15-30 minutes, same week as application)

You can think of this as an initial admissions consultation or phone interview, but really we’re just making sure it’s the right fit! Some people think applying for Codeup means getting paid to learn to code, some people think we teach medical coding. This call is just to introduce ourselves, make sure you’re crystal clear on what Codeup is (a full-time career accelerator that trains and places data scientists and software developers), and to answer any questions you might have.


Application (<5 minutes)

The first step in your application process is your application! It’s basically just sending us your name and contact info so we can get the ball rolling on making you a Data Scientist or Software Developer! How exciting!

Now, are you pumped to begin your own application process and experience all this in action? You can fill out your super quick application here. Our (helpful and friendly) Admissions team will be in touch ASAP to help you change careers. Even if you’re not sure if Codeup is right for you, we can help you work through your doubts and hesitancies in your initial phone call.


With an Admissions Manager as your guide, our Teaching Assistants as your personal tutors, and our Financial Aid and Enrollment Manager as your tuition planning assistant, you’ll never have to go at this alone! Apply now!

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.