Tag Archives: data science

Review of codeup, the most expensive bootcamp in the usa

I graduated from Codeup in June 2019 as part of Ada cohort, the first Data Science cohort. In this article, I will review Codeup, giving pros and cons of the school with some attention to the data science program in particular and also discuss my experience in the program. My opinions will differ from others because of my particular background. I am an artist, a self-taught programmer with less than 1 year of experience with Python (at start of program), and I have a very privileged educational background. I am from San Antonio, TX (where Codeup is located) and was living there before I attended, and I got generous funding to attend the data science bootcamp.

TLDR: Codeup is a great school with many resources and great instructors. I believe the great curriculum, instructors, network and potential for financial aid make the cost worth it.

I first heard about Codeup, a small career accelerator school, because of their web development program. When their data science program was announced, I was very excited. The staff and instructors at Codeup were my first selling point as I was really impressed by their Senior Data Scientist, Maggie Guist, and their Director of Strategic Partnerships, Stephen Salas. Before I applied to the program, I visited the campus and met with Maggie. Like when previously choosing what graduate school to attend, I followed my instincts by seeking out cool instructors and people that I get along with.

The application process was straight forward with small technical tests, short answer questions and longer essay questions for scholarship applications and finally a phone call interview with someone at Codeup. In my case, I spoke with Stephen Salas. I got accepted and during my decision-making process, I had to determine my financial aid situation because I had no money. Codeup recommended a number of financial aid options including government grants, scholarships, and personal loans, which was the worst-case scenario. I wrote about the financial package I got in the previously mentioned blog post.

In addition, I spoke with alumni from the web dev program and learned about the pace of the program and the experience of attending a bootcamp. I was terrified of choosing the wrong thing to study. Would I really like it? Was I giving into corporate, capitalist America? I found that most people I spoke with really liked Codeup and found the whole experience transformative in a positive way.

I accepted and secured my spot in the program and we were given a number of online courses for preparation. I had 2 months to complete them and I went through all the courses carefully. Ultimately, when we started the program it was very slow at first. I was expecting this because of what others told me. The first week of the program were exciting as we focused on understanding the full data science pipeline but then it quickly got boring as we learned the basics of Python. However, I was appreciative of this ‘slow’ time because I really struggled with the 9-5 routine. As the program progressed and we did our first projects with real data, I became somewhat depressed as we were tackling business concepts that I didn’t understand. Everyone else in the class seemed to understand business principles like churn, attrition and customer acquisition and even excel at giving insightful business recommendations based on these and other factors. But having no experience in ‘business’, I struggled with this the most.

So my first critique about the program would be lack of teaching business concepts and I’d recommend more business oriented instruction.

While the program progressed from Excel, Python, basic statistics, and SQL, we moved into the fundamental data science methodologies. This was the meat of the program and what I enjoyed most since we got a good taste of different methods in data science practice. We had regular group projects on each of the methodologies we learned, which was useful and instructive. Each project was followed by a class presentation which was very nerve-wracking. But by the end of the program, it got easier and we all started to excel at this.

The second critique of the program was time since we didn’t have enough to cover some topics and the last few weeks of instruction were very rushed. However, they changed the program length to address this already.

The last two weeks of the program were wholly committed to completing a group capstone project which we were able to design ourselves. But since these were group projects not everyone got to work on a project that they designed. Finally, we presented our findings to a group of employer partners at the very end of the program. Because I am interested in design research and I got to design a project idea that I was passionate about, I found the capstone project proposal process challenging, fun and exciting. At times the process was confusing to some people and maybe even unfair if someone’s project was not chosen. However, the overall experience of working in groups was undeniably valuable and the instructors did a fantastic job creating groups and guiding our capstone projects.

My experience of Codeup was positive overall. The best parts of Codeup were the instructors and staff, the very smart students that I met while attending and the large network of partnerships that they have established over the years. In particular, their relationship with local organizations helped me find enough financial aid to attend, which was incredible. They also offer scholarships that were very useful.

If you have any questions about Codeup or bootcamps in general, I’d love to help if I can and you can email me at jesse.jinna.ruiz@gmail.com.

Tips for Choosing and Funding a Coding Bootcamp

By Jesse Ruiz

Bootcamps or career accelerator programs are short term education programs designed to help you learn new skills and find a job. If you are thinking about attending one, I will share some tips about finding a bootcamp, my story about how I chose to attend Codeup in San Antonio, TX and how I got funding to attend.

My first tip is to spend at least a few months to a year researching the topic you want to study and the bootcamps available. There are tons of resources online to learn programming. I will provide a detailed table below of the courses I took, most of which are free. While you are learning the basics, start to learn about the bootcamps that teach this subject, read through bootcamp curriculum, take notes on tuition costs and start dates and note whether or not they provide scholarships. This first step is crucial for figuring out if this topic is something you are genuinely interested in.

Secondly, when you start researching bootcamps, you will find that cost of tuition can be high. The best strategy is to look simultaneously look for funding and bootcamps. First look locally and seek out local and federal grants to attend based on being under-employed, unemployed or under-represented in the field (minorities). I was only able to find funding because I met with a local career training program which enabled me to access local and Department of Labor funds. If you don’t meet the criteria of being being under-employed, unemployed or under-represented in the field, then don’t worry! There are still other scholarships and loans out there.

Warning!—only start to contact/call up the bootcamps when you are comfortable with your basic skills in programming (or whatever you are trying to learn) and when you are committed to attending. Bootcamp admissions will aggressively seek you out. They want you to attend their courses. You should have clear intentions about what you want to do, how much money you want to spend, and how good you are at programming. Just be honest with the people you speak to about your circumstances. This is a process so take your time. Often, if you get rejected from a bootcamp, you can still re-apply later.

Lastly, there are almost always loan companies that specialize in loan for students of bootcamps. If the cost of tuition is still prohibitive, you can consider loans as your last option. In most cases, these loans can be repaid easily with the job you will (hopefully, most likely) get after you graduate. Some bootcamps offer refunds if you don’t get a job (with conditions) and others offer deferred tuition where you don’t pay anything until you get your first job.

As for my experience, I learned about Data Science online and spent 10 months researching the subject and bootcamps. I took a slew of courses online to learn the basics, which I will share below. Then I started to apply to bootcamps. Ultimately, I was able to find Codeup in my hometown. I visited their campus and spoke with their admissions representative about funding. I loved that this school was in my hometown, so it was a practical choice for a full-time program. I also liked the instructors and admissions people that I met. The admissions person told me about their funding options and sent me to a local career training program, which informed me about local and federal grants that were not easily accessible online. Working with this local program was long and uncertain but I stuck with it. The real reasons I was able to get funding through them were because I had been under-employed for years, I had used up all my savings, I was living at home with family and I was unemployed at the time that I applied for the funding. In the end, I chose Codeup because I was able to find funding, it was in my hometown and I genuinely liked the people I met there, especially Maggie Giust, the Senior Data Scientist.

I will share a table of the exact funding amounts that I got below. This will probably not be the norm. I got extremely lucky with my funding.

All in all, this whole process is precarious, scary and hard. You should give yourself plenty of time to research and learn about the process, the bootcamps and the subject you are trying to study.

If you need any advice, please feel free to contact me directly. And if this was helpful please send it along to anyone you think would benefit from it.

List of resources for researching bootcamps



List of the Courses I Took (In order that I took them)

Name of Course Notes Difficulty/My Critique & Experience Link to course
1. Data Science & Analytics Career Paths & Certifications: First Steps with Jungwoo Ryoo **Requires sign in. By pass by using local library access or your university’s access, i.e. “Sign in with your organization’s portal”   Easy Lynda.com (search title after you sign in)
2.Statistics Foundations with Eddie Davila Same as above Easy/Medium Lynda.com (search title after you sign in)
3. Excel 2016 Essential Training with Dennis Taylor Same as above Easy, Run through videos at 2X speed Lynda.com (search title after you sign in)
4. A Gentle Introduction to Programming Using Python Utilized Python 2. Required setting up Python environment on your computer. Medium, Very fast paced. Not a good idea to learn Python 2. Stopped course halfway MIT 6.189 OCW
5. Learning Path: Becoming a User Experience Designer This is a group of courses meant to teach UX. **Requires sign in. By pass by using local library access or your university’s access, i.e. “Sign in with your organization’s portal” Medium. Mostly lectures. Lynda.com (search title after you sign in)
6. Python Tutorial Took a couple of days to complete. Sign up for free; doesn’t require setting up an environment on your computer Easiest, short exercises. Mode Analytics
7. Learn Python 2 Sign up for free; lots of exercises; doesn’t require setting up an environment on your computer. Easy/Medium; took about a week to complete Codecademy Python
8. Data Structures Fundamentals Enroll for free on EdX, self-paced Medium/Hard; Didn’t understand most of it; stopped halfway. EdX UCSD Data Structures Fundamentals
9. Introduction to Algorithms MITX Enroll for free. Video lectures and HW assignments Hard. Stopped after 5 lectures. MIT 6.006 OCW
10. Statistics and Probability Khan Academy Join for free. Very robust website with quizzes and video lectures. Easy/Medium; Spent about 4 weeks on it, slowly. One of my fav. sites. Khan Academy Stats and Prob
11. Linear Algebra Khan Academy Join for free. Very robust website with quizzes and video lectures. Easy/Medium; Spent about 2 weeks on it, slowly. Khan Academy Linear Algebra
12. Introduction to JavaScript: Drawing and Animation Join for free. Very robust website with quizzes and video lectures. Easy/Medium; Spent about 2 weeks on it, slowly. Khan Academy Intro To JS
13. Data Science Math Skills, Duke University Join for free. Audit courses for free. Some times you can get stuck when they ask you to pay in order to submit quizzes. If this happens to you, just skip the quizzes or sign up for a “free trial” and cancel before you are charged. Easy, work through exercises slowly. Spent about 1 week on it. Coursera, Data Science Math Skills, Duke U
14. Linear Algebra for Machine Learning, Imperial College London Same as above Easy/Medium; Spent about 2 weeks on it. Didn’t learn the page rank assignment because of the pay wall. Coursera, Linear Algebra for Machine Learning, Imperial College London
15. Basic Statistics, University of Amsterdam Same as above Easy/Medium; Spent about 2 weeks on it. Made a new account so that I could get a ‘free trial’ to submit quizzes. Slowly did all work. Coursera, Basic Statistics, University of Amsterdam

Other courses I dabbled in and other resources:

Basic HTML and HTML5 and CSS, FreeCodeCamp.org

The Open Source Data Science Masters, Created by Clare Corthell, http://datasciencemasters.org/

List of 5-Day Data Challenges, Kaggle, https://www.kaggle.com/rtatman/list-of-5-day-challenges/

Siraj Raval, How-To Videos and Curriculum on Github and Youtube, https://github.com/llSourcell/Learn_Data_Science_in_3_Months

Update: As of October 2019, I have learned that Project Quest no longer has funds from the Department of Labor, which was the majority of my funding. Please contact Project Quest directly for more information about the resources that they currently provide.