My Review of the UT Austin Data Analysis & Visualization Bootcamp

*originally published February 18, 2020 on my LinkedIn

I just completed the 6 month part time Data Analysis & Visualization Bootcamp and it was a total adrenaline rush the whole time! I really enjoyed my interactions with the instructor, the TAs, and the material we learned in just 6 months. The curriculum was quite broad, ranging from basic Excel skills to introductory Python to a deeper overview of machine learning than I expected (this is dependent on the instructor and the program as they update their materials over time). I really enjoyed learning all that I did and the homework assignments and group projects were a fantastic way to really cut your teeth on the material (that I spent many nights staying up late for merely because I could not let something go before going to bed). The bootcamp is a great way to broaden your skills should you go in with a clear goal in mind.

What do you learn?

I would split the class into 3 parts as it currently is:

1. Basic Python & SQL — learning basic Python skills, data analysis and visualization using pandas, APIs, web scraping, etc. Some SQL training using Docker and Postgres (you may use MySQL depending on instructor). I will also lump Excel in here since it was a pretty short section right at the beginning.
2. Basic web dev — you learn HTML/CSS/Javascript in this section. Enough to put up visualizations on a webpage and how to set up a basic website/application but not as much as the Coding Bootcamp (i.e. Web Dev Bootcamp) of course. This section is a bit weirder to people as it’s not so much analysis as it is learning the web dev skills to put up your analysis on a webpage which can get people lost sometimes because it is so different from the previous section of the course.
3. Advanced topics — this one seems to vary a lot from class to class. My class focused on basic machine learning skills (i.e. supervised learning, regression, AWS, NLP, Spark, etc.) and a bit of Tableau and R (not much R compared to Python though). Can get very advanced if you have had no experience with data science topics. It’s amazing what my cohorts learned and created by the end of it though.

What are the program’s strengths?

• In person classes. Sure, you had to show up 3 times a week and be in class but it was very helpful to go over exercises with classmates and TAs learning alongside with you face-to-face. I personally think it is more motivating too because you have to actually be somewhere and learn for a structured amount of time. (Note: at the time when I took the class, the online option did not exist but it does at the time of this review)
• Networking. Due to the face-to-face nature of the classes, you get to interact with people that may be working in the field already and get to know them. It is a 6 month long class after all and getting to know people over that time period makes for a stronger connection than online classes with Slack/message boards or someone you might meet at a meetup once a month or so. Definitely easy to take advantage of and possibly get referrals or refer them yourself as I did.
• Diverse topics. Before the the bootcamp, I kept getting rejected from jobs because my skill set was not broad enough and I had to keep saying no whenever an interviewer asked me if I had any experience in “X” technology. The bootcamp filled in pretty much exactly what they were asking for while I was searching and I could finally stop saying no.
• Portfolio creation. The homework assignments are like mini-projects and the group projects take a couple weeks plus they give you experience with working on a team. This made my Github go from nearly empty aside from some training exercises to something I could show off to an employer or give a talk for and something I was comfortable with someone going through it to see what I am now capable of.
• Part time classes. Did not have to give up a paycheck while taking the course.

What are the program’s weaknesses?

• Very structured and fast paced. This is true of all bootcamp programs. It may be part time but you still have to make time for it after work. It’s not as flexible as some online program you can drop in and out of at your own convenience and pace. If you miss even 2 classes in a row without reviewing them, you are very far behind due to the amount of material you cover (you don’t need to master every single little thing of course but some skills compound on each other). So be sure you have the time to dedicate 6 months of effort for this program.
• Hard to keep up if you do not have some technical experience or inclination. Sure, it’s possible to start from nothing and build yourself up but it’s much harder and I think people who didn’t have that background were more likely to get less out of the class. I think the class works better as a crash course or multiplier of skills rather than a gentle introduction to them (but you are supposed to feel some uncomfortableness when learning something new anyway so that is natural). The initial skills assessment test is too easy to pass. I would suggest at least being comfortable with certain prerequisite skills before starting the course (outlined below) in order to maximize your chances of success (success being defined as being employer ready).
• Costs. Not really a weakness per se but something to consider. All bootcamps cost a pretty penny, especially the in-person ones. Although it is much cheaper than other in-person bootcamps in the Austin area which is nice to know.
• Not enough time spent on visualization analysis. A lot of programming that can get heavy at times but we never really compared what types of visualizations are appropriate for what and that would be a more helpful section than something like the VBA scripting in my opinion.

Prerequisite skills to learn

You can learn these skills on the fly during class but it will reduce a lot of frustration if you play around and become familiar with them before the program. I really do think you can learn it in the month before the program starts honestly.

• Moving around in a terminal. You don’t need to write full on scripts but moving from folder to folder and knowing what simple shell commands do would help a lot. If you’ve only ever used a GUI and have never touched a terminal before, you’re going to be have to ramp up faster than usual after the class starts.
• Git/Github. Just enough to know how to add, commit, pull, merge. You will have some class time to learn it though but it will be less intimidating if you learn basics first. Also, being comfortable with the terminal goes hand in hand with this.
• Basic programming skills. Do you know how to create a loop or if statements? Are you familiar with classes? You get to do some of it in the Python course but it helps to understand the underlying principles so that you are confident you can do it in some other language like Javascript too (because you will end up doing that).
• Basic SQL skills. Just be able to do basic queries and how a SQL table is structured. Helps to be able to think in terms of SQL when working with data.

Job assistance

The program offers resume reviews and interview prep. I can’t comment on this too much though since I got a new job halfway through the program and stopped using it at that point. I did do a few workshops at the start of the program though and they did seem helpful and there were people from across different cohorts all over the country and at different stages (pre-bootcamp, midway, already graduated, etc.). The coaches gave helpful feedback and seemed to be open to reach out to for help but it was hard to make time during the program due to time constraints so it may just be a better thing to pick up after it in my view. I think it needs to be pushed more though. It felt a lot more like some side thing from Trilogy as opposed to the very attentive university itself. This is a crucial part though because the hardest part of building yourself up for a new job isn’t going through code errors or making a machine learning model work. At least you get guaranteed feedback for those skills, which is not always so true when job hunting. It is to know how to speak to employers and pitch your value, to know how to network for opportunities, and generally how the application process works (or doesn’t work) in the first place.

What is the class structure like?

The class meets two times during the week and once on the weekend. There are usually two classes (Monday/Wednesday and Tuesday/Thursday) that then combine in the Saturday class. Each class you get to go through activities with the instructor and then do a student exercise right after that to practice on your own or with a partner and ask each other or a TA questions as you try to figure it out. It’s a very active learning environment. The lectures are accessible so that you can review them later and you have access to the activities anytime. You have homework to submit each week reviewing the skills you learned and a TA will review and grade them. There are 3 group projects in the course: a Python data analysis, a web application data analysis, and a final project that involves machine learning. Each are about 2 weeks long and you get to present them in class to show off your work and practice how you present them to possible employers.

Was the class worth it?

Yes, as long as you have a clear goal in mind and do enough research to know what the bootcamp actually offers and that it matches your goals. It’s not a quick, overnight path from zero to being a data scientist but it can help you get started on that path while making you useful as a data analyst (which is just as well since data scientist is not an entry level role at all). It is first and foremost a data analysis class (hence its title). I had a couple years of data analysis/ETL development going in and wanted a fast way to build up my skills with certain technologies (because I was spinning in circles for a long time) and a usable portfolio which is exactly what I got. Make sure you can answer the “why?” question clearly before going in and it will help you have a better chance at succeeding at your goals.

Originally published at https://www.linkedin.com.