An Underdog’s First Day of Data Science Bootcamp at General Assembly.

David Lee
5 min readJul 21, 2020

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Icebreakers, virtual work environment setups, expectations, and graduation requirements are definitely helping me with the reality that I’m a student again.

Pre-course work.. and where I stand.

Prior to day 1, after passing a candidate screening in April, I had a tech setup guideline along with a weeks workload of Data Science pre-course material to complete.

Note: I won’t provide the details on the screening process, unless explicitly given permission from General Assembly.

Here are some of the main pre-course topics:

  • An intro to Data Science along with its use case
  • Math: Statistics / Probability / Linear Algebra
  • Python: The most popular Language for Data Science followed by R
  • Version Control: Using the terminal(bash) to set up Git and Github
  • A timed Assessment test

The baseline is me. This is for anyone reading who may be intimidated to applying to a bootcamp due to lack of credentials:

So how good am I?

“I didn’t know almost any of these in April”

Back in 2001, when I had taken my SATs and tried my hand at an Industrial Design degree at Pratt Institute for college, my Math skills were sharp and I had received a score of 740 out of 800 for the math section on my SATs. At the time my score placed in the 98th percentile. Although I had a good score nearly two decades ago, I had never taken a College level math course as it didn’t apply to my degree, which I unfortunately didn’t complete.

If I were to make a table and rate my experience levels for each of these topics, on a scale of 0 to 10, it would look something like this:

Math:
Statistics | 1/10
Probability | 2/10
Calculus | 0/10
Linear Algebra | 0/10
Version Control:
Git | 1/10
Github | 2/10
Programming:
Python | 0/10
Data Science | 3/10
* I had to learn the topic of Data Science to show some competiancy during the prescreen process, which explains a higher score of 3/10.

The caveat was that after a week of learning the basics, I’d have to face an assessment test that would determine whether I would be awarded an income share agreement. If you missed my last post, I go in some detail on it. Basically, I had to learn a great deal with little time to spare.

I wouldn’t be writing an article now if I didn’t get into the Catalyst program(income share agreement) due to today’s economic uncertainties.

Here are my test results:

I thought I failed, but I ended up doing fairly well considering being in new territory.

Although I knew some of the answers to the questions, I had a lot of difficulty understanding the why and how for many concepts in the list above.

“I based the next three months of my training around this idea to build a good foundation to prepare me for today.”

The actions I committed to from April to today are pretty well laid out on my previous post conclusion section for anyone new reading this.

Day 1 is about setting up for an awesome day 2 and beyond:

My dad, building the foundation to something much greater.

As one would expect, getting ready for a new class requires for a seamless working environment for learning to happen. It turns out that my cohort(classroom) is the largest DSI class since the program’s inception. I initially had concerns about interruptions and tech issues that would slow the pace of the classroom, but I’m quite impressed with how tech issues are resolved due to best practices trainings, and communication rules that were established.

I’m familiar with both Zoom and Slack, as I have already created both integrations in an organization previously, but by having a few instructors to troubleshoot technical difficulties in separate Zoom breakout sessions helped so much in maintaining a constant flow of information. Our 10am-6:30pm Zoom call rarely had weird noises in the background, nor people speaking over each other. Great!

Within that time, we got a lot done. We were able to meet our lead and associate instructors, career coach, and student support team. We learned the ins and outs of using our communication tools. We reviewed the requirements and expectations of the class along with the types of projects we could expect at the end of the course. Plagiarism ethics, social strategies, and support channels were also introduced. The rest of the day was a more in-depth introduction to the command line to get the cohort on the same page.

I knew before the class that Macs are generally more popular among programmers and noticed a good mix of Mac and Windows users in our class room. I also researched beforehand that many corporations, large datasets, and machine learning computations are performed in Linux devices. I’ve set my desk up to have both machines side by side and actually decided on this set up to learn the differences in each operating system (OS). We reviewed some of the basic material from our assignments before class, and also completed our repository setups (git and github) with Secure Shell Protocol (SSH) keys for easy connections to our online profiles, without the need to reenter passwords for every change. After the SSH connections to our Github Enterprise connections were set up, the day came to a close, but not for me.

My strategy and what really helped me:

My strategy comes down to differentiation. When we briefly introduced ourselves to each other, my expectations of having a room full of really smart people were realized. I noticed many new graduates from universities, data related majors, and math intensive professionals were with me(a sales guy with no degree). The extensive prep work leading towards today helped me understand nearly everything that was done on the terminal, along with setting up the repositories. I have done this before when I was setting up my Wireguard VPN install on my Ubuntu 20.04 LTS Virtual Private Server(VPS) on my Linux Fedora 32 box here. The problems I faced during that installation helped me so much in understanding the ideas behind SSH Keys. I went ahead and setup SSH connections to both my Enterprise & Personal Github account on both my Mac and Linux machines to extra practice. This will also allow me to work on the same projects seamlessly between devices. My mac obviously being my portable device and my Linux device performing all of the heavy computations, mirroring what I can expect in a large company. To my knowledge, I’m the only one that is taking this course with a Linux approach.

I’m hoping that learning Linux, and understanding the nuances between both of my operating systems, and my progress that I share on my blog will help in my marketability after graduation. For now, I think I’m ready for the morning, and I’m hoping to share the more technical aspects to Data Science that I learn each week.

Last thought:

Its really nice to see so many faces at once since the world has been on lock down for a while now.

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David Lee