Another Obligatory Post-Bootcamp Summary

As I have mentioned in a previous article found [here], I have been attending a successful bootcamp to receive a formal data science education. Recently, I have finally reached a milestone which had initially seemed impossible to accomplish — before and during my studies. I am proud to say that I have graduated General Assembly Data Science Immersive camp, the nationally notorious bootcamp able to prepare students for data science industry within three months, consisting of disciplined training and constant professional guidance. With General Assembly, I was able to touch upon many subfields of focus in data science as well as experiment with different tools used in industry today. This ranges from working with natural language processing procedures, to popular Python libraries, to Amazon Web Services, to neural networks.

A strong focus for the program had been placed on understanding the statistical fundamentals in mathematics — something requiring constant practice and improvement on my end — while another focus for the program had been on working collaboratively with peers. This ultimate focus was something stressed within the course, especially with having to creatively navigate over the hurdle of the social distancing policies resulting from the Covid-19 pandemic. The entire program originally was intended to be hosted in Manhattan, near the Flatiron district, at the official General Assembly campus. Unfortunately, the pandemic’s effects had halted any plans for travel, as strict social distancing policies were in place. For the safety of the staff and students, we were left with no other option but to continue our education entirely through a remote environment, by communicating consistently through the Slack and Zoom platforms. I believe this is the first time General Assembly had ever conducted a full-time remote data science cohort in this manner — where students were tasked with learning through live online lectures for 8 hours a day, 4 days a week (the fifth working day of every week only consisted of 3–4 hour class requirement). From such experience, I am glad to state that the entire program appeared to be a success, where my peers were able to perform and excel beyond standards across quizzes, labs, and projects. It was a little upsetting to discover that I was not going to meet some of these individuals in person throughout the course, but this did not stop us from successfully communicating and establishing a good camaraderie. I would even argue that working remotely in this manner has bettered our communication skills and work ethic, as all communication relied heavily on delivering well thought out instant messages at reasonable hours. The experience felt very close to a normal work environment, where meetings were constantly scheduled, problems were constantly shared openly for discussion, and ideas were constantly acknowledged and pursued to achieve a collaborative goal. General Assembly was a great school in that it forced students to be held accountable for their actions and idleness; it forced students to learn how to manage projects and delegate work appropriately; it forced students to know when and where compromise was needed.

The Hurdles

Many hurdles existed throughout my studies where the majority of them were mental. For the most part, a student was spending an average of 10–12 hours a day on a computer to just meet the requirements for the cohort. Not much room was left for failure across the bootcamp’s intense demands as all failed work must be made up for within a certain timeframe and complete failure in almost any requirement yielded the inability to graduate from the cohort. To express simply…the bootcamp was very challenging. However, as stated previously, the largest hurdle to accomplish was poor mentality. Much of my time was spent understanding common computer science thought and practice. It took me a while to correctly use for loops in my code where necessary and know how to store information gained from for loops in particular. For loops are relatively simple concepts to any computer scientist, but struggling on this as well as managing Python dictionaries led me to believe I wasn’t going to survive the course. The end result was a lot of pessimism and consistently thinking “what if I cannot finish my work in time?” In one way, I justify this pessimism as realism by analyzing what work needs to get done and comparing my work rate to the time left over. Couple this mentality with the previously expressed notion that I was staring at a computer all day long — not getting enough sunlight — and now I had the recipe for a mental disaster. As I look back at it, I cannot find any other way to really get through the program. The workload was immense and required a very large time sacrifice; excessive and overbearing optimism did not seem like the appropriate mental state to have when trying to utilize an intense focus on work for many hours in the day (not to also mention it was hard to find such optimism at certain points). The saying from Stephen King’s, The Shining, puts the end effect into great perspective where “all work and no play makes Jack a dull boy.”

Fast forward towards the end of the cohort where all of the above predominantly played a role in my life for the last 3 months. Through it all, I had sweat, grit, migraines, and ultimately satisfaction. It was honestly amazing to see how far I have come and what problems I was facing at this point rather than earlier. I initially had relied on a lot of outside help, but saw that the frequency for which I was asking for help from others who were either in industry or who practiced Python and data science as a hobby had shrunken dramatically. The problems which I faced were unique to myself as I continued to seek higher level answers. My worries on technical inefficiencies (like writing bad for loops) had been mitigated as I began to abstract my work for general use cases; and dictionary worries…well you should observe the mess of dictionaries I had to deal with in my [ongoing capstone project], which served as great dictionary practice. Regardless of specifics, the commitment to perform to standards by a constant deadlines was over and the feeling of accomplishment post program was overwhelmingly positive.

Future Work

With school over, the next steps to make are to apply for jobs in industry reflecting my own interests. To start, I know that whatever I am hoping to be a part of should involve a fair bit of machine learning. The beauty of data science is that it allows somebody to be adaptive to different roles. However, a lack of focus can easily be detrimental to any data scientist if an employer were to believe somebody does not have their heart aligned with a potential role or mission of the company. To help prepare myself for adaptability in ML, I also will be coupling personal projects to showcase to employers which will involve various uses of machine learning. Finally, I also intend to continue writing about my work. Writing blogs to continue a digital presence helps me gain traction with people who do not completely understand a project I am working on or are simply looking for a more put together format of a project rather than navigating through spaghetti of code.

Advice for Future Students with General Assembly

- You’ll figure it out. Whatever it is you are stuck on, you will figure it out. You’ll learn that something will work out for you or you will have to use something else to solve your problem. Just remember that it’s not out of your reach.

- Ask for help. People are eager to problem solve. Many computer scientists enjoy paying it forward when it comes to being stuck at coding. Instructors are always more than happy to provide a relatively simple explanation to whatever you may be stuck on within the course.

- Stick to a routine and include breaks in that routine. You’ll need these breaks and the you’ll enjoy the discipline you gain from the routine.

- Generalize and abstract. If you are repeating work consistently in your course, consider taking the time to create a function for one of your projects which you can

- Use therapy. General Assembly offers counselors to professional speak with to help you navigate through your outside issues playing a role in your studies.

- Be proud of what you’ve accomplished. A man settled when he cherishes what he has, but a man lives when he cherishes what he’s earned.

- Be calm. It’s not over if you fail to graduate. Employers will be more inclined to see you talk about impressive projects rather than knowing you achieved a certificate of completion. The presence of passion outweighs merit.

Finally, recall that sticking with it is worth it. The benefits will ultimately outweigh the time sacrifice put in. These will be skills you will carry for a lifetime. To sound off in the words of John Locke, from the hit TV show known as Lost, “Struggle is nature’s way of strengthening.”

Data Scientist | Mechanical Engineer | EIT