Since the beginning of this year, I’ve been working towards a career change into data science. As I’ve made my way through my data science bootcamp, my blogs have documented the reasons I chose to pursue data science, how I’ve managed a bootcamp among other things in life, and how my background in public health is an avenue I hope to explore as a data scientist.
Now that we’re transitioning into the fall season, I have the end of my bootcamp to look forward to, in addition to the holidays. Instead of new curriculum material to work through, I’ll be developing and working on a capstone project. My goal is to work with health-related data and continue that theme from my last two portfolio projects. For now, as I approach the capstone project, I wanted to reflect on some things I’ve learned, things I’ve found most challenging, and what I plan to work on to land my first job as a data scientist.
What I’ve learned:
Thankfully, I’ve been able to learn a thing or two about data science- what it encompasses and how it’s still developing. However, there’s a lot I don’t know. In other words, it is nearly impossible to know everything, and know everything well. One of the most important things I’ve taken away from my bootcamp so far is how to work through something I’m not familiar with (which is the entirety of this bootcamp). Searching online, asking my peers and instructors, and reading blogs/forums have become second nature. More times than not, I can’t dive into all the details of a model or a concept because of time constraints and at the pace the bootcamp is moving. I’ve also never been challenged at this level to learn and understand so many concepts in a time span as short as this. Therefore, I’ve had to be comfortable asking questions and searching for help when I need it. More specifically, asking the right questions and knowing where to look.
Working efficiently is also something I’ve learned to be better at. In the beginning, I was trying to remember everything. Since there was so much new information, I wanted to absorb as much I could. However, I quickly realized that it was impossible to memorize everything. I knew it was impossible, but the studious person inside me really wanted to try their best. Instead, the bootcamp showed me the plethora of resources out there. Again, the best thing I could do for myself was to know where to look. If I understood the concepts and resources, then I could compile that and know the workflow I need to take when working through my projects. Of all the times I ran into something I couldn’t do or understand, the help I needed was out there. Being organized and understanding the steps I needed to take helped me make more use of my time rather than dwelling on my shortcomings.
What I’ve found the most challenging:
Similarly, the lessons I’ve learned throughout the bootcamp so far has stemmed from the most challenging parts I’ve had to work through. Due to the complexity and nature of data science, it always felt as if there was more I could learn or incorporate into my projects. Knowing when to move on was difficult because I knew there was something I could continue to improve with each iteration or step in a project. In addition, taking the feedback from each project and working to refine those aspects in the following project was easier said than done. Particularly, taking my results and linking it back to the business problem and giving actionable items to the stakeholder was something I’ve never had to do. Since each project consisted of a different business problem, it was a component I tried to continually improve.
Besides learning the various concepts, time management was definitely challenging. Balancing work, bootcamp, and a personal life (a lot of times all in the same space) was something I underestimated. I knew that it would be difficult based on first-hand experiences from a few friends, but there were many times where I thought, “oh, this should take me maybe one or two hours to work through,” and it ended up being closer to four hours instead. There were a lot of unpredictable moments where it took me longer to grasp something but then other times, it would simply click. Learning to ride those waves at first was not easy, but I’ve learned to accept things for what they are and continue to work through them the best that I can. I believe my experience of balancing the bootcamp amongst other parts of my life will only contribute to my success in the data science field.
What I’ll be working on:
With about three months left of my bootcamp, I felt it was time I start preparing for post-bootcamp. In my case, it’s trying to land a data scientist/analyst role. Some of these tips I’ve seen across many blogs, discussed within my bootcamp, and recommended by close friends and mentors fo mine. The first is building my portfolio and resume. By the end of the bootcamp, I will have completed a total of 5 projects. My goal is to tweak each project to the best of my ability from the feedback I’ve received. Since I don’t have any data science work experience, being able to talk about my projects thoroughly will be a major component during the interview process.
As mentioned in my last blog, I hope to stay within the health field. I plan to cater my capstone project to encompass health-related data analysis, such as two of my other projects, in order to hone in on not only my interests but also the conceptual understanding from my public health background. In addition to these blogs I’ve published so far, writing more technical blogs about my projects will help me concisely present my work as well. Of course, highlighting these projects and the technical skills will be vital to my resume. I’ve found many helpful templates, and I’ve started asking friends in tech for feedback on my resume.
Lastly, a major component of the job searching process is preparing for the technical interviews. Due to my previous work experience, I’ve had many interviews about my soft skills, but I’ve never worked in a technical role, which means I’ve never been asked to display technical skills during an interview. Two resources that I started using recently are LeetCode and codecademy. Between these two websites, you can find algorithm questions, interview practice, mini courses on specific topics, and other useful resources. I’ve started brushing up on my coding skills, specifically on codecademy because of the way it’s structured, I can’t move on to the next step in a coding problem until I complete the step prior. It helps me breakdown the thought process of each problem, and it helps me pinpoint my weaknesses or concepts I should review in the bootcamp curriculum.
With the knowledge and skills I’ve been able to obtain from my bootcamp, the transition into data science has become less daunting and feels more obtainable as each week passes. I know with three months left, I’ll continue to learn and improve. However until I sat down to write this blog, I didn’t think about how much I’ve already gained from this experience. I knew there was a lot to grasp, and I would be lying if I said I never had any doubts, but I quickly realized that data science encompasses so much. There will always be something to work on and learn even after my first data science role.