I started my journey into Data Science through General Assembly’s Data Science Immersive course, a 12-week program that trains students on the latest in data science, machine learning, data visualization, and data wrangling.
After spending eight years as a mechanical engineer, I learned quickly the value of distilling down complicated systems to understandable and useable information. Once I felt that the role of an engineer was no longer the direction I thought I could be the most impactful, I knew I needed to explore new areas.
After some dramatic and sudden life changes, I found myself at a crossroads and with the opportunity to go in a substantially different direction. That direction was data science. General Assembly (GA) became my avenue to explore in detail the data science realm I had always briefly touched on in my previous roles. I made the commitment to join the program in early July and was ready to start by the end of the month.
There was some significant anxiety on my part jumping back into what I perceived as intense coding, something I had not done heavily since my time as a researcher during my undergrad at UT. Fortunately, GA provided a solid pre-work package to prepare me for the class as well as great instruction over Python during the first week. By the third week, I was coding like a pro, line after line, without even blinking an eye.
I found myself with an interesting group of new data scientist with an incredible array of backgrounds and personalities. With our local instructor J at the helm, we set off on a journey of lab work, eight lectures a week, and intense projects crammed into a truly immersive experience. I naturally gravitate into my default nature of helping out the other students when I can. If I could teach, then I understood the material myself. If we all did well, it would reflect well on the program and we could all collectively push each other to be better.
At the end of the course, I will be well prepared across all of the following topics in Data Science:
- Frequentist and Bayesian Statistics
- Exploratory Data Analysis
- Supervised Learning
- Web Scraping, APIs, and NLP
- Correlated and Spatial Data Analysis
- Unsupervised Learning
- Neural Networks
- Big Data
For more details about my projects, head over to my Github page to learn more.