Kathryn Bryant recently landed a Senior Data Scientist job at National Grid. When she decided to change her career from academia, she didn’t want to invest too much time in learning new skills. She found NYC Data Science Academy fit her needs perfectly. The program offers everything that would get her up to speed on the coding, big data and machine learning side, as well as connect her to companies hiring data scientists. The next April Cohort for NYCDSA is closing on soon. Apply by March 23rd to see if you can create your version of Kathryn’s journey below:
Could you fill us in on your educational and career background before you decided to learn data science?
I have two degrees from Northern Arizona University: A BA in Spanish and a BS in Mathematics. I continued my study of math at the graduate level at Bryn Mawr College where I earned my Masters and then went on to earn my Ph.D. with a specialization in topography. I graduated in 2016 and landed a job in academia at Colorado College as a visiting professor.
Why did you decide to enroll in the NYC Data Science Academy bootcamp?
I heard about data science in college and was interested in shifting my career in that direction. But, having already completed a doctorate, I did not want to add more years of schooling to my plate. The bootcamp proposition sounded ideal. The curriculum seemed so comprehensive, starting from first principles and progressing to the level where I wanted to be for my career.
What were your educational objectives?
I was looking to start by developing more coding proficiency. I had played with Python and R a bit, as it was a component of an introductory stats course I taught. I also knew about the importance of big data and machine learning and wanted to learn more about that.
How did you find your experience there?
I found it really rewarding. The first weeks were deceptively slow. The program then ramped up its intensity, to the point of 12 hour days for six to eight weeks. We had morning lectures and then spent the afternoons on additional topics or working on projects. It was a really collaborative experience with everyone coming in from different backgrounds. It is awesome to have everyone coming together, sharing unique ideas and expertise. The instructors and TAs are very willing to help. The environment is very intense but friendly.
How did you get to work at applying the skills you learned in the bootcamp?
The skills we were taught were developed through lectures and homework and solidified through projects. I did four projects at the bootcamp. One involved data visualization in R. I found the Shiny project really fun and exciting, as I had never built anything that elaborate before. As we were advised to pick a project based on some domain knowledge, and I didn't have much, I decided to draw on what I knew from my dad’s experience as an airline pilot for Increasing Airline Customer Satisfaction. That was cool.
The third project was based on a Kaggle Advanced Regression Techniques Competition. The goal was to predict housing prices in Ames, Iowa. That was a group project and my first experience practicing machine learning.
The biggest project I worked on was the a capstone. As a group, we found a problem, defined a problem, and used data to try to solve it. The sector we were working on was mobile advertising. The goal was to predict how individuals would interact with advertisements. We were given 30 terabytes of data relating to people clicking or not clicking ads over the course of a month. We were asked to find value in the data.
How long did it take for you to find your current job? How did NYC Data Science Academy help you?
It took just about a month and worked well, thanks to the school bringing in companies to meet with us. After graduating there was Christmas break. I spent a month working on my resume, reviewing machine learning, and applying for jobs. Three weeks after sending out applications, I got some calls and scheduled interviews.
Before I had those interviews, though, I learned about what it’s like to work at National Grid thanks to NYCDSA hiring individuals from the company to come and host mock interviews and explain a day-in-the-life. That made their perspective on what it was like to jump from the bootcamp to the work environment particularly valuable. I followed up with them the following week at the school’s hiring event, and they brought me in two days later for an interview. They made me an offer the next day. I still had to meet with other companies but after reviewing all my offers, I decided to accept the one from National Grid.
What advice would you have for students considering enrolling in a data science course or program at NYCDSA?
Try to match your existing skill levels to a program that is appropriate. That means matching it to your current level of experience or at least knowing you can get to that level through prework. The bootcamp picks up its pace. Go to lectures as much as you can. Pay attention as much as you can. Ask questions and do the homework as much as you can. There’s a lot to absorb, so I think it’s really important to be there and put in the effort. Oh, and try to get some sleep while hanging on for dear life!
Looking back at you own experience, what are your thoughts?
I’m really grateful for the whole experience. It felt like a huge risk for me. Switching careers is scary, and I needed to move across the country to do it. I did the best I could. Combined with the incredible support at NYCDSA, I feel this was the best decision of my life. I know that sounds hyperbolic, but it’s true. It allowed me to change my career in a short amount of time to something I enjoy a lot.
This post was sponsored by NYC Data Science Academy.
|Courses:||Data Science with Python: Machine Learning, 12-Week Data Science Bootcamp... View All 11 Courses|
|Subjects:||Data Science, R, Python, Hadoop, Spark|
NYC Data Science Academy offers data science boot camps in full- or part-time formats year-round. Students can attend their programs on campus or online. They also provide a quarterly career day where hiring partners meet students for possible job opportunities.