William Zhou is currently working as an Informatics Specialist at Memorial Sloan Kettering Cancer Center. While working as a Data Analyst at the NYU Langone Medical Center, he enrolled in NYC Data Science Academy’s remote Data Science Bootcamp as a part time student, enabling him to acquire the skills he needed to transition to a data science career within the healthcare industry.
What’s your educational background? What was your job before switching to data science?
I have a Bachelor’s Degree in Pharmaceutical Science from Soochow University and a Master’s degree in Healthcare Policy and Management from Columbia University. I worked as a Data Analyst NYU Langone Medical Center focusing on quality, safety, and improvement.
You already were working full time in the healthcare field. What inspired you to pursue data science?
In early 2016 I witnessed AlphaGo defeated Li Shishi, and that made me realize how powerful Artificial Intelligence is. I also see a bright future for data science in the healthcare industry. I self-learnt an online Machine Learning course on Coursera and was able to utilize the skill in my work project and had a positive outcome. Being proud of my achievement, I also realized there was still a long way down the road. This is the reason why I decided to join bootcamp - fully armed myself with comprehensive data science skill set and then shifting my career towards a new page. NYCDSA is a perfect choice for me as it teaches anything you need to know to work as data scientist and allows you to keep full time job as the same time.
What in particular appealed to you about NYC Data Science Academy?
The learning experience at NYC Data Science Academy is excellent. The course content includes all of the cutting edge technology you need to know for real life data science projects. The remote bootcamp will pair you with other students to work on several data science projects. You can learn while practically coding, and you can learn with all the other students. From my perspective this is a very effective learning approach.
The bootcamp also gave me the skills and confidence when applying data science to real-world projects that I needed for job interviews. For example, as part of the bootcamp, I participated in a Kaggle competition involving machine learning techniques for predicting real estate pricing in the Russian market. That allowed me to apply what I had learned about algorithms and models. Though that was my first Kaggle competition, my group was able to achieve the Top 1%, and that really boosted my confidence for job interviews.
How did you manage your job and the course work?
I joined NYC Data Science Academy as a part time online student while working full time at NYU Langone. My job usually took up to 40 hours per week. I devoted about 35 hour a week for learning all of online course content. On Monday – Friday, I’d come into NYCDSA to self-study from 6 pm to 10 or 11 PM and also come during the weekend. So all together it was like 35 hours. Still, there is so much to learn and sometime I felt like it was not enough.
What kind of support did you have in finding a data science job?
The bootcamp is really great at helping students find jobs. There are two things in particular that stand out. One is the resume revision. I got my resumes revised by Vivian and Chris, and when I applied to different job openings, I saw a very high response rate. The second is that the school has a really large network in the data science field across different industries. The alumni of the program are working in many different kinds of companies and industries, and when they have openings, they reach out to the school for candidates.
How did your job search go?
I began my job hunt in June 2017 right after I graduated from the bootcamp. I got my first job offer in August. Over the course of three months, I received six job offers: two from insurance companies, two from consulting companies, and two from hospitals. I accepted the offer from Memorial Sloan Kettering Cancer Center.
How have you applied your data science skills to your current job?
My work as an Informatics Specialist is to generate reports for quality and safety monitors, and to participate in projects to develop clinical decision support applications that will be embedded in the Electronic Medical Records system. For example, one of my current projects is to develop alert algorithms to help providers identify patients at risk of developing on-site sepsis. As it is a cancer center, there is a potential risk of patients of getting sepsis. The tools I have been using includes Python, SQL, and many Machine Learning packages. A lot of machine learning techniques are involved in this risk assessment.
What would you advise those interested in learning data science?
The data science discipline is constantly growing and developing, and it requires you to keep learning. So my suggestion for students is to study really hard, learn really hard, stay hungry, stay foolish.
This post was sponsored by NYC Data Science Academy.
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