Graduate / PS for Columbia University Master of Science in Data Science (Part-time); once dismissed from PhD [6]

I plan to apply for the MS in DS program in Columbia University part-time. As all courses are provided in the night and my company is not far from it.

I have the following questions:
1. Do I need to clarify part-time in my essay? As they do have the same requirement bar, courses and degree for both full-time and part-time students. Do I need to address how I could manage?

2. Do I need to address how I overcome my biggest failure as I got dismissed from Economics PHD program after the first year? Or I just put what I put here: I am not ready for it hoping they won't notice it either?

3. My first-year PHD GPA is very low around 2.8 but after I transferred to Master, I got all straight A-. Should I explain this? As in my transcripts, they put PHD and master courses altogether.

to be a Data Scientist in the Health industry

I was raised up by my grandparents ever since my parents abandoned me while I was a 6-month baby. The highest education degree of my parents was my father's as a high school student who quitted in the second year. Yet I love reading and learning since kid even nobody kept telling me how wonderful the process of chasing knowledge is and how it will change my life. My grandparents raised me very conservatively, encouraging me to stay, never to travel.

The curiosity of knowledge is always the urge that helps me get where I am. After high school, despite being admitted to XX University (ranked 8 in China but 1 hour from home), instead, I chose to go as far as I could, XX University. Still a top school in China, XX is the farthest away I could get from my hometown. There I studied Economics. I don't know much about what Economics is about and my grandparents picked it for me as they thought it must be related to money. A lucky coincidence: I love economics and the view it provided for me to see the world and everyday life as an Economist. It is like a filter that you could see the world in another way and more rational and scientific.

In the 4th year, I exchanged to Sup De Co Business School in France for one year. There I studied International Business Administration and got my second bachelor's degree. It made sense so much that you learned international business administration in an international environment. After the wonderful experience in France, I decided to further pursue a higher degree. With a strong academic background in statistics and economics related courses, I got full range scholarship as an Economics PhD student in SUNY Albany. Whereas, the first year was a leap for me as I took advanced Econometrics, Microeconomics and Macroeconomics courses without taking any Intermediate courses before. But through self-learning, I passed the Macroeconomics qualification exam yet failed the Microeconomics one. I realized that I might not be ready for PhD program yet and thus transferred to Economics Mater program concentrating in Forecasting. Unlike the very theoretical setting of PhD courses, the forecasting program is full of practical use and I quickly got obsessed with using statistical models via SAS coding to solve the real-life problems. In the 2nd year, I got an average GPA of 3.71.

During my master program, I got an Research Analyst internship opportunity in DOH. That is the first time I realize how to organize and transfer the data and interpret them in a meaningful way to help people. As a member of the core team of three, we also drafted a paper: Multicomponent Approach to Evaluating a Pre-exposure Prophylaxis (PrEP) Implementation Program in Five Agencies in New York and submitted for publication. In the paper, we used a multicomponent approach to evaluate a PrEP implementation program. The health area seems different from Economics, but the methods to establish the model, fit the data and analyze the data are common. The way to process, structure, and analyze data really interest me and I know that it would be the career I could pursue in my life.

After graduation, I got a position as Data Analyst in the Office Of Mental Health. There I got access to Medicaid Data Warehouse data. Using SQL querying and data mining, we monitor the performance of health plans while implementing the managed care transform. The database is large and for extracting meaningful information from relational databases, you need to figure out clear logic.

After OMH, I transferred to New York XXX-a health information exchange company. There as the first time, I used Machine learning to solve a fuzzy-matching problem. Using Supervised learning, I managed to find the duplicate records submitted by providers. This has been a long existing unsolved issue. It is hard to find duplicates using only name-address strings as identifiers without machine learning, let alone the data we collected are unstructured and manually input. It is the first time I know how machine learning could help my work. And I know that I should learn more about the methodology and algorithms behind it instead of just using the open source packages online. I want to be a package developer in the future.

Reduced cost analysis, cheap large data storage and digitization of health records made big data and machine learning application to health is demanding. Right now, in the public health, the traditional statistical analyses are still the main methodologies. But I know that big data and machine learning could certainly help us dig more in the torrent of health data we have. Directly after graduating, I will transfer my career path to be a Data Scientist in the Health industry. There I will learn how to transfer what I learned to meaningful application in health like analyze mental health records which are mostly qualitative data using NPL, use big data and machine learning to link health data in different sources, join the unstructured data from SNS and personal health devices, provide useful insights that allow superior healthcare services etc. From there, in the long-term, I will continue to work in the health sector as a data scientist for the booming health data market.