Step into the world of data analytics and career progression with Yun Gong, a seasoned data scientist and Johns Hopkins University alum. In this episode, Yun shares her wealth of knowledge amassed over 9 years of experience in data modeling experience in applied economics research and industry in the United States. Dive deep into discussions covering the nuances of the data analytics field, hear about her personal career path, her experience pivoting careers and much more. Whether you are at the beginning of your program or about to graduate, Yun Gong's insights will enlighten and inspire, providing invaluable guidance for navigating the dynamic landscape of data-driven industries. Listen to gain a strategic edge and chart your course towards success in the ever-evolving realm of data science.
00:00 Introduction
03:47 Episode's Content
04:06 What's data science?
04:58 Is there a difference between data science and data analytics?
06:11 Do data analyst and data scientists work together?
06:35 Day-to-day of a data scientist
09:30 The importance of communicating/translating results to stakeholders
10:12 The meaning of impact
10:46 What software and tools do you use most regularly?
13:13 What are the typical backgrounds for people who are becoming data scientists?
14:52 Do you see people who are self-taught go into the profession?
16:38 It's hard to anticipate your end profession
17:07 Transitioning from the academic world to the corporate world
19:42 How did you break into the data science world?
21:23 Skills learned in graduate school
23:04 Learning a new language on your own
23:35 Working at CVS Health
24:50 Experiential learning on the job
26:45 Consulting & Machine Learning
29:12 The pandemic's impact on work
30:54 Importance of work/life balance
31:58 What is it like to work remotely on team machine learning projects?
33:05 Working at LinkedIn
33:26 Testing product features
36:21 Deep Dive Analysis
37:07 Challenges
37:31 The importance of prioritizing tasks
39:26 Skills: Sharp intuition
43:13 Senior vs. regular data scientist
44:46 Was the data generated in LinkedIn a predictor of the labor conditions in the US?
48:12 New business using data drive decision making methods
50:28 How is data science going to change in the future?