by Dr. Hui Lin (Shopify) and Dr. Ming Li (Amazon)
Many aspects of day-to-day data science work are almost absent from the conventional statistics literature and curriculum. Yet these activities account for a considerable share of the time and effort of data professionals in the industry. This short course, "Data Science in Practice," focuses on the practical side of data science and statistical analysis.
This course aims to increase the visibility of industrial data science-related careers, data workflows, data environment, project cycle, and most common techniques. It is designed for audiences with a statistics education background, and it bridges the gap between traditional statisticians and data professionals in the industry.
No software download or installation is needed, and the hands-on part is done through the internet browser with hands-on sessions in Databricks and Colab (using PySpark).
Location: Thornton Hall, Room 404 (4th floor)- Link to Map
Schedule for June 13, 2022:
9 - 11:30
Part one: Data science as a career
Part two: Data science project cycle
11:30 - 12:30: Lunch
12:30 - 2:30
Part one: Big data platform, pipeline, and modeling
Part two: Deep learning in a nutshell
2:30 - 3:00 break
3:00 - 4:40 Hands-on exercises
Ming Li is a Research Science Manager at Amazon and adjunct instructor of the University of Washington. He organized and presented the 2018 JSM Introductory Overview Lecture: Leading Data Science: Talent, Strategy, and Impact. He was the Chair of Quality & Productivity Section of ASA. With a few years' experience in data science and machine learning, he has trained and mentored numerous junior data scientists with diversified backgrounds such as statistician, software developer, database programmer, and business analyst. He is also an Instructor of Amazon's internal Machine Learning University and the recipient of Amazon's Best Science Mentor Award. He holds a Ph.D. in Statistics from Iowa State University.
Hui Lin is currently a Lead Quant Researcher at Shopify, and has previously worked at Google/Netlify/DuPont. She holds a Ph.D. in Statistics from Iowa State University. She enjoys making analytics accessible to a broad audience and sharing her experience in data science (or the more general quantitative field). Outside her full-time job, she is a blogger and Youtuber. Her dream is to start a real Scientist Cafe.