The data science problem is humans and the solution is the internet

Abstract:

Humans cause problems for data science at multiple levels. Any data analysis involves human decisions - but how do we know which decisions are "best"? How do we educate enough humans to even handle the range and speed of data science problems we are facing? How do we hire data scientists, keep track of what they are doing, and keep them up on the latest data science innovations? I'll talk about how we can tackle these problems using internet scale education, research, and employment initiatives being developed by the Johns Hopkins Data Science Lab ( http://jhudatascience.org/ ).

Bio:

Jeff is an associate professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health. He is also co-creator of the Johns Hopkins Data Science Specialization on Coursera ( https://www.coursera.org/specializations/jhu-data-science ) that has enrolled over 4 million students. He writes a blog at Simply Statistics ( https://simplystatistics.org/ ) and is the author of the best-selling book "The Elements of Data Analytic Style" ( https://leanpub.com/datastyle/ ).

----------------------------

Agenda:

• 6:30pm -- Networking and Refreshments

• 7:00pm -- Introduction, Announcements

• 7:15pm -- Presentation and Discussion

• 8:30pm -- Data Drinks (Tonic , 2036 G St NW)

----------------------------