News
SSDA Data Science Story Speaker Series with Sean Law
June 12, 2020 - SSDA
SSDA is pleased to have Spartans back with us this summer to share their data science stories via Zoom. Our first speaker was Sean Law, who received his Ph.D. at MSU, and serves as an advisor on an enterprise A.I. Council at TD Ameritrade.
SSDA Announces Data Visualization Winners
September 20, 2019 - ssda@msu.edu
In the 2019 SSDA Data Visualization Challenge, we received a total of 13 entrees from a wide range of fields and topics. Three faculty judges evaluated every visualization based on a three-part rubric that considered communication effectiveness, creativity, and design. We appreciate the time and energy they put into these assessments. The judges' tasks were made difficult given the high quality and diverse approaches and topics of the submitted work. Now, we are excited to announce the winners.
Graduate Student Welcoming and Resource Fair
August 23, 2019 - ssda@msu.edu
The Social Science Data Analytics Initiative (SSDA) builds skills and connections for students interested in applying data science to important social questions. SSDA runs free workshops throughout the year, including introductory training sessions on working with data using R and Python, and intermediate and advanced topics including machine learning, data visualization, and data processing. SSDA also hosts talks and working group sessions, and works with other campus groups to sponsor events. Not least, SSDA is developing a Graduate Fellows program to enable you to communicate your growing abilities and talents to the world!
SSDA Student Engagement Program
June 26, 2019 - ssda@msu.edu
Michigan State University's Social Science Data Analytics (SSDA) is a Social Science College level initiative to promote quantitative analysis throughout the college and beyond. The goal of the SSDA Student Engagement Program (SSDA-SEP) is to prepare future data scientists to tackle social science questions. Through this student engagement program, social science students will develop a deeper understanding of how quantitative analysis and programming skills could benefit their research and studies, while non-social science students, especially those with machine learning or programming backgrounds, could apply those skills to address important social science problems.