Required Course for DS Certificate and DS Masters: EECS 409* 

*either section meets requirement

EECS 409-001: Each semester MIDAS hosts weekly seminars featuring data science leaders from industry and academia.  Seminars are held Mondays, 4-5pm.  Attendance required for completion of this course.
EECS 409-002: MIDAS will offer a 1-credit seminar course  in the Winter 2021 semester on Electric Vehicles’ Adoption Sustainability and Safety.  A course description and meeting times are below.  Data Science Certificate students and Master’s students may count this as their 1-credit seminar requirement.
Course Details

This journal club-style course consists of guest speakers and in-depth discussion of papers, briefings, and data related to three major unknowns in the EV transition:

a) incentives and consumer behavior in EV adoption,
b) repurposing, recycling, and remaking batteries,
and
c) managing battery accidents and fires
Time permitting, students will also discuss the skill shift needed in the workforce that could shape many labor markets and education (from gears and cranks to electrolytes and wires).
Professor: Anna Stefanopoulou
Meeting Time: Thursdays, 3:30pm via Zoom
Core Courses for the Graduate Certificate Program
Other Methods and Technique Courses Approved for the Certificate Program
Applied Data Science Courses Approved for the Certificate Program
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Massive Open Online Courses (MOOCs)

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SurvMeth 687 (or 746): Applications of Statistical Modeling
AM
Advanced Statistical Modeling, designed for students on both the social science and statistical tracks for the two programs in survey methodology, will provide students with exposure to applications of more advanced statistical modeling tools for both substantive and methodological investigations that are not fully covered in other MPSM or JPSM courses. Modeling techniques to be covered include multilevel modeling (with an application to methodological studies of interviewer effects), structural equation modeling (with an application of latent class models to methodological studies of measurement error), classification trees (with an application to prediction of response propensity), and alternative models for longitudinal data (with an application to panel survey data from the Health and Retirement Study). Discussions and examples of each modeling technique will be supplemented with methods for appropriately handling complex sample designs when fitting the models. The class will focus on essential concepts, practical applications, and software, rather than extensive theoretical discussions. Offering: Annually, Fall
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Other Methods and Technique Courses Approved for the Certificate Program

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