MSDS 400 Syllabus Goldfeder Summer 2020 v2.pdf - MSDS 400...

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1 MSDS 400: Math for Data Scientists Instructor Contact Information Instructor: Philip M. Goldfeder, PhD Email: [email protected] Mobile: 773.245.6283 Office Hours: By appointment Response Time: I will usually respond to emails within moments and postings within hours. Optional Synchronous Meetings (Sync Sessions): TBA Biography I have undergraduate degrees in Physics and Philosophy from Lafayette College in Easton, PA and graduate degrees in Applied Mathematics from some school in Evanston whose name is probably familiar to you if you are reading this. My prior work was as a management consultant with Booz & Company (formerly Booz Allen and Hamilton), specializing in mergers and acquisitions. I have been published in Journal of Phys. Chemistry B, Mathematical Problems in Engineering, Journal of Engineering Mathematics , and Graziadio Business Review , as well as several chapters in the Macmillan Science Library . I have taught many subjects over many levels, ranging from middle school through doctoral level, in both traditional and online settings, here in the states and internationally. I live in the Chicagoland area with my wife and sons. My favorite pastimes are Crossfit, travel, and family time, in varying order depending on the day. Course Description Mathematics is the underlying basis of many aspects of data science. This course will provide students with an overview of much of the mathematics used throughout the MSDS program. The course will begin with a brief overview of algebraic concepts and linear functions, including least squares lines. Some aspects of linear algebra will also be covered, such as operations with matrices, linear systems of equations, and linear systems of inequalities. The course will cover a few topics in discrete mathematics such as probability and graphs. Following this, the course will move on to calculus, both differential and integral. Finally, the course will end with an