| Dr. Benjamin Soltoff | |
|---|---|
| [email protected] | |
| Office | Room 219, 1155 E. 60th St. |
| GitHub | bensoltoff |
- Meeting day: August 31-September 18, MTWThF
- Time: To be determined
- Location: To be determined
This course surveys mathematical and statistical tools that are foundational to computational social science. Topics to be reviewed include mathematical notation and linear equations, calculus, linear algebra, probability theory, and statistical inference. Students are assumed to have encountered most of these topics previously, so that the camp serves as a refresher rather than teaching entirely new topics. Class sessions will emphasize problem solving and in-class exercises applying these techniques. Students who successfully complete the camp are situated to pass the MACSS math and statistics placement exam and enroll in computationally-enhanced course offerings at the University of Chicago without prior introductory coursework.
- Students in the Masters in Computational Social Science
- MA and PhD students in the social sciences who have significant prior training and experience in mathematics and statistics and seek to complete the Certificate in Computational Social Science
- Students looking for a slower-paced camp focused specifically on algebra, calculus, and probability should enroll in SOSC 30100 - Mathematics for Social Sciences. This two-week course makes no assumption of prior math/stats training. Those of you who struggle with the material of this course may switch after the first week to SOSC 30100.
This course may only be taken for pass/fail (non-credit), not for a letter grade or audit. Assignments are comprised of daily problem sets. You are encouraged to work in groups, and the instructional staff is available for consultation during class hours. We expect most students should be able to finish the problem sets during class hours. Grades will be based upon performance on the problem sets.
The University of Chicago is committed to diversity and rigorous inquiry from multiple perspectives. The MAPSS, CIR, and Computation programs share this commitment and seek to foster productive learning environments based upon inclusion, open communication, and mutual respect for a diverse range of identities, experiences, and positions.
This course is open to all students who meet the academic requirements for participation. Any student who has a documented need for accommodation should contact Student Disability Services (773-702-6000 or [email protected]) and provide me (Dr. Soltoff) with a copy of your Accommodation Determination Letter as soon as possible.
Course texts are subject to change for fall 2020
- Bertsekas, D. P., & Tsitsiklis, J. N. (2008). Introduction to probability, 2nd edition. Belmont, MA: Athena Scientific.
- Pemberton, M., & Rau, N. (2015). Mathematics for economists: an introductory textbook, 4th edition. Oxford University Press.
| Date | Topic | Subtopic |
|---|---|---|
| 31-Aug | Linear equations | Linear equations, inequalities, and sets and functions |
| 1-Sep | Linear equations | Quadratics, logarithms, sequences, and limits |
| 2-Sep | Calculus | Differentiation |
| 3-Sep | Calculus | Critical points and approximation |
| 4-Sep | Linear algebra | Matrix algebra |
| 7-Sep | No class (Labor Day) | |
| 8-Sep | Linear algebra | Systems of linear equations and determinants |
| 9-Sep | Calculus | Functions of several variables and optimization with several variables |
| 10-Sep | Calculus | Integration and integral calculus |
| 11-Sep | Probability | Sample space and probability |
| 14-Sep | Probability | Discrete random variables |
| 15-Sep | Probability | General random variables |
| 16-Sep | Probability | Multivariate distributions |
| 17-Sep | Statistical inference | Classical statistical inference |
| 18-Sep | Statistical inference | Bayesian statistical inference |