Data Science

1年

2.00単位

セメスタ指定なし

小松　悟朗

English

紀尾井町キャンパス

This course provides an introduction to the advanced statistical methods in Data Science, Machine Learning, and Econometrics.

Students will also have hands-on experiences on programming languages such as Python &/or R, to apply those methodologies to real world data.

No programming skills are required. We also welcome those who are interested in programming skills and data scientists.

*It is highly recommended that students have already taken “Statistics” offered by GSIA.
*The class materials and schedule are subject to change depending on students’ statistical skills and research topics.

Course Title: Data Science

Class Format: Lecture

Content:
After we review some basic concepts in probability and statistics, we will learn about the basic toolkits in machine learning and Econometrics, using Python and/or R programming languages.

Students must bring their own laptop computers for the in-class programming exercises.

Keyword:
Data Science, Machine Learning, Supervised Learning, Regression Analysis, Causal Inference, Unsupervised Learning, Clustering, Python, R, Data Scientist


Week 1—Preliminaries
1 Statistics Review and Introduction to R (1)
2 Statistics Review and Introduction to R (2)

Week 2—Causality and Measurement
3 Causality
4 Measurement and Clustering

Week 3—Regression Analysis (1)
5 Simple Regression Model
6 Multiple Regression Analysis: Estimation

Week 4—Regression Analysis (2)
7 Multiple Regression Analysis: Inference and OLS Asymptotics
8 Multiple Regression Analysis: Further Issues and Qualitative Regressors

Week 5—Causal Inference and Program Evaluation (1)
9 Difference-in-Differences
10 Panel Data

Week 6—Causal Inference and Program Evaluation (2)
11 Instrumental Variables
12 Regression Discontinuity Design, Propensity Score Matching

Week 7—Wrap-up
13 Wrap-up

1 Statistics Review and Introduction to R (1)

Read an assignment

Solve and submit homework quizzes

2 Statistics Review and Introduction to R (2)

Read an assignment

Solve and submit homework quizzes

3 Causality

Read an assignment

Solve and submit homework quizzes

4 Measurement and Clustering

Read an assignment

Solve and submit homework quizzes

5 Simple Regression Model

Read an assignment

Solve and submit homework quizzes

6 Multiple Regression Analysis: Estimation

Read an assignment

Solve and submit homework quizzes

7 Multiple Regression Analysis: Inference and OLS Asymptotics

Read an assignment

Solve and submit homework quizzes

8 Multiple Regression Analysis: Further Issues and Qualitative Regressors

Read an assignment

Solve and submit homework quizzes

9 Difference-in-Differences

Read an assignment

Solve and submit homework quizzes

10 Panel Data

Read an assignment

Solve and submit homework quizzes

11 Instrumental Variables

Read an assignment

Solve and submit homework quizzes

12 Regression Discontinuity Design, Propensity Score Matching

Read an assignment

Solve and submit homework quizzes

13 Wrap-up

Read an assignment

Solve and submit homework quizzes

Homework Quizzes: 50%
Final Project: 50%

To be provided when necessary/requested

To be provided

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Quantitative Social Science: An Introduction
Kosuke Imai
Princeton University Press.
ISBN: 978-0691175461
2017

Introductory Econometrics: A Modern Approach (7th Edition)
Wooldridge
South-Western Pub.
ISBN: 978-1337558860
2019

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd ed.
Peter Bruce, et al.
Oreilly & Associates Inc.
978-1492072942
2020