Data Science ｜シラバス情報

授業科目名

Data Science

学年

1年

単位数

2.00単位

実務経験の有無

開講クォーター

セメスタ指定なし

担当教員

小松　悟朗

授業形態

授業で主に使用する言語

English

授業方法区分

開講キャンパス

紀尾井町キャンパス

授業の到達目標及びテーマ

This course provides an introduction to Data Science, Econometrics, and causal inference.

Identifying cause-and-effect is of most importance in many business and academic fields, thus the causal inference are now widely used in Business, Marketing, Psychology, Economics, Public Policy, and most importantly, in writing your master thesis.

In this class, we learn Statistics/Econometrics techniques to identify causality, together with some recent development in machine learning, all necessary for a modern data scientist.

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

授業の概要

Course Title: Data Science

Class Format: Lecture

Content:
After quickly reviewing the basic concepts in probability and statistics, we will learn about the basic toolkits in Econometrics/causal inference as well as a couple of machine learning techniques.

Students are also expected to use statistical software such as Python &/or R, to apply those methodologies to real world data.

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

Week 1—Statistics Review
1 Statistics Review (1)
2 Statistics Review (2)

Week 2—Regression Analysis (1)
3 Simple Regression Model
4 Multiple Regression Analysis: Estimation

Week 3—Regression Analysis (2)
5 Multiple Regression Analysis: Inference and Further Issues
6 Qualitative Regressors

Week 4—Causal Inference and Program Evaluation(1)
7 Difference-in-Differences
8 Panel Data

Week 5—Causal Inference and Program Evaluation (2)
9 Instrumental Variables
10 Regression Discontinuity Design, Propensity Score Matching

Week 6—Unsupervised Learning
11 Principal Component Analysis, Factor Analysis
12 Clustering

Week 7—Wrap-up
13 Wrap-up

授業計画

1回

1 Statistics Review (1)

事前学習

Read an assignment

事後学習