社会学実証基礎演習３

科目名	社会学実証基礎演習３
教員名	菅野剛
単位数	1	課程	前期課程	開講区分	文理学部
科目群	社会学専攻
学期	後期	履修区分	選択必修

授業概要	- Programming and Data Science - This course might NOT be qualified as "I: Graduate Seminar in Multivariate Analyses", the standard curriculum at the Japanese Association for Social Research (JASR), due to inevitable fundamental changes in course content and lecture topics in 2019.
授業のねらい・到達目標	- We are facing the Fourth Industrial Revolution (4IR). The times have changed outside the class. Worth learning is the STEAM not in the class. - Learn Data Science the hard way. Learn Data Science by oneself. You will need several other courses on social survey, statistics, data analysis, programming, machine learning, and AI elsewhere. - Introduction to research with data science: statistics, programming, and data analysis. - Report of the Central Council for Education http://www.mext.go.jp/b_menu/shingi/chukyo/chukyo4/houkoku/1412988.htm - Coucil for Science, Technology and Innovation https://www.kantei.go.jp/jp/singi/tougou-innovation/dai2/siryo1.pdf https://www.kantei.go.jp/jp/singi/tougou-innovation/dai4/siryo1-1.pdf
授業の方法	https://sites.google.com/a/nihon-u.ac.jp/sugano-lab/home/google-classroom - Preparation for class by reading textbooks and by learning online resources beforehand is required. - Discuss about the topics and applied data analysis during the class. - Programming and analyses are provided as pre-course work and homework. - NU-MailG accounts and joining to Google Classroom are required. - BYOD: Bring your own device. - We do not provide support for the Windows operating system due to shortage of human resource. - Courses are to be closed upon no registration.

授業計画
1	【授業内容】 Classroom: Notification of NU-AppsG accounts, password reminder settings, Password settings, explanation of Google Classroom, joining a class, Google Colaboratory, Python, Introduction to Programming and Data Science. 【事前学習】 Pre-course work: Introduction to Programming and Data Science 【事後学習】 Homework: Introduction to Programming and Data Science
2	【授業内容】 Optimization and the Knapsack Problem, Google Colaboratory, Python 【事前学習】 Pre-course work: Optimization and the Knapsack Problem 【事後学習】 Homework: Optimization and the Knapsack Problem
3	【授業内容】 Decision Trees and Dynamic Programming, Google Colaboratory, Python 【事前学習】 Pre-course work: Decision Trees and Dynamic Programming 【事後学習】 Homework: Decision Trees and Dynamic Programming
4	【授業内容】 Graph Problems, Google Colaboratory, Python 【事前学習】 Pre-course: Graph Problems 【事後学習】 Homework: Graph Problems
5	【授業内容】 Plotting, Google Colaboratory, Python 【事前学習】 Pre-course: Plotting 【事後学習】 Homework: Plotting
6	【授業内容】 Stochastic Thinking, Google Colaboratory, Python 【事前学習】 Pre-course: Stochastic Thinking 【事後学習】 Homework: Stochastic Thinking
7	【授業内容】 Random Walks, Google Colaboratory, Python 【事前学習】 Pre-course: Random Walks 【事後学習】 Homework: Random Walks
8	【授業内容】 Inferential Statistics, Google Colaboratory, Python 【事前学習】 Pre-course: Inferential Statistics 【事後学習】 Homework: Inferential Statistics
9	【授業内容】 Monte Carlo Simulations, Google Colaboratory, Python 【事前学習】 Pre-course: Monte Carlo Simulations 【事後学習】 Homework: Monte Carlo Simulations
10	【授業内容】 Sampling and Standard Error, Google Colaboratory, Python 【事前学習】 Pre-course: Sampling and Standard Error 【事後学習】 Homework: Sampling and Standard Error
11	【授業内容】 Experimental Data Part 1, Google Colaboratory, Python 【事前学習】 Pre-course: Experimental Data Part 1 【事後学習】 Homework: Experimental Data Part 1
12	【授業内容】 Experimental Data Part 2, Google Colaboratory, Python 【事前学習】 Pre-course: Experimental Data Part 2 【事後学習】 Homework: Experimental Data Part 2
13	【授業内容】 Machine Learning, Google Colaboratory, Python 【事前学習】 Pre-course: Machine Learning 【事後学習】 Homework: Machine Learning
14	【授業内容】 Statistical Fallacies, Google Colaboratory, Python 【事前学習】 Pre-course: Statistical Fallacies 【事後学習】 Homework: Statistical Fallacies
15	【授業内容】 Programming and Data Science, Google Colaboratory, Python 【事前学習】 Pre-course: Programming and Data Science 【事後学習】 Homework: Programming and Data Science

その他
教科書	使用しない
参考書	John V. Guttag, Introduction to Computation and Programming Using Python: With Application to Understanding Data., The MIT Press, 2016, 2 edition ジョン・V. グッターグ (著), 久保幹雄 (翻訳) 『世界標準MIT教科書 Python言語によるプログラミングイントロダクション第2版: データサイエンスとアプリケーション』近代科学社 2017年 - Textbooks are optional.
成績評価の方法及び基準	授業参画度(100%) - Self-directedness and Intellectual flexibility.
オフィスアワー	- Appointment times will generally be available after the class. Ask any questions at any time on Google Classroom.