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データサイエンス基礎研究

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令和2年度以降入学者 データサイエンス基礎研究
教員名 菅野剛
単位数    2 課程     開講区分 文理学部
科目群 社会学専攻
学期 前期 履修区分 選択必修
授業形態 遠隔授業(オンデマンド型)
授業の形態 Google Chrome ブラウザ を使い Google Classroom で行います (クラスコード は Blackboard に掲載)。
必要な場合は Google Meet、 Google Chat、 Hubs などによる同時双方向で対応します。
Blackboard ID 20231806
授業概要 Introduction to Programming and Data Science
授業のねらい・到達目標 Beware of confirmation bias and train yourself to make decisions as logically as possible.
Familiarize yourself with English, statistics, and programming, which are the lingua franca of the world.
授業の形式 講義、演習
授業の方法 Prior learning is required by reading the textbook, studying online, and performing programming and data analysis.
Students learn, practice, and get feedback.
An NU-MailG account and enrollment in Google Classroom are required.
授業計画
1 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 (2時間)
【事後学習】Homework: Introduction to Programming and Data Science (2時間)
2 Introduction to Python. Assessment and feedback.
【事前学習】Pre-course work: Introduction to Python (2時間)
【事後学習】Homework: Introduction to Python (2時間)
3 Core Elements of Programs. Assessment and feedback.
【事前学習】Pre-course work: Core Elements of Programs (2時間)
【事後学習】Homework: Core Elements of Programs (2時間)
4 Simple Algorithms. Assessment and feedback.
【事前学習】Pre-course work: Simple Algorithms (2時間)
【事後学習】Homework: Simple Algorithms (2時間)
5 Functions, scoping, and abstraction. Assessment and feedback.
【事前学習】Pre-course work: Functions (2時間)
【事後学習】Homework: Functions (2時間)
6 Tuples and Lists. Assessment and feedback.
【事前学習】Pre-course work: Tuples and Lists (2時間)
【事後学習】Homework: Tuples and Lists (2時間)
7 Dictionaries. Assessment and feedback.
【事前学習】Pre-course work: Dictionaries (2時間)
【事後学習】Homework: Dictionaries (2時間)
8 Testing and Debugging. Assessment and feedback.
【事前学習】Pre-course work: Testing and Debugging (2時間)
【事後学習】Homework: Testing and Debugging (2時間)
9 Exceptions and Assertions. Assessment and feedback.
【事前学習】Pre-course work: Exceptions and Assertions (2時間)
【事後学習】Homework: Exceptions and Assertions (2時間)
10 Classes and object-oriented programming. Assessment and feedback.
【事前学習】Pre-course work: Classes and Inheritance (2時間)
【事後学習】Homework: Classes and Inheritance (2時間)
11 An Extended Example. Assessment and feedback.
【事前学習】Pre-course work: An Extended Example (2時間)
【事後学習】Homework: An Extended Example (2時間)
12 Computational Complexity. Assessment and feedback.
【事前学習】Pre-course work: Computational Complexity (2時間)
【事後学習】Homework: Computational Complexity (2時間)
13 Some simple algorithms and data structures. Assessment and feedback.
【事前学習】Pre-course work: Searching and Sorting Algorithms (2時間)
【事後学習】Homework: Searching and Sorting Algorithms (2時間)
14 Plotting and more about classes. Assessment and feedback.
【事前学習】Pre-course work: Plotting (2時間)
【事後学習】Homework: Plotting (2時間)
15 Programming and Data Science. Assessment and feedback.
【事前学習】Pre-course work: Programming and Data Science (2時間)
【事後学習】Homework: Programming and Data Science (2時間)
その他
教科書 適宜紹介する。
参考書 John V. Guttag, Introduction to Computation and Programming Using Python: With Application to Understanding Data., The MIT Press, 2016, 2 edition
P.G.ホーエル 『初等統計学』 培風館 1981年 第4版
T.H.ウォナコット・R.J.ウォナコット 『統計学序説』 培風館 1978年
P.G.ホーエル 『入門数理統計学』 培風館 1978年
『IT技術者の長寿と健康のために (長野宏宣・中川晋一・蒲池孝一・櫻田武嗣・坂口正芳・八尾武憲・衣笠愛子・穴山朝子)』 近代科学社 2016年
盛山和夫 『社会調査入門』 有斐閣 2004年
今井耕介 『社会科学のためのデータ分析入門(上)』 岩波書店 2018年
成績評価の方法及び基準 授業内テスト:Online tests(50%)、授業参画度:Reaction or response papers(50%)
Self-directedness and Intellectual flexibility.
オフィスアワー Ask any questions at any time on Google Classroom. Appointment times will generally be available after the class.

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