About

This is a lecture note of a course jointly taught in the Winter term AY2021.

First we start with the contents of the slides and gradually include other contents into this book.

Course Contents

  1. Introduction:
    - About the Course
    - Data Science
    - Data: Big Data, Public Data, etc.
    - R, RStudio, [RStudio Cloud]
    - [A] Self-introduction, etc.
  2. Exploratory Data Analysis (EDA) 1
    - R Basics with RStudio and/or RStudio.cloud; R Script
    - [P] swirl
    - [A] Quiz on Moodle
  3. Exploratory Data Analysis (EDA) 2
    - R Markdown; R Notebook
    - Data Visualization
    - Package: tidyverse: Introduction to ggplot2, Explore, I
    - Data: datasets
    - [P] RStudio Primers: Programming Basics, Visualization Basics
    - [A] R Notebook using datasets package data
  4. Exploratory Data Analysis (EDA) 3
    - Data Wrangling
    - Package: tidyverse, WDI: Introduction to tibble, dplyr, WDI, WDIsearch
    - Data: World Bank Data
    - [P] RStudio Primers: Work with Data, Wrangle, I
    - [A] WDI: submit R Notebook file with a4_ID.nb.html
  5. Exploratory Data Analysis (EDA) 4
    - Tidy Data
    - Package: tidyverse, dplyr: pivoting
    - Data: WDI, UN data, CLASS.xlsx, JHU Covid-19 Data
    - [P] RStudio Primers: Visualize Data – Explore, II
    - [A] EDA on Public Data: submit R Notebook file with a5_ID.nb.html
  6. Exploratory Data Analysis (EDA) 5
    - Data Modeling and EDA
    - Package: modelr, HistData
    - Data: datasets::cars, datasets::iris, HistData::GaltonFamilies
    - [P] RStudio Primers: Tidy Your Data – Wrangle, II
    - [A] Modeling and EDA: submit R Notebook file with a6_ID.nb.html
  7. Topics 1
    - Inference Statistics (Regression, hypothesis testing, classification, etc.)
    - Standardization, PPDAC
    - Package: tidyverse, WDI, car, modelsummary
    - Data: GDP using WDI,
    - [A] Assignment on regression analysis and PPDAC cycle
  8. Topics 2
    - Categorical Variables, Analysis of Variance (ANOVA)
    - Classification, Causal Inference
    - Data: NHEFS data, datasets::iris
  9. Topics 3: Guest Lecture
  10. Presentation
    - Students’ Presentations
    - Course Round-up

[P]: Practice, [A]: Assignment