Basic Statistics and Data Science with R

Course Introduction

This course is for beginners in statistics and data analysis who are interested in understanding concepts used in practice and getting hands-on experience using R. There are many different topics and techniques in data analysis. Everyone living in this era is familiar with the keywords such as the 4th Industrial Revolution and Big Data, but it can be overwhelming to understand what these technologies are, what skills are needed in the industry, and where to start. Data analysis doesn't just include complex concepts but also practical skills that anyone can learn and use right away. In this course, we aim to understand what data analysis is, what fields exist, and learn basic statistical knowledge used in the field. We will focus on statistical knowledge specialized in statistical analysis, visualization, and machine learning, and learn the basics of the R language, applying real-world problems to quickly grasp what data analysis is.

Course Content

Week 1

  • Basic statistical concepts (mean, median, variance, matrices, probability distributions)
  • Installing R and explaining the interface
  • Practice: Calculating mean and variance with R

Week 2

  • Working with data
  • Basic functions (For, Ifelse, While, Function)
  • Practice: Writing an algorithm to output minimum and maximum values

Week 3

  • Importing data
  • Data visualization using the ggplot2 package
  • Practice: Visualizing Covid-19 data

Week 4

  • Statistical hypotheses (null hypothesis, alternative hypothesis)
  • P-value, significance level, statistical errors (Type I error, Type II error)
  • Estimation and confidence intervals

Week 5

  • T-test
  • Practice 1: Comparing income differences between men and women
  • Practice 2: Changes in student performance before and after education

Week 6

  • Linear regression analysis
  • Practice: Predicting braking distance using car speed

Week 7

  • Logistic regression analysis
  • Practice: Creating a diabetes diagnosis model using medical data

Course Materials

Course syllabus syllabus.pdf
Lecture 1 lecture1.pdf
Lecture 2 lecture2.pdf
Lecture 3 lecture3.pdf
Lecture 4 lecture4.pdf
Lecture 5 lecture5.pdf
Lecture 6 lecture6.pdf
Lecture 7 lecture7.pdf