R: 程式、機率與統計 (111-2)

R: Statistical Programming Methods

Course Syllabus


Aligning with the Big Data Business Analytics Platform, NSYSU, this course is aimed to focus on the delivery of statistical methods using R language. Compared to the conventional statistics courses focuses on the concept only, this course is aimed to develop the fundamental and working statistical knowledge with the hands-on experience for students to conduct further advanced business analysis.

The course will include:

  • R programming
  • Data Analysis
  • Statistical Experiments and introduction of probability
  • Regression and Prediction


By the end of the course, students are expected to:

  • Understand the basic R language and use Rstudio
  • Manage and filter data to support analysis
  • Apply statistical tools in R to analyze data and discuss findings
  • Demonstrate data visualization techniques
  • Develop regression models


Peng, R., 2016. R Programming for Data Science. The Lean Publishing
Field, A., Miles, J. & Field, Z., 2012. Discovering Statistics Using R. SAGE Publications Ltd
Bruce, P., Bruce, A., & Gedeck, P., 2017. Practical Statistics for Data Scientists: 50 Essential Concepts Using R and Python. O’Reilly


Class participation: 15%

Individual Assignment: 25%

Midterm Open-book exam: 25%

Final Project: 30%

Peer Review: 10%

Week Syllabus
1 Introduction to course
2 Basic R Language and Introduction to R studio
3 Exploratory Data Analysis (1)
4 Exploratory Data Analysis (2)
5 Data Visualization (1)
6 Data Visualization (2)
7 Sampling Distribution
8 Midterm and Practice
9 Significance Testing (1)
10 Significance Testing (2)
11 Simple Linear Regression
12 Interpretation of Linear Regression
13 Logistic Regression
14 Hands-on Practice and Group Discussion
15 Group Project
16 Group Project