🌞 Course ID：IB533 (111.1)  2022/09/05 ~ 2023/01/02
🗓 Time/Place： Every Monday Morning 09:10 ~ 12:00 / R3051
⏱ Collaborative Learning Session： Every Tuesday Evening 18:30 ~ 21:00 / R1032
👩‍🏫 Instructor： Yung-Jan Cho, tonychuo@mail.nsysu.edu.tw

⏰ NOTICES :
※ Please do the followings Before the First Class：
■ Fill in the Initial Questionnaire
■ Subscribe to our YouTube Channel
■ Bring in your notebook computers with `R` 和 `RStudio` installed！

◇ 【 Facebook 】 ◇ 【 YouTube 】 ◇ 【 Google Drive 】 ◇ 【 TA TEAM 】 ◇

### Course Outlines

PART-I: DATA & PROGRAMMING
01 Intro. R and RStudio
02 Cases: Solving Business Problems by Data Manipulation
03 Descriptive Analysis with Simple Plots
04 Cases: Exploring Data by Comparison

PART-II: APPLICATIONAL PROBABILITY
05 Applicational Probability in R
06 Case: Data, Model, Prediction, Decision
07 Case: Analyze Marketing Research Data

PART-III: DATA EXPLORATION
08 Explorative Analysis Methods
09 Data Visualization Techniques
10 Cases: Clustering and Dimension Reduction
11 Case: Retail POS Data

PART-IV: PREDICTIVE MODELS
12 Predicting Quantity, Linear Regression
13 Predicting Probability, Logistic Regression
14 Case: Customer Value Management

15 From Decision to Prediction
16 Assumption and Simulation
17 Performance Evaluation and Optimization
18 Capstone Project: Data Driven Marketing Plan

### Course Description

Capitalizing the business school’s Big Data Business Analytics Platform (https://bap2.cm.nsysu.edu.tw/), in this course we cover: (1) R Language Basics, (2) Data Exploration and Visualization, (3) Application of Probability & Statistics, (4) Applicational R-Packages, and (5) Practical Strategic Planning. Besides the aforementioned topics of business analysis, this course also prepares the students for advanced technical courses related to big data, machine learning and artificial intelligence in the General Management, Digital Marketing and FinTech Micro Curriculums in the ITSA Program (https://bap2.cm.nsysu.edu.tw/?page_id=513).

### Prerequisites

Although there is no mandatory prerequisites, basic knowledge of probability (high school level) is presumed. As for statistics, we will align with the class of IB502 (110.1) – Statistics and Quantitative Methods. The course loading is quite heavy. For those who do not have programming experience, it’d take 6 ~ 10 hours per week to finish the personal and team assignments.

### Objectives

1. Introduce to programming (R) language. Overcome the entry barrier of programming language with interactive notebooks, web-pages and web-based simulation tools.
2. Develop major business analytics skills in practical data cases.
3. Practice and experience the synergy among programming language, statistics and managerial knowledge.
4. Prepare the advanced analytics courses that involve big-data, ma-chine learning and/or artificial intelligence.

### Text Book

No text book is required. There is an optional (online, free) reference book at：
Hadley Wickham and Garrett Grolemund, R for Data Science, O’REILLY 2016 (https://r4ds.had.co.nz/)

We will use self-developed materials, including web pages, program notebooks, app’s and presentation files as below

• Course website: integer the self-made materials and the online resources to facilitate mutual learning. (HTML)
• Guided videos: assist students to understand and preview the online contents.
• Handouts: the slides of every unit. (PPT)
• Course notes: code used in every course will be provided. (R-Notebook)
• Assignment notes: the assignment of every unit will be provided. (R-Notebook)
• Online simulation: assist students to understand complicated concepts through the interactive simulation. (R:Shiny)
• Course videos: the videos of every course will be recorded and provided for students to review. (YouTube)

The optional reference readings are :

1. Field, A., Miles, J. & Field, Z. (2012). Discovering Statistics Using R. SAGE Publications Ltd.
2. Kabacoff, R. (2015). R in Action: Data Analysis and Graphics with R. Manning Publications
3. Peng, R., R Programming for Data Science. The Lean Publishing (pdf)
4. Venkatesan, R., Farris, P., & Wilcox, R. T. (2015). Cutting-edge marketing analytics: real world cases and data sets for hands on learning. Pearson Education.
5. Wickham, H. & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.