Programming for Business Analytics (Graduate)
Course Overview
This course teaches you to turn messy business data into defensible answers with R. You will learn to wrangle and visualize data, evaluate claims about causality, fit and interpret linear regressions, and quantify how much you should trust an estimate through sampling distributions, the bootstrap, confidence intervals, and hypothesis tests. Every method is practiced on real datasets, and the semester culminates in a team project published as a portfolio-ready article with a public GitHub repository.
Agentic AI now writes much of the code. The skill that matters has shifted from writing code to directing and verifying it: without the underlying building blocks, you cannot tell when an analysis is wrong, and you end up steered by the tool rather than steering it. The course therefore builds programming and visualization foundations first, then turns to statistical inference, and every in-class exercise ends with a verification step.
This is the graduate course (ISS5066, Thursdays). The emphasis goes beyond applying the methods to evaluating and producing rigorous empirical research: the final project must use self-collected or non-trivial data, address identification and threats to inference, and reach greater analytical depth. It also builds the foundation for Experiments and Causal Inference (ISS5096). Students may not receive credit for both ISS4066 and ISS5066.
Learning Objectives
- Data wrangling and visualization: filter, summarize, group, join, and reshape data with the tidyverse, and build clear exploratory graphics with ggplot2.
- Causality and regression: distinguish randomized experiments from observational studies, and use simple and multiple linear regression to reach defensible conclusions.
- Statistical uncertainty: reason with sampling distributions, the bootstrap, confidence intervals, and hypothesis tests, including permutation tests and power.
- Professional workflow: R, RStudio, Quarto, git, and GitHub, plus the habit of checking what AI-assisted code actually did.
Who Should Take This Course
No prerequisites. The course is designed for students from any background who want a working foundation in data analysis for business.
Teaching Team
Instructor
Jaewon Yoo · Assistant Professor
Institute of Service Science, College of Technology Management
National Tsing Hua University
- TSMC Bldg. R828B
- jaewon.yoo [at] iss.nthu.edu.tw
- Office hours by appointment: book a slot via Google Calendar.
Teaching Assistant
Mave Kimara Alexander
M.S. Student, Taipei School of Economics and Political Science (TSE) · NTHU IMBA alumna
alexander.mave [at] gmail.com
Course Platforms
| Purpose | Platform |
|---|---|
| Syllabus, weekly schedule, policies | This website |
| Slides, handouts, in-class exercises, assignment submissions, grades, announcements | eeclass |
| Office hours and project consultations | Google Calendar booking |
| Private matters |
Course Details
- Sep 7 – Dec 26, 2026
- Thursday
- 14:20–17:20
- TSMC Bldg. R421
Communication
Lecture slides, handouts, in-class exercises, assignments, and grades are on eeclass (NTHU's learning platform; enrolled students are added automatically). Announcements go out through eeclass. For private matters, email the instructor. Slides are not posted on this public page.
What's New
This page is updated during the semester. Recently changed items are listed below.
- ISS5066, Thursdays. Undergraduates should enroll in ISS4066 (Undergraduate).
- Sep 6, 2026: Fall 2026 page published. In Week 6 (Oct 15) there is no class meeting; instead, teams meet the instructor by appointment to discuss research questions.
- No take-home quizzes this year. The 5% "Quizzes & In-Class Exercises" component consists of five in-class exercises (1% each), submitted on eeclass the same day.
- Team presentations with Q&A in the final week.
Topics Covered
Data Wrangling & Viz
Transforming messy data into tidy formats and communicating insights with ggplot2.
Causality & Regression
Evaluating causal claims and using simple and multiple linear regression.
Statistical Uncertainty
Sampling distributions, the bootstrap, confidence intervals, and hypothesis tests.
Professional Workflow
R, RStudio, Quarto, git, and GitHub, plus directing and verifying AI-assisted analysis.
Weekly Schedule (Graduate, Thursdays)
Assignments are due before class starts (14:20); Sunday deadlines are 23:59. Week 6 has no class meeting; teams meet the instructor by appointment instead. Readings should be completed before class. The schedule is tentative; eeclass announcements take precedence.
Part I: Working with Data in R
Week 1 (Sep 10): Course Introduction and R Programming Basics
- How the course works
- A first look at R and RStudio; Quarto
- Getting started: programming errors, operators, variables
- Readings: MD Ch. 1; AAG Ch. 4.3, 5, 6; VT Ch. 1–2. Install R and RStudio before class.
Swirl tutorials 1–3 by Week 2
Week 2 (Sep 17): Data Types, Data Structures, and Visualization I
- Numeric, character, logical, factor, dates
- Vectors, lists, data frames; reading data
- Building plots by layers with ggplot2
- Readings: MD Ch. 2; AAG Ch. 3, 4, 7.1, 9, 10, 27, 28; VT Ch. 3–4
Assignment 1 released
Due: Group formation survey, Sun Sep 20
Week 3 (Sep 24): Functions and Visualization II
- Writing and reading functions
- Histograms, facets, boxplots; principles of analytic graphics
- Readings: AAG Ch. 7.2, 11; VT Ch. 5–6
GitHub setup guide posted
Week 4 (Oct 1): Data Wrangling with dplyr
- Operating on rows, columns, and groups
- Bar plots
- Readings: AAG Ch. 14–16; VT Ch. 7; MD Ch. 3
Part II: Causality, Prediction, and Regression
Week 5 (Oct 8): Causality
- What is a causal effect?
- Randomized experiments; calculating effects
- Observational studies
- Readings: QSS Ch. 2.1–2.5
Due: Assignment 1
Due: Milestone 1: GitHub repository, Sun Oct 11
Week 6 (Oct 15): Individual or team meetings by appointment (no class meeting)
- With the causal-inference concepts in hand, discuss your research question and data before the proposal is due
Week 7 (Oct 22): Relationships, Importing, and Tidying Data
- Z-scores and correlation
- Pivoting longer; joining datasets
- Readings: AAG Ch. 11; MD Ch. 4; QSS Ch. 3.5–3.6
Week 8 (Oct 29): Prediction and Iteration
- Predicting election outcomes
- Loops; evaluating predictions; time-series plots
- Readings: AAG Ch. 4.7, 12; VT Ch. 8–9
Week 9 (Nov 5): Regression and Model Fit
- Modeling with a line; linear regression in R
- Model fit
- Readings: MD Ch. 5; QSS Ch. 4.1, 4.2.1–4.2.4
Course withdrawal period Nov 2–20
Due: Assignment 2
Week 10 (Nov 12): More on Regression
- Multiple regression
- Categorical predictors
- Readings: MD Ch. 6.1–6.2; QSS Ch. 4.2.6–4.3.2
Problem Set 2 (IMS Ch. 8) released
Due: Problem Set 1, Sun Nov 15
Due: Milestone 3: first visualization, Sun Nov 15
Part III: Statistical Uncertainty
Week 11 (Nov 19): Sampling and Sampling Distributions
- Sampling framework
- Random variables; the normal and the central limit theorem
- Readings: MD Ch. 7
Due: Problem Set 2, Sun Nov 22
Week 12 (Nov 26): The Bootstrap and Confidence Intervals
- Resampling; bootstrap CIs for means and ATEs
- Computing and interpreting CIs
- Readings: AAG Ch. 18–19; MD Ch. 8; IMS Ch. 12
Week 13 (Dec 3): Hypothesis Testing
- Hypothesis tests with infer; two-sample and permutation tests
- Issues with hypothesis testing; power
- Readings: MD Ch. 9; IMS Ch. 11
Problem Set 3 (IMS Ch. 11–12) released
Team consultations Nov 30–Dec 4: book a slot via Google Calendar
Due: Milestone 4: first analysis, Sun Dec 6
Week 14 (Dec 10): Models of Uncertainty
- Using the normal for inference
- CIs for experiments
- Readings: IMS Ch. 13
Due: Assignment 4
Due: Problem Set 3, Sun Dec 13
Week 15 (Dec 17): Inference for Regression
- Uncertainty for regression coefficients
- Presenting OLS regressions
- Readings: QSS Ch. 7.3
Part IV: Presentations
Week 16 (Dec 24): Team Presentations and Q&A
- Each team presents for 15 minutes, followed by Q&A; exact slots depend on the number of teams
Due: Assignment 5, Sun Dec 27
Lecture Slides & Handouts
- Slides and handouts are distributed on eeclass to enrolled students (not posted here).
- Week 1: Course Introduction and R Programming Basics
- Week 2: Data Types, Data Structures, and Visualization I
- Week 3: Functions and Visualization II
- Week 4: Data Wrangling with dplyr
- Week 5: Causality
- Week 7: Relationships, Importing, and Tidying Data
- Week 8: Prediction and Iteration
- Week 9: Regression and Model Fit
- Week 10: More on Regression
- Week 11: Sampling and Sampling Distributions
- Week 12: The Bootstrap and Confidence Intervals
- Week 13: Hypothesis Testing
- Week 14: Models of Uncertainty
- Week 15: Inference for Regression
Textbooks
Readings are assigned from the following books (see the syllabus for the week-by-week list):
- [MD] Ismay, Chester and Albert Y. Kim. 2022. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse.
- [QSS] Imai, Kosuke and Nora Webb Williams. 2022. Quantitative Social Science: An Introduction with Tidyverse. Princeton University Press.
- [IMS] Çetinkaya-Rundel, Mine and Johanna Hardin. 2021. Introduction to Modern Statistics. OpenIntro.
- [AAG] Lander, Jared P. 2017. R for Everyone: Advanced Analytics and Graphics, 2nd ed. O’Reilly.
- [VT] Yau, Nathan. 2011. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley.
Software
- R and RStudio Desktop. Visual Studio Code is fine as an editor.
- Quarto (bundled with RStudio) for reproducible reports; git and GitHub for the final project.
- AI tools (ChatGPT, Claude, Gemini, agentic coding assistants) are permitted and expected; see the AI use policy.
Grading
| Component | Weight |
|---|---|
| Final project (report, presentation Q&A, milestones, peer evaluation) | 50% |
| Assignments (5) | 15% |
| Problem sets (3) | 10% |
| In-class exercises (5) | 5% |
| Attitude / participation | 10% |
| Attendance | 10% |
Within the final project: written report, presentation with Q&A, four milestones, and peer evaluation. A rubric is provided on eeclass.
Final Project
A team research project held to a research-grade standard: self-collected or non-trivial data, an explicit identification argument, and analysis carried to publishable-style depth. The deliverable is a portfolio-ready article or webpage with a linked GitHub repository containing all code. Week 6 is reserved for individual or team meetings by appointment, for feedback on your research question and data before the proposal is due.
| Milestone | Due |
|---|---|
| GitHub repository | Sun Oct 11 |
| Data and proposal | Sun Oct 25 |
| First visualization | Sun Nov 15 |
| First analysis | Sun Dec 6 |
| Final report | Sun Dec 20 |
| Presentation and Q&A | Thu Dec 24 |
Assignments
Five assignments, each a short Quarto report on a provided dataset applying the method just covered (data wrangling and visualization, causality, regression and prediction, the bootstrap, hypothesis testing). Late submissions lose 1 percentage point of the assignment’s weight per day.
Problem Sets
Three problem sets drawn from Introduction to Modern Statistics (Ch. 7; Ch. 8; Ch. 11–12), submitted as a single PDF.
In-Class Exercises
Five short exercises done during class without generative AI and uploaded to eeclass the same day. Each ends with a verification step: check what your code actually did before you trust the output.
Course Policies
AI Use Policy
Students are permitted to use AI tools, including large language models and agentic coding assistants (e.g., ChatGPT, Claude, Gemini), for assignments and the final project. You are fully responsible for everything you submit. The final project is designed so that a high-quality result requires genuine understanding; AI accelerates competent work but does not substitute for it. However, it is imperative that the final submission predominantly reflects the student’s understanding and personal input. Students who use AI tools must adhere to the following guidelines:
- Thorough explanation: provide a detailed explanation of how the AI tool was used in completing the work.
- Original AI responses: submit the original responses generated by the AI tool alongside your submission.
- Critical evaluation and personal input: critically evaluate the AI-generated content and clearly indicate the portions modified or expanded with your own insights and understanding.
- Proper attribution: give proper attribution to AI-generated content, clearly indicating the sections that are AI-generated.
Attempting to cheat the system by relying heavily on AI-generated content without substantial personal input is ultimately the student’s loss, as it undermines the learning process.
In line with NTHU’s Implementation Guidelines on AI Ethics Statements and Learning Assessment (Sep 2026): if AI use is identified but not disclosed as required, the work may be re-evaluated or assigned no credit. In-class exercises are completed without generative AI. When the instructor uses AI to produce teaching materials or learning resources, this is indicated in the materials. By enrolling in this course, students are considered to have agreed to this statement.
Academic Honesty
All submitted work must be your own. A first violation results in a zero for that assignment; subsequent violations result in a failing grade. Cheating, plagiarism, self-plagiarism, and misrepresentation of authorship all count. Submissions may be checked with Turnitin.
Attendance and Participation
Attend every class with your laptop. Attendance is recorded from Week 2 and counts for 10%; each missed class costs about 1% of the final grade. Notify the instructor or a TA before class if you must miss it. Participation is assessed through in-class discussion and the eeclass discussion board.