Before the first class Open

To be completed before Week 01 (4 September 2026).

The course brings together students with very different starting points. Some arrive with years of engineering practice, others have never written a line of code, and most sit somewhere in between. This short survey lets me see that distribution before we begin, so that the pace of the Python sessions, the depth of the statistical material, and the choice of worked examples can be set against the group that is actually in the room rather than an imagined average student.

The survey asks about four things:

• Background — your academic and professional field, and what brought you to analytics.
• Technical experience — prior exposure to Python, statistics, and machine learning.
• Research interests — the problems or industries you would like to work on in your individual project.
• Expectations — what you hope to be able to do by the end of the semester.

It takes about five minutes. Responses are used only to plan the course, and individual answers are not shared.

Open the survey

Responses are submitted through GitHub, so a GitHub account is needed to complete the form. If that is inconvenient, email your answers to me instead.

Introduction to Business Analytics — TIM51001

Graduate School of Technology and Innovation Management, UNIST · Autumn 2026 · Letter grade

This course examines the mechanisms of technological innovation in a rapidly changing science, technology, and policy environment. Using the theories and methods of business analytics, students learn research methodology built on academically structured frameworks and practise applying it with Python, developing an individual research or business analytics topic during the semester.

Objectives

Understand the mechanisms of technological innovation in a rapidly changing science, technology, and policy environment.

Frame business and research problems within academically structured analytical frameworks (IMRD) and connect them to appropriate data and methods.

Apply statistical, machine-learning, and network-analysis methods with Python and vibe coding.

Design an individual research project and present the results clearly in written and oral form.

Weekly schedule

Week	Date	Contents
01	4 Sep	Course Introduction
02	11 Sep	Introduction of AI/ML and Business Analytics
03	18 Sep	Introduction of AI/ML and Business Analytics
04	25 Sep	Chuseok Holiday — No Class
05	2 Oct	No Class — rescheduled to 9 October
06	9 Oct	Statistical Analysis I Make-up class
07	16 Oct	Statistical Analysis II
08	23 Oct	Research Proposal and Feedback
09	30 Oct	Research Proposal and Feedback
10	6 Nov	Machine Learning I
11	13 Nov	Machine Learning II
12	20 Nov	Network Analysis I
13	27 Nov	Network Analysis II
14	4 Dec	Individual Research Presentations
15	11 Dec	Individual Research Presentations
16	18 Dec	Final Exam

Assessment

Attendance and participation 10% · Final examination 40% · Individual report 30% · Individual presentation 20%. There are no team-based or group assignments.

Attendance is recorded weekly. Students must attend at least 75% of classes to earn credit; four or more absences result in an F grade. Absences are governed by university academic regulations, and the official absence form should be submitted in advance where possible.

Materials

There is no required textbook. Lecture materials are provided before each class, and a list of recommended references is shared during the semester. Suggested reading: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Géron, O'Reilly, 3rd ed., 2023).

The class of 2 October is cancelled and rescheduled as a make-up class on 9 October. The weekly schedule and topics are otherwise subject to change depending on class progress and university policy.