Syllabus

1. General information

Department: Faculty of Data Science and Artificial Intelligence
College: College of Technology, National Economics University
Office: Room 1613, Building A1, National Economics University

2. Course description

A hands-on introduction to data mining for business decision-making. The course equips students with business-analytic thinking: framing the right question, selecting the right technique, and interpreting results in actionable business language — not writing algorithms from scratch.

Students work through the full CRISP-DM lifecycle using Orange Data Mining (a no-code, drag-and-drop platform) supplemented by light Python/pandas scripts. Core techniques covered include data exploration and visualization, association rules, k-Means clustering, KNN and Naïve Bayes classification, and linear and logistic regression. Midterm assessments include a practical exam on Orange and a group project presentation; the final is a written exam.

3. Course objectives

• G1. Describe and explore business data through descriptive statistics and visualization.
• G2. Structure data mining projects using the CRISP-DM process from business understanding to deployment.
• G3. Apply core data mining techniques — association rules, clustering, classification, and regression — to business datasets.
• G4. Use Orange Data Mining and Python for practical, end-to-end data mining workflows.
• G5. Communicate analytical findings as clear, actionable business recommendations.

4. Course learning outcomes (CLOs)

Students who successfully complete this course will be able to:

5. Assessment

Attendance & Participation — 10%. Base score of 8.0/10 for full attendance without active participation. Each active participation adds +0.25 pts. More than 4 absences → score of 0 and ineligibility for the Final Exam. Surplus above 10.0 is halved and added to the Midterm grade.

Midterm Exam — 40%. Part 1 — load a dataset, preprocess, model, and interpret results in Orange (covers Weeks 1–7). Part 2 — group project on a self-selected dataset; present findings to the class (covers full course).

Final Exam — 50%. 90-minute written exam. Questions test technique selection for business scenarios, interpretation of model outputs, and understanding of CRISP-DM. Covers all 15 weeks.

Notes

• Group project: teams of 3–4 students; topic and dataset confirmed by Week 12.
• Two current-event presentations required (one before and one after the midterm exam).
• Each lab week includes a business memo (4–5 sentences interpreting analytical results in business language) — submitted alongside the Orange workflow.

6. Schedule pattern

The course follows an alternating lecture / lab pattern across 15 weeks.

• Lecture weeks (1, 3, 5, 7, 9, 11, 13): 90-minute sessions introducing concepts, techniques, and business applications.
• Lab weeks (2, 4, 6, 10, 12): 90-minute hands-on sessions in Orange or Python, each concluding with a business memo.
• Week 8: Midterm Exam 1 — 90-minute individual practical exam on Orange.
• Week 14: Midterm Exam 2 — group project presentations.
• Week 15: Final Exam — 90-minute written exam.

Each lab week produces two deliverables: an Orange workflow (or Python notebook) and a 4–5-sentence business memo interpreting the findings.

7. Group project

Midterm Exam Part 2 — 20% of the final grade.

Teams of 3–4 students choose a dataset and apply the full CRISP-DM process to a business question of their choice. The project culminates in a presentation to the class during Week 14.

Deliverables

• Week 12: Confirm team composition, topic, and dataset.
• Week 14: 10–12-minute presentation + Q&A. Submit slides and Orange workflow before the session.

Evaluation criteria

• Problem framing and CRISP-DM alignment (20%)
• Data preparation quality (20%)
• Technique selection and execution (30%)
• Interpretation and business memo (20%)
• Presentation clarity (10%)

8. Policies

Attendance. Random attendance checks. More than 4 absences removes eligibility for the Final Exam and sets the participation grade to zero.

Submission. Lab assignments are due by the end of the week following the lab session. A two-week grace period applies; each late week incurs a 25% penalty. No submission accepted after the grace period.

Electronics. Laptops are required for all lab sessions. During lectures, phones must be silent and non-educational device use is prohibited unless otherwise instructed.

Classroom conduct.

• Read the assigned chapter before each lecture session.
• Bring a fully charged laptop to every lab session.
• Submit lab assignments together with a business memo on time.
• Group project contributions are individually assessed. Free-riding is penalised.
• No plagiarism or unauthorised AI-generated content. Violations result in a zero for that component.

Academic integrity. All work must be original and properly cited. Plagiarism or unauthorised use of AI-generated content results in a zero for that component. Repeated violations are reported to Student Affairs.

9. Required software

• Orange Data Mining (orangedatamining.com) — primary tool; free, open-source, drag-and-drop; standalone installer bundles Python and all required libraries. No separate Python installation needed.
• Python 3 with pandas — accessed via VS Code (local) or Google Colab (browser-based, no install required); used for reading and adapting data scripts.
• Microsoft Excel or Google Sheets — used for early-stage descriptive statistics and the Excel data mining exercise.

Installation instructions are provided in Lab 0.

10. Learning resources

Main textbook

• Matthew North (2012). Data Mining for the Masses. Global Text Project / Textbook Equity (Creative Commons). Free PDF: download

Supplementary references

• Shmueli, Bruce, Gedeck & Patel (2019). Data Mining for Business Analytics: Concepts, Techniques and Applications in Python. Wiley.

• James, Witten, Hastie & Tibshirani (2023). An Introduction to Statistical Learning (2nd ed.). Springer (free PDF from authors).