BUS220: Business Intelligence and Analytics
2025/2026, Spring Trimester
Course Information
| Course faculty | Oleh Omelchenko (o_omelchenko@kse.org.ua) |
| Course assistants | Andrii Sidliarenko (asidliarenko@kse.org.ua) |
| Department | Computer Science Department |
| Study program | Economics and Big Data |
| ECTS credits | 4 (120 academic hours total) |
| Class hours | 48 academic hours: 16 lecture hours + 32 practice hours |
| Course language | English |
| Course format | Offline, full-time studies |
Overview
Prerequisites
This course assumes participants have successfully finished STAT230 Principles of Data Visualization and STAT163 Data Manipulation Essentials, or have equivalent experience with data manipulation and visualization.
Fundamental skills:
- Experience with Python/Pandas or R for data manipulation and cleaning
- Understanding of data types and structures of tabular data
- Understanding of key data visualization concepts, how to create standard charts in any data visualization tool or library
Nice to have skills:
- Spreadsheet experience (Excel or Google Sheets)
- Understanding of basic business metrics (revenue, costs, profit margins)
- Understanding of basic statistical principles (mean, distribution, outliers etc.)
Background and Course Rationale
While data visualization teaches how to make effective charts, and data manipulation teaches how to work with data programmatically, this course bridges these skills to answer how to turn data into insights that inform decisions.
Business Intelligence encompasses the tools, techniques, and practices used to transform raw data into actionable information. Whether you’re working in a corporation, non-profit, research institution, or government agency, the ability to prepare data, design meaningful metrics, build interactive dashboards, and communicate insights effectively is increasingly essential.
This course is designed for students who have foundational data and visualization skills and are ready to apply them in real-world analytical contexts. You’ll learn to think systematically about analytical problems: understanding what questions need answers, identifying relevant metrics, preparing data appropriately, building maintainable analytical solutions, and presenting findings to diverse audiences.
We focus on widely-accessible, cross-platform tools (Google Sheets, Tableau Public) to ensure all students can participate regardless of their computing environment. More importantly, you’ll learn principles and approaches that transfer across any analytics tools and contexts you encounter in your career.
Course Aims
This course aims to:
- Develop practical skills needed to work as an analyst in various organizational and research contexts
- Build proficiency in widely-used tools for data preparation, analysis, and visualization (spreadsheets, Python, Tableau)
- Teach systematic approaches to metrics design, KPI selection, and dashboard strategy
- Provide hands-on experience with realistic datasets across multiple domains
- Foster the ability to translate analytical questions into concrete solutions
- Develop communication skills for presenting data insights to diverse stakeholders
- Prepare students for analytics roles requiring end-to-end solution development
Learning Outcomes
By the end of the course, students will be able to:
- Use Advanced Spreadsheet Features: Apply pivot tables, complex formulas, data validation, and conditional formatting to perform data analysis and create interactive reports
- Prepare Data for Analysis: Clean, transform, and structure data from various sources using appropriate tools, handling common data quality issues encountered in real-world datasets
- Design and Calculate Metrics: Identify appropriate metrics and KPIs for different analytical scenarios and implement them using Tableau calculations, LOD expressions, and table calculations
- Build Interactive Dashboards: Create user-friendly dashboards in Tableau that address specific audience needs, applying dashboard design principles and information hierarchy
- Apply Domain-Specific Analytical Patterns: Understand common analytical approaches across different domains (marketing, sales, operations, finance) and select appropriate metrics and visualizations for each context
- Implement Advanced Tableau Techniques: Use parameters, calculated fields, sets, groups, table calculations, and Level of Detail (LOD) expressions to address analytical problems
- Communicate Data Insights: Present analytical findings clearly to both technical and non-technical audiences, translating data into actionable recommendations
- Develop Analytical Solutions: Design and implement solutions that span from raw data to final dashboard, demonstrating understanding of the analytical workflow
Course Structure
The course runs for 8 weeks with one lecture and two consecutive practice sessions per week. Topics within each week are contextually aligned, with practice sessions building directly on lecture concepts.
| Day | Session Type | Duration | Notes |
|---|---|---|---|
| Day 1 | Lecture | 80 min | Conceptual content, demonstrations |
| Day 2 | Practice 1 | 80 min | Hands-on exercises |
| Day 2 | Practice 2 | 80 min | Continued practice, graded submission |
Preliminary Materials: Before most lectures, students will be assigned required preparatory materials (readings, videos, tutorials). Students should review these materials before the lecture.
Lectures: Eight lectures, one per week. Starting from Week 2, each lecture begins with a graded quiz covering the previous week’s material.
Practice Sessions: Sixteen practice sessions (two per week) provide hands-on experience. Students submit their practice work (Tableau workbooks, spreadsheet files, or Python notebooks) through Moodle after each session.
Individual Assignments: Five assignments throughout the course. Assessed on both technical implementation (70%) and documentation/explanation (30%).
Group Project: A comprehensive analytical dashboard project (groups of 3-4 students). Includes a milestone submission in Week 6 and final presentations in Week 8.
Grading Policy
| Component | Points | Total |
|---|---|---|
| Lectures (graded quiz) | 6 weeks × 2 pts each | 12 |
| Practices | 7 weeks × 2 pts each | 14 |
| Assignments | 5 assignments × 8+2 pts each | 40+10 |
| Group Project | 35 pts | 35 |
| Total (capped at 100) | 111 max |
Graded Lecture Quizzes (12 pts total, 2 pts each)
Short interactive quizzes administered at the start of lectures via Moodle:
- Tests retention of previous week’s concepts
- 5-7 questions, 10 minutes total
- Must be present to participate
- Grading: 60%+ correct = 2 pts, 30-60% = 1 pt, <30% = 0 pts
Practice Submissions (14 pts total, 2 pts each)
- Evaluated on “satisfactory” basis — completion over perfection
- Must demonstrate understanding of core concepts
- Submit on Moodle within 3-day window
- Grading: Complete & correct approach = 2 pts, Missing/inadequate = 0 pts
Individual Assignments (40+10 pts total, 8+2 pts each)
Late Submission Impact:
- On time: 100% of earned grade
- 1-7 days late: 50% of earned grade
- 7+ days late: 0 points (not accepted)
Group Project (35 pts)
A comprehensive analytical dashboard developed by groups of 3-4 students.
Deliverables:
- Tableau dashboard (published to Tableau Public or submitted as .twbx file)
- Written documentation (data sources, metrics definitions, analytical approach, design decisions)
- Group presentation (10-15 minutes)
Grading Criteria:
- Data preparation and quality (7 pts)
- Metrics selection and implementation (7 pts)
- Dashboard design and usability (7 pts)
- Analytical insights and recommendations (7 pts)
- Presentation and documentation (7 pts)
Assignments Overview
| Assignment | Learning Objectives | Description |
|---|---|---|
| Assignment 1: Spreadsheet Analysis | LO1, LO7 | Analyze a business dataset using advanced spreadsheet features including pivot tables and complex formulas. Create an interactive analytical report with conditional formatting and document your analytical approach. |
| Assignment 2: Data Cleaning & Preparation | LO2, LO7 | Clean and transform a messy dataset with typical data quality issues using Python/pandas or spreadsheets. Document issues found and solutions applied, then prepare analysis-ready data for Tableau. |
| Assignment 3: Metrics Implementation | LO3, LO7 | Identify and calculate appropriate KPIs for a given business scenario. Implement metrics in Tableau using calculated fields, create visualizations showing trends, and justify your metric selection. |
| Assignment 4: Advanced Tableau Features | LO3, LO6 | Solve analytical problems requiring LOD expressions and table calculations. Create dynamic analysis using parameters and demonstrate understanding of calculation contexts. |
| Assignment 5: Complete Dashboard | LO4, LO5, LO7 | Design and build a comprehensive dashboard for a specific audience using appropriate domain-specific metrics. Apply interactivity, filters, and actions effectively while documenting design decisions. |
| Group Project | LO8 | A comprehensive analytical dashboard developed by groups of 3-4 students addressing a real-world analytical challenge. |
Schedule
| Week | Topics | Activities & Deadlines |
|---|---|---|
| 1 | Spreadsheet Fundamentals | Practice submission (2 pts) |
| Course overview & BI landscape. Role of spreadsheets in analytics. Data types, cell references, basic functions. Data validation and cleaning in spreadsheets. P1: Spreadsheet interface, basic formulas (SUM, AVERAGE, IF, COUNTIF). P2: Data cleaning techniques, sorting, filtering, removing duplicates. | Assignment 1 introduced | |
| 2 | Advanced Spreadsheets & Analytical Reports | Quiz 1 (2 pts), Practice (2 pts), Assignment 1 deadline (8+2 pts) |
| Pivot tables and cross-tabulation. Advanced formulas (VLOOKUP, INDEX/MATCH, array formulas). Conditional formatting. Creating analytical reports. P1: Pivot tables for multi-dimensional analysis. P2: Complex formulas for business calculations. | ||
| 3 | Data Preparation & Tableau Foundations | Quiz 2 (2 pts), Practice (2 pts) |
| Data preparation workflows. Cleaning data with Python/pandas. Introduction to OpenRefine (demo). Connecting Tableau to data sources. Joins, unions, blending. P1: End-to-end data cleaning (Python/pandas → CSV → Tableau). P2: Tableau connections, joins, unions. | Assignment 2 introduced | |
| 4 | Business Metrics & Dashboard Foundations | Quiz 3 (2 pts), Practice (2 pts), Assignment 2 deadline (8+2 pts) |
| Understanding business metrics and KPIs. Calculated fields in Tableau. Aggregations and granularity. Dashboard layout principles. Filters and interactivity. P1: Standard business metrics with calculated fields. P2: First interactive dashboard. | Assignment 3 introduced, Group project introduced | |
| 5 | Advanced Calculations | Quiz 4 (2 pts), Practice (2 pts), Assignment 3 deadline (8+2 pts) |
| Table calculations (running totals, percent of total, ranking, moving averages). LOD expressions (FIXED, INCLUDE, EXCLUDE). Calculation context and granularity. P1: Table calculations for time-series and ranking. P2: LOD expressions for complex questions. | Assignment 4 introduced | |
| 6 | Advanced Interactivity & Multi-view Dashboards | Quiz 5 (2 pts), Practice (2 pts), Assignment 4 deadline (8+2 pts) |
| Parameters for user control. Dashboard actions (filter, highlight, URL). Navigation between pages. Sets and set actions. P1: Parameters and dynamic calculations. P2: Multi-page dashboards with navigation. | Assignment 5 introduced | |
| 7 | Polish, Performance & Advanced Topics | Quiz 6 (2 pts), Practice (2 pts), Assignment 5 deadline (8+2 pts) |
| Visual design best practices and accessibility. Performance optimization. Tooltips and annotations. Special visualization types. Publishing considerations. P1: Dashboard refinement and UX. P2: Advanced analytical scenarios. | ||
| 8 | Integration and Project Presentations | Final Project deadline (35 pts) |
| Best practices review. Career pathways. Self-service BI and data governance basics. Group project presentations. |
Learning Materials
Textbooks:
- Big Book of Dashboards by Steve Wexler (O’Reilly link)
- Practical Tableau by Ryan Sleeper (O’Reilly link)
- Fundamentals of Data Visualization by Claus Wilke (Web)
- Tableau Public Documentation
- Excel/Google Sheets documentation and tutorials (specific links provided per week)
Tools & Software:
- Tableau Public (Desktop or Web version)
- Google Sheets
- Python/R for data manipulation and transformation
Make-Up Policy
Weekly Quizzes: Must be completed during lecture hours. Make-up opportunities may be granted for documented emergencies if reported before the lecture.
Practice Submissions: Must be submitted within a 3-day window after the practice day. Example: practice on Thursday → deadline is Sunday 23:59 Kyiv time.
Assignments: Submissions after deadline but within one week: 50% of earned points. More than one week late: not accepted.
Deadline Extensions: Must be requested at least 2 days before the deadline.
AI Tools Usage Policy
Encouraged:
- Using AI as a learning assistant to explore new concepts
- Asking clarifying questions about complex topics
- Generating example code snippets to understand specific functions
- Debugging assistance and error explanation
- Brainstorming alternative approaches
Prohibited:
- Using AI to complete entire assignments without understanding
- Submitting AI-generated solutions without significant personal modification
- Using AI for project presentations or documentation without disclosure
Documentation Requirements:
Unless otherwise specified, all AI usage must be documented including:
- Which AI tool was used
- Complete chat history or prompts used
- Clear identification of parts completed independently vs. with AI assistance
- Short summary of how AI-generated content was modified
Academic Integrity
Academic integrity is highly valued by the KSE community. We have a zero-tolerance policy towards academic plagiarism, self-plagiarism, fabrication, falsification, cheating, deception, bribery and other types of violations. Penalties may vary from receiving zero points for an assignment to expulsion from KSE. All rules and procedures are stated in the KSE Code of Academic Integrity.
Course Faculty
Oleh Omelchenko, M.Sc. in Telecommunications
Office hours: offline, every Wednesday, 16:00-18:00, KSE Dragon Capital Building. (also available online in Slack during working hours)
Professional Experience: 8 years of experience in data analytics and analytics development, specifically with web and mobile applications. Current position: Senior Product Analyst at MacPaw.
Teaching Philosophy: I believe in an approach that goes beyond predefined topics — during my courses we may touch topics that aren’t typically present in similar courses, chosen based on what students might expect in various working environments. I also expect students to be inquisitive during lectures and not hesitate to ask questions.