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 practical course that equips students with essential skills in data-driven decision-making using Microsoft Excel. The course covers the complete data analytics workflow: importing and cleaning data, exploratory data analysis, statistical analysis, visualization, and professional reporting of insights.

Students develop proficiency in modern Excel capabilities including Power Query, PivotTables, and Power Pivot. Through hands-on labs and a capstone group project, students learn to transform raw data into actionable business insights and communicate findings to diverse audiences.

3. Course objectives

• G1. Master Excel data tools — import, clean, and transform data efficiently.
• G2. Conduct exploratory analysis — detect patterns, summarize data, visualize insights.
• G3. Apply statistical methods — test hypotheses, build models, interpret results.
• G4. Communicate findings professionally — create dashboards, reports, and presentations.
• G5. Execute complete analytics projects — end-to-end workflow with real data.

4. Course learning outcomes

Students who successfully complete this course will be able to:

5. Assessment

Participation & Homework — 20%. Online submission. Correctness, completeness, and clear documentation.

Midterm Exam — 30%. 90-minute computer-based test. No make-up without medical or emergency documentation.

Final Project — 50%. Data analysis (40%), visualizations and dashboard (30%), written report (20%), presentation (10%).

Notes

• Lab weeks include group progress presentations that contribute to the Final Project evaluation.
• Students work in groups of 3–4 and select one of 11 available project topics at the start of the semester.
• Each progress report (Weeks 2, 4, 6, 8, 11) builds toward the final presentation in Week 15.

6. Schedule pattern

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

• Lecture weeks (1, 3, 5, 7, 9, 12): 2-hour sessions introducing core concepts.
• Lab weeks (2, 4, 6, 8, 11, 13, 14, 15): 2-hour hands-on sessions plus group progress presentations.
• Week 10: 90-minute computer-based midterm exam.

Each lab week includes a group progress presentation deliverable. Groups select a project topic in Week 2 and build toward the final presentation in Week 15, following a scientific research workflow.

7. Policies

Attendance. Minimum 80% attendance required. More than two absences may impact the participation grade.

Submission. Homework is due by midnight at the end of the week posted. Late submissions are penalised 10% per 24 hours, to a maximum of 48 hours late. After 48 hours, the assignment scores zero.

Eligibility. Students must achieve at least 5 points for class participation to be eligible for the final assessment, per university regulations.

Classroom conduct.

• Complete readings before each class and take thorough notes.
• Submit assignments on time and in the required format (Excel files with clear documentation).
• Laptops and phones are for educational use only during class.
• Group project contributions are individually assessed. Free-riding is penalised.

Academic integrity. All work must be original and properly cited. Plagiarism or unauthorised collaboration results in a zero for that assignment. Repeated violations are reported to Student Affairs.

8. Required software

• Microsoft Excel (Microsoft 365 recommended; Excel 2021 minimum)
• Google Sheets (acceptable alternative for most exercises)
• Microsoft account and Canvas/LMS login
• Excel Online for cloud-based collaboration

9. Learning resources

Main textbook

• George Mount (2023). Modern Data Analytics in Excel: Using Power Query, Power Pivot, and Dynamic Arrays. O'Reilly Media.

Supplementary references

• Dan Remenyi, George Onofrei, Joseph English (2009). An Introduction to Statistics using Microsoft Excel. Academic Publishing International.

• H. Barreto (2021). Business Analytics with Excel — Gateway to Business Analytics. PALNI Open Press (open access).

• S. Christian Albright, Wayne L. Winston (2017). Data Analysis and Decision Making with Microsoft Excel. Cengage Learning.

10. Final project

Objective: Apply all course skills to a real dataset and communicate findings professionally.

Students work in groups of 3–4 and select one of 11 available topics. Each topic includes a curated dataset and a problem statement with research questions.

Components

• Data analysis (40%): Clean, explore, and analyse data using Power Query, PivotTables, Excel formulas, and statistical tools.
• Visualisations & dashboard (30%): Create effective charts and an interactive Excel dashboard.
• Report (20%): Clear, concise written report with findings, methodology, and recommendations.
• Presentation (10%): 15-minute group presentation + 5-minute Q&A.

Progress milestones