1. GENERAL INFORMATION
| Item | Details |
|---|---|
| Course Title | Data Analysis with Spreadsheet Program |
| Course Number | |
| Course Type | Elective |
| Credits | 3 |
| Lecture Hours | 12 hours (alternating weeks: 1, 3, 5, 7, 9, 12) |
| Tutorial/Lab Hours | 16 hours (alternating weeks: 2, 4, 6, 8, 11, 13, 14, 15) |
| Self-study Hours | 90 hours |
| Total Contact Hours | 29.5 hours (including midterm exam) |
| Duration | 15 weeks (alternating lecture/lab schedule) |
| Prerequisite | Basic Computer Skills; Introduction to Statistics (recommended) |
2. DEPARTMENT AND INSTRUCTORS
Department: Faculty of Data Science and Artificial Intelligence, College of Technology, National Economics University
Office Address: Room 1613, Building A1, National Economics University
Course Instructor
Dr. Trong-Nghia Nguyen
Email: nghiant@neu.edu.vn
Website: https://nghianguyen7171.github.io/
Office Hours: To be determined
Tutorial
MSc. Khanh-Le Duy
Email: khanhld@neu.edu.vn
Website: Faculty Profile
Role: Lab troubleshooting and homework help
3. COURSE DESCRIPTION
Data Analysis with Spreadsheet Program is a practical course designed to equip students with essential skills in data-driven decision making using spreadsheet tools. 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 spreadsheet capabilities including PivotTables, Power Query, and Power Pivot. Through hands-on labs and a capstone project, students learn to transform raw data into actionable business insights and communicate findings effectively to diverse audiences.
4. LEARNING RESOURCES
Main Textbook
Modern Data Analytics in Excel: Using Power Query, Power Pivot, and Dynamic Arrays
Author: George Mount
Publisher: O’Reilly Media, Inc. | Year: 2023
Access: O’Reilly Learning Platform
Supplementary Textbooks (Free/Open Access)
-
An Introduction to Statistics using Microsoft Excel (Remenyi, Onofrei, English)
Access: PDF Download -
Business Analytics with Excel (H. Barreto) - PALNI Open Press
Access: Gateway to Business Analytics -
Data Analysis and Decision Making with Microsoft Excel (Winston & Albright) - Selected chapters
Access: PDF Download
Reference Resources
-
Microsoft Excel Training Videos
Access: Microsoft Excel Video Training -
GitHub Excel Datasets for Data Analytics Beginners
Access: Practice Datasets for Excel -
Canvas/LMS
Access: Course-specific link (provided in class) - Assignment submission and resources -
Kaggle Datasets
Access: Kaggle Datasets - Practice datasets for data analysis -
World Bank Open Data
Access: World Bank Data - Global datasets for analysis projects
5. COURSE GOALS & LEARNING OUTCOMES
Main Learning Goals
| No. | Goal | Focus |
|---|---|---|
| G1 | Master Excel data tools | Import, clean, 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, presentations |
| G5 | Execute complete analytics projects | End-to-end workflow with real data |
Core Learning Outcomes
- CLO 1: Import, clean, and prepare data from multiple sources
- CLO 2: Perform exploratory data analysis using descriptive statistics and visualizations
- CLO 3: Conduct basic statistical tests and build regression models
- CLO 4: Create effective data visualizations and interactive dashboards
- CLO 5: Write professional reports and present analytical findings
- CLO 6: Execute complete data analysis projects independently
6. COURSE ASSESSMENT
| Assessment Type | Timing | Weight | Description |
|---|---|---|---|
| Participation & Homework | Weekly | 20% | Class engagement + lab assignments |
| Midterm Exam | Week 10 | 30% | Computer-based exam (90 min) |
| Final Project | Weeks 13-15 | 50% | Complete analysis + report + presentation |
Grading Scale
| Grade | Score Range | Interpretation |
|---|---|---|
| A | 90-100 | Excellent |
| B | 80-89 | Good |
| C | 70-79 | Satisfactory |
| D | 60-69 | Passing |
| F | <60 | Failing |
7. SIMPLIFIED 15-WEEK SCHEDULE (Alternating Lecture/Lab with Group Project Focus)
Schedule Pattern: Lectures and Labs alternate. Lecture weeks introduce core concepts and techniques that apply to all project topics. Lab weeks include hands-on practice with these concepts and structured group progress reports that follow a scientific research workflow. Groups select a project topic from the 11 available options and work on it throughout the semester, applying concepts learned in lectures. Each progress report builds toward answering specific research questions related to their chosen topic’s problem statement.
| Week | Type | Main Topic | Key Content | Readings | CLOs | Assessment |
|---|---|---|---|---|---|---|
| 1 | Lecture | Course Introduction & Excel Basics | Course overview, analytics workflow, Excel interface, essential formulas (SUM, AVERAGE, COUNT, IF, VLOOKUP) Materials: Week 1 Slides · Lab 1 Instructions · Week 1 Quiz |
[1] Ch. 1 | CLO 1 | Participation |
| 2 | Lab | Excel Basics Practice | Hands-on practice with formulas and functions; Group Progress Report 1: Topic selection, problem statement, research questions, and initial data exploration plan Lab 01 Answers |
[1] Ch. 1 | CLO 1 | Lab Assignment 1 + Group Progress Report 1 |
| 3 | Lecture | Data Import & Cleaning | Import from CSV/Excel/web, Power Query basics, data cleaning, handling missing values Materials: Week 3 Slides · Lab 2 Instructions · Lab 2 Dataset (Google Drive) |
[1] Ch. 2-3 | CLO 1 | - |
| 4 | Lab | Data Import & Cleaning Practice | Import and clean datasets using Power Query; Group Progress Report 2: Data quality assessment, cleaning methodology, and preliminary data structure documentation Materials: Lab 2 Instructions · Lab 2 Dataset (Google Drive) |
[1] Ch. 2-3 | CLO 1 | Lab Assignment 2 + Group Progress Report 2 |
| 5 | Lecture | Exploratory Data Analysis | Descriptive statistics, summary tables, data segmentation, PivotTables | [1] Ch. 4; [2] Ch. 8 | CLO 2 | - |
| 6 | Lab | EDA Practice | Perform descriptive analysis and create PivotTables; Group Progress Report 3: Descriptive statistics summary, key patterns/insights relevant to research questions, and identification of variables for further analysis | [1] Ch. 4 | CLO 2 | Lab Assignment 3 + Group Progress Report 3 |
| 7 | Lecture | Data Visualization & Dashboards | Chart types, design principles, effective visualizations, interactive dashboards | [1] Ch. 9, 28-29 | CLO 4 | - |
| 8 | Lab | Visualization Practice | Create charts and build interactive dashboards; Group Progress Report 4: Visual analysis of key research variables, preliminary dashboard prototype, and visual insights that address specific research questions | [1] Ch. 9 | CLO 4 | Lab Assignment 4 + Group Progress Report 4 |
| 9 | Lecture | Statistical Analysis & Regression | Hypothesis testing basics, simple regression, interpreting results | [2] Ch. 2-6 | CLO 3 | - |
| 10 | Midterm | Midterm Exam | Comprehensive exam covering weeks 1-9 (Excel basics, data import, EDA, visualization, statistics) | Review materials | CLO 1-4 | Midterm Exam (30%) |
| 11 | Lab | Statistical Analysis Practice | Apply statistical tests and build regression models; Group Progress Report 5: Statistical hypotheses, analysis results (correlations, regression models, or survival analysis), and interpretation of findings in context of research problem | [2] Ch. 5-6 | CLO 3 | Lab Assignment 5 + Group Progress Report 5 |
| 12 | Lecture | Professional Reporting & Communication | Report structure, writing for audiences, presenting insights effectively | [1] Ch. 11; [3] Ch. 10 | CLO 5 | - |
| 13 | Lab | Final Project Work I | Complete analysis, build visualizations/dashboards, write report drafts; Project Checkpoint: Full analysis results, comprehensive dashboard addressing all research questions, draft report with methodology and findings sections | Project guidelines | CLO 1-6 | Project Checkpoint |
| 14 | Lab | Final Project Work II | Refine analysis, finalize dashboards, complete written reports; Peer review and feedback | Project guidelines | CLO 1-6 | Project Refinement |
| 15 | Lab | Final Presentations | Student group presentations (15 min each), Q&A, course wrap-up | - | CLO 5,6 | Final Presentation (50%) |
Schedule Notes:
- Weeks 1, 3, 5, 7, 9, 12: Lecture sessions (concise but comprehensive conceptual learning applicable to all project topics)
- Weeks 2, 4, 6, 8, 11, 13, 14, 15: Lab sessions (hands-on practice + group project reporting)
- Week 10: Midterm Exam (90 minutes, computer-based)
- Group Project Work: Students form groups (3-4 members) and select one of 11 available project topics. Groups work on their chosen topic throughout the semester, following a structured research workflow. Each progress report (Weeks 2, 4, 6, 8, 11) has specific deliverables linked to scientific research milestones: problem formulation → data preparation → exploratory analysis → visual analysis → statistical analysis. Groups build toward final presentation in Week 15, demonstrating how their analysis addresses the research problem and answers their research questions.
8. WEEKLY SCHEDULE & CONTACT HOURS
Alternating Schedule:
- Lecture Weeks (Weeks 1, 3, 5, 7, 9, 12): 2 hours of lecture (Time TBD)
- Lab Weeks (Weeks 2, 4, 6, 8, 11, 13, 14, 15): 2 hours of hands-on lab practice (Time TBD)
- Midterm Week (Week 10): 90-minute exam
- Self-Study: 4-6 hours/week (Homework, reading, project work)
- Total Contact Hours: 6 lectures × 2 hours + 8 labs × 2 hours + 1.5 hours (midterm) = 29.5 hours
- Total Engagement: ~135 hours (including self-study)
9. COURSE POLICIES
Attendance & Participation
- Minimum 80% attendance required
- Active participation in labs and discussions expected
- More than 2 absences may impact participation grade
Assignment Submission
- Homework due by midnight (end of week posted)
- Late submissions: -10% per 24 hours (max 48 hours late)
- Format: Excel files with clear documentation and comments
Midterm & Final Exams
- Midterm (Week 10): 90-minute computer-based test
- Final: 15-minute presentation + written project report
- No make-up exams without medical/emergency documentation
Academic Integrity
- All work must be original and properly cited
- Plagiarism/unauthorized collaboration = 0 grade for assignment
- Repeated violations reported to Student Affairs
Accommodations for Students with Disabilities
- Register with Student Services for formal accommodations
- Contact instructor early with documented needs
- Accommodations: extended exam time, assistive technology, note-taking support
10. LEARNING SUPPORT & RESOURCES
Instructor Support
- Office Hours: 2x per week (schedule on Canvas)
- Email: nghiant@neu.edu.vn
- Consultation: By appointment via Zoom or in-person
Learning Materials
- Weekly lecture slides and recorded videos on Canvas
- Real datasets for practice: Kaggle, World Bank, OECD, Vietnam Statistics Office
- Excel templates and starter files for all labs
- Case studies and reference articles
Additional Support
- University Writing Center: Report writing and editing
- Library Services: Data sourcing and research support
- Course Tutorial (MSc. Khanh-Le Duy): Available for lab troubleshooting and homework help
- Email: khanhld@neu.edu.vn
- Office Hours: To be determined
11. REQUIRED TECHNICAL SETUP
- Software: Microsoft Excel (Microsoft 365 recommended; Excel 2021 minimum)
- Hardware: Computer with minimum 4GB RAM, stable internet connection
- Accounts: Microsoft account, Canvas/LMS login
- Access: Excel Online for cloud-based collaboration
12. FINAL PROJECT OVERVIEW
Objective: Apply all course skills to a real dataset and communicate findings professionally through a complete data analysis project.
Project Structure:
Students work in groups (3-4 members) and select one of 11 available topics for their final project. Each topic is aligned with specific course knowledge areas and includes a curated dataset with instructions. See the Topics page for detailed information on all available topics.
Components:
- Data Analysis (40%): Clean, explore, analyze data using Excel tools (Power Query, PivotTables, formulas, statistical analysis)
- Visualizations & Dashboard (30%): Create effective charts and interactive dashboard using Excel visualization tools
- Report (20%): 2,000-2,500 words with findings, insights, recommendations, properly structured and cited
- Presentation (10%): 15-minute group presentation + 5-minute Q&A
Timeline & Group Reporting:
Week 2 (Lab) - Progress Report 1: Project Initiation & Problem Formulation
Deliverables:
- Selected topic and dataset justification
- Clear problem statement linked to the chosen topic’s research context
- 3-5 specific research questions that address the problem
- Initial data exploration plan (what variables will be analyzed and why)
- Group work plan and role assignments
Format: 1-2 page document (Word/PDF) + Excel file with initial data overview
Week 4 (Lab) - Progress Report 2: Data Preparation & Quality Assessment
Deliverables:
- Data quality report (missing values, outliers, inconsistencies identified)
- Documented data cleaning methodology (decisions made and rationale)
- Cleaned dataset with data dictionary (variable definitions, types, ranges)
- Preliminary data structure analysis (shape, key variables, relationships)
- Challenges encountered and solutions applied
Format: Excel file with cleaned data + 1-2 page methodology document
Week 6 (Lab) - Progress Report 3: Exploratory Data Analysis & Pattern Discovery
Deliverables:
- Descriptive statistics summary relevant to research questions
- Key patterns and insights discovered through EDA
- Identification of important variables for addressing research questions
- PivotTables summarizing critical dimensions
- Preliminary findings that begin to answer research questions
Format: Excel file with EDA analysis + 2-3 page findings summary with tables
Week 8 (Lab) - Progress Report 4: Visual Analysis & Dashboard Prototype
Deliverables:
- Visualizations addressing specific research questions (minimum 5 charts)
- Preliminary interactive dashboard prototype
- Visual insights that support or challenge initial hypotheses
- Chart selection rationale (why each chart type was chosen)
- Dashboard layout and functionality demonstration
Format: Excel dashboard file + 2-page visual analysis document
Week 11 (Lab) - Progress Report 5: Statistical Analysis & Hypothesis Testing
Deliverables:
- Statistical hypotheses formulated based on research questions
- Results of statistical analyses (correlation, regression, survival analysis, etc.)
- Interpretation of findings in context of the research problem
- Model diagnostics and validation (if applicable)
- Statistical evidence supporting or refuting research hypotheses
Format: Excel file with analysis results + 3-4 page statistical analysis report
Week 13 (Lab) - Project Checkpoint: Comprehensive Analysis Review
Deliverables:
- Complete analysis addressing all research questions
- Comprehensive interactive dashboard with all key visualizations
- Draft written report (minimum 1500 words) including:
- Introduction and problem statement
- Methodology section
- Findings and results section
- Initial insights and implications
- Presentation outline/slides draft
Format: Complete Excel workbook + Draft report (Word/PDF) + Presentation slides
Week 14 (Lab) - Project Refinement: Final Report & Peer Review
Deliverables:
- Finalized written report (2,000-2,500 words) with:
- Complete methodology and analysis sections
- Comprehensive findings and interpretations
- Recommendations based on research findings
- Conclusions and limitations
- Final dashboard with all refinements
- Peer review feedback incorporated
- Presentation finalized and rehearsed
Format: Final Excel workbook + Complete report + Final presentation slides
Week 15 (Lab) - Final Presentations: Research Findings Communication
Deliverables:
- 15-minute group presentation covering:
- Problem statement and research questions
- Methodology overview
- Key findings and insights
- Recommendations and conclusions
- Q&A session (5 minutes)
- Final submission of all project materials
Format: Live presentation + Submission of all deliverables (Excel, report, slides)
Available Topics: 11 curated topics aligned with course curriculum (see Topics page for details). Groups select one topic that best fits their interests and career goals.