1. GENERAL INFORMATION

Item Details
Course Title Data Analysis with Spreadsheet Program
Course Number  
Course Type Elective
Credits 3
Lecture Hours 15 hours (1 hour/week)
Tutorial/Lab Hours 30 hours (2 hours/week)
Self-study Hours 90 hours
Total Contact Hours 45 hours
Duration 15 weeks (1 week per topic)
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

Dr. Trong-Nghia Nguyen

Email: nghiant@neu.edu.vn

Website: https://nghianguyen7171.github.io/

Office Hours: To be determined

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

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 + 7 lab assignments
Midterm Exam Week 8 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. THREE-PHASE COURSE STRUCTURE & 15-WEEK SCHEDULE

PHASE 1: FOUNDATIONS (Weeks 1-4)

Objective: Master Excel basics, data import, and cleaning for robust data preparation

Week Main Topic Key Content Readings CLOs Activities Assessment
1 Course Introduction & Excel Basics Course overview, analytics workflow, Excel interface, basic formulas (SUM, AVERAGE, COUNT, IF) [1] Ch. 1 CLO 1 Lecture/demo, Q&A session Participation
2 Data Import & Preparation Basics Import from CSV/Excel/web, data structure, handling missing values, removing duplicates [1] Ch. 1-2; [2] Ch. 6 CLO 1 Import lab, hands-on practice Homework 1
3 Power Query Fundamentals Query editor, filter/sort/transform, consolidate multiple sources, refresh strategies [1] Ch. 2-3 CLO 1 Power Query lab, build queries Homework 2
4 Exploratory Data Analysis I Descriptive statistics (mean, median, quartiles, std dev), summary tables, segmentation [1] Ch. 4; [2] Ch. 8 CLO 2 Stats lab, interpret summaries Homework 3

Phase 1 Skills Checkpoint: Students able to import data and perform basic EDA

PHASE 2: ANALYSIS & VISUALIZATION (Weeks 5-11)

Objective: Develop analytical and visualization skills; master statistical methods and modern Excel tools

Week Main Topic Key Content Readings CLOs Activities Assessment
5 Data Visualization Basics Chart types, design principles, preattentive attributes, color use, accessibility [1] Ch. 9 CLO 4 Create multiple charts, redesign Homework 4
6 Dashboard Fundamentals Dashboard layout, interactive controls (slicers, timelines), KPI indicators, storytelling [1] Ch. 28-29 CLO 4,5 Build interactive dashboard, case study Homework 5
7 Statistical Analysis & Hypothesis Testing Probability distributions, hypothesis testing, t-tests, ANOVA, interpreting p-values, confidence intervals [2] Ch. 2-4 CLO 3 Hypothesis testing lab, case examples Homework 6
8 Midterm Exam Review sessions, exam preparation, computer-based test Review materials CLO 1-3 Midterm exam (90 min) Midterm Exam
9 Regression Analysis Simple & multiple regression, interpretation, assumptions, diagnostics [2] Ch. 5-6 CLO 3 Regression lab, model building Homework 7
10 Time Series & Forecasting Time series decomposition, trend analysis, forecasting methods [1] Ch. 10-11 CLO 2,3 Time series decomposition, forecasting Lab work
11 Power Pivot & Data Modeling Power Pivot basics, create relationships, DAX fundamentals, advanced dashboards [1] Ch. 5-7 CLO 1,4 Build data model, DAX measures Lab work

Phase 2 Skills Checkpoint: Students able to conduct complete analysis and create professional visualizations

PHASE 3: INTEGRATION & PROFESSIONAL COMMUNICATION (Weeks 12-15)

Objective: Apply all skills to real-world projects; communicate insights professionally; develop independence

Week Main Topic Key Content Readings CLOs Activities Assessment
12 Professional Communication & Reporting Report structure, writing for audiences, presenting insights, ethical visualization, documentation [1] Ch. 11; [3] Ch. 10 CLO 5,6 Write executive summary, practice presentation Homework 8
13 Integrated Project Work I Project planning, select dataset, data wrangling, initial exploration Project guidelines CLO 1-6 Lab: Project setup, instructor consultation Project checkpoint
14 Integrated Project Work II Complete analysis, build visualizations/dashboards, write findings, peer review Project guidelines CLO 1-6 Lab: Report finalization, peer feedback Project refinement
15 Final Presentations & Course Wrap-up Student presentations (15 min each), Q&A, course reflection, future learning paths Review syllabus CLO 5,6 Presentations, evaluation, reflection Final Presentation

Phase 3 Skills Checkpoint: Students execute complete end-to-end data analysis projects independently

8. WEEKLY SCHEDULE & CONTACT HOURS

  • Lecture: 1 hour/week (Time TBD)
  • Lab/Tutorial: 2 hours/week (Hands-on practice in computer lab)
  • Self-Study: 6 hours/week (Homework, reading, project work)
  • Total: 9 hours engagement per week × 15 weeks = 135 hours

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 8): 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 TA: Available for lab troubleshooting and homework help

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

Components:

  • Data Analysis (40%): Clean, explore, analyze data using Excel tools
  • Visualizations & Dashboard (30%): Create effective charts and interactive dashboard
  • Report (20%): 2,000-2,500 words with findings, insights, recommendations
  • Presentation (10%): 15-minute talk + 5-minute Q&A

Timeline:

  • Week 13: Project planning and data wrangling (Phase 3 start)
  • Week 14: Analysis and report writing
  • Week 15: Final presentation and submission

Datasets: Provided list or student-selected (instructor approval required)

13. KEY LEARNING COMPETENCIES BY PHASE

Phase 1: Foundations

Competency Skills Assessments
Technical Proficiency Excel navigation, formulas, data structures Homework 1-3
Data Preparation Importing, cleaning, transforming raw data Homework 2-3

Phase 2: Analysis & Visualization

Competency Skills Assessments
Analytical Thinking Pattern recognition, hypothesis testing, modeling Midterm exam, Homework 6-7
Visualization Design Creating effective charts and dashboards Homework 4-5
Statistical Reasoning Understanding distributions, tests, regression, forecasting Homework 6-7, Midterm

Phase 3: Integration & Communication

Competency Skills Assessments
Project Management Planning, executing, documenting analytics projects Weeks 13-15 project
Professional Communication Writing reports and presenting findings clearly Homework 8, Final presentation
Independent Learning Self-directed analytics work on real datasets Final project

14. COURSE CONTACTS & RESOURCES

Primary Instructor:

Name: Dr. Trong-Nghia Nguyen
Email: nghiant@neu.edu.vn
Website: https://nghianguyen7171.github.io/
Office: Room 1613, Building A1, National Economics University
Office Hours: [Days & Times - To be determined]

Teaching Assistant:

Name: [TA Name]
Email: ta.email@neu.edu.vn
Lab Hours: [Times]

Course Website: Canvas LMS [Course Link]
Emergency Contact: [Department Phone Number]

15. SYLLABUS POLICIES & DISCLAIMERS

  • This syllabus is subject to change at instructor discretion
  • Significant changes will be announced in class and on Canvas
  • Students are responsible for checking Canvas regularly for updates
  • In case of emergency, course format may shift to online/hybrid delivery

Version: 1.0 (Simplified Edition - Phase-Integrated Schedule)
Effective Date: Fall 2025
Last Updated: November 2025

This syllabus follows National Economics University academic standards and quality assurance guidelines for higher education programs.