Basic Data Science in Economics and Business
FDA.6.1.2.01.V | 3 Credits | National Economics University
This course provides an introductory foundation in data science thinking and techniques for students in economics, finance, and business administration. It equips learners to leverage and analyze data for decision-making in business environments, covering the complete lifecycle of a data science project from data collection and cleaning to visualization, modeling, and reporting. The course emphasizes hands-on learning with real-world datasets through exercises and small projects.
Course Overview
📊 Course Information
- Course Code: FDA.6.1.2.01.V
- Credits: 3
- In-Class Hours: 45
- Self-Study Hours: 90
- Program: Undergraduate
📚 Prerequisites
- Mathematics for Economists
- Probability Theory and Mathematical Statistics (or Statistics in Economics and Business)
🎯 Course Objectives
- G1: Understand the roles and applications of data science in economics, finance, and marketing, and describe the basic steps in the lifecycle of a data science project.
- G2: Use Python programming to process tabular data, including writing statements with basic control structures and using NumPy and Pandas.
- G3: Understand processes for collecting, cleaning, and wrangling data from common sources, using transformations, combinations, and aggregations for analysis.
- G4: Apply visualization tools and reporting to build appropriate data visualizations aligned with analytical goals, and present and interpret results in reports using Markdown, PDF, or presentation formats.
- G5: Apply basic machine learning models such as linear regression, decision trees, random forests, gradient boosting, etc., to solve simple predictive tasks in business, and evaluate models with quantitative metrics such as MAE, RMSE, and R².
💻 Required Software
- Python 13.0 or higher
- Jupyter Notebook or Google Colab
- NumPy, Pandas, Matplotlib, Seaborn, scikit-learn
This Week (Week 6)
📌 Week Highlights
- 📊 Learn to read and write data in CSV, JSON, and Excel formats
- 🌐 Practice web scraping and data collection from websites
- 📖 Read Chapter 7 from the course textbook
- 📓 Download Lecture 4 notebook from course schedule
- 💻 Complete data input/output exercises
- 📤 Submit Homework 2 by Oct 16, 22:00
📢 Announcements
Homework 2 Due Soon!
2025-10-10
Homework 2 (Data Input and Storage) is due on Oct 16 at 22:00. Submit your .ipynb file via Google Drive. Practice with CSV, JSON, Excel, web scraping, and APIs.
Lecture 4 Quiz Available
2025-10-10
Quiz on Data Input and Storage is now available! Test your understanding of CSV, JSON, Web scraping, RSS feeds, APIs, and SQL databases. 30 questions with hints.
🗓️ Coming Up
- Week 7: Data Input and Storage Practice - Next week
- Week 8: Midterm 1 & Data Preprocessing - In 2 weeks
Instructors
Dr. Nguyen Trong Nghia
Lecture
Member of the Business AI Lab (BAI LAB) research group and lecturer at the Department of Data Science and Artificial Intelligence, School of Technology, National Economics University. Holds a PhD in Computer Science from Chonnam National University, Korea (2025), with expertise in AI applications for business.
nghiant@neu.edu.vn
MSc. Nguyen Thi Minh Trang
Tutorial
Lecturer at the Department of Data Science and Artificial Intelligence, School of Technology, National Economics University. Member of the Lab for Research and Technology Transfer of Data Science and Artificial Intelligence. Holds a Master's degree in Business Analytics from Nottingham Trent University, UK (2023).
ntmtrang@neu.edu.vn
MSc. Dam Tien Thanh
Tutorial
Member of the DataOptLab research team, Department of Data Science and Artificial Intelligence, School of Technology, National Economics University. Graduated with Honors from the University of Technology – VNU (2020) and holds a Master's degree from Phenikaa University (2023).
thanhtd@neu.edu.vnCourse Learning Outcomes (CLOs)
By the end of this course, students will be able to:
G1: Understand the roles and applications of data science in economics, finance, and marketing, and describe the basic steps in the lifecycle of a data science project.
G2: Use Python programming to process tabular data, including writing statements with basic control structures and using NumPy and Pandas.
G3: Understand processes for collecting, cleaning, and wrangling data from common sources, using transformations, combinations, and aggregations for analysis.
G4: Apply visualization tools and reporting to build appropriate data visualizations aligned with analytical goals, and present and interpret results in reports using Markdown, PDF, or presentation formats.
G5: Apply basic machine learning models such as linear regression, decision trees, random forests, gradient boosting, etc., to solve simple predictive tasks in business, and evaluate models with quantitative metrics such as MAE, RMSE, and R².
Course Schedule
| Week | Type | Topic | Materials | Assessment |
|---|---|---|---|---|
| 1 | Lecture |
Introduction to Data Science and Applications in Economics and Business
|
Chapters 1, 2, 3
📓 Notebook |
In-class discussion |
| 2 | Lecture |
Python Programming Language
|
Chapter 4
📓 Notebook |
In-class discussion; homework |
| 3 | Practice |
Python Programming Practice
|
Basic Python practice
📓 Notebook |
In-class discussion; homework |
| 4 | Lecture |
Python Libraries for Data Science
|
Chapters 5, 6
📓 Notebook |
In-class discussion; homework |
| 5 | Practice |
Python with NumPy and Pandas
|
NumPy practice; Pandas practice
📓 Notebook |
In-class discussion; homework |
| 6 | Lecture |
Data Input and Storage
|
Chapter 7
📓 Notebook |
|
| 7 | Practice |
Data Input and Storage Practice
|
Practice on data input and storage
📓 Notebook |
|
| 8 | Midterm |
Midterm 1 & Data Preprocessing Lecture
|
📓 Notebook |
Midterm 1 |
| 9 | Practice |
Data Preprocessing Practice
|
Preprocessing practice
📓 Notebook |
In-class discussion; homework |
| 10 | Lecture |
Data Transformation and Feature Engineering
|
Chapter 8 | In-class discussion; homework |
| 11 | Practice |
Data Transformation Practice
|
Data transformation practice | In-class discussion; homework |
| 12 | Lecture |
Data Visualization
|
Chapter 10 | In-class discussion; homework |
| 13 | Practice |
Data Visualization Practice
|
Visualization practice | In-class discussion; homework |
| 14 | Lecture |
Modeling with Data (Machine Learning)
|
Chapter 11 | In-class discussion; homework |
| 15 | Midterm & Practice |
Midterm 2 & Machine Learning Modeling Practice
|
Modeling practice | Midterm 2 |
Note: Schedule is subject to change. Check course announcements for updates.
Homework Assignments
Complete and submit your homework assignments via the Google Drive links below. All submissions must be in .ipynb (Jupyter Notebook) format.
NumPy and Pandas Practice
Work with NumPy arrays and Pandas DataFrames to perform data manipulation tasks. Practice array operations, DataFrame creation, data selection, and basic statistical analysis.
Upload your .ipynb file to the shared folder
Deliverables:
- Jupyter notebook (.ipynb) with solutions
- Brief documentation of approach
Grading Criteria:
- Correctness (60%)
- Code efficiency (20%)
- Documentation (20%)
Data Input and Storage
Practice reading and writing data from multiple sources including CSV, JSON, Excel files, web scraping, RSS feeds, Web APIs, and SQL databases.
Upload your .ipynb file to the shared folder
Deliverables:
- Jupyter notebook (.ipynb) with data collection code
- Sample datasets collected (CSV/JSON)
- Documentation of data sources used
Grading Criteria:
- Correctness (60%)
- Code quality (20%)
- Documentation (20%)
Data Collection and Cleaning
Collect data from web sources, clean and preprocess it for analysis.
Deliverables:
- Python script or notebook
- Cleaned dataset (CSV)
- Process documentation
Grading Criteria:
- Data quality (40%)
- Code quality (30%)
- Documentation (30%)
Data Visualization Project
Create comprehensive visualizations to explore and present insights from a business dataset.
Deliverables:
- Jupyter notebook with visualizations
- Written analysis (500–700 words)
Grading Criteria:
- Visualization quality (40%)
- Insight generation (40%)
- Presentation (20%)
Machine Learning Business Application
Build and evaluate a machine learning model to solve a business prediction problem.
Deliverables:
- Complete Jupyter notebook
- Dataset and preprocessing code
- Final report with model evaluation
- 5-minute presentation
Grading Criteria:
- Model performance (30%)
- Code quality (25%)
- Report quality (25%)
- Presentation (20%)
Important: Late submissions will be penalized 1 point per day. Missing submissions receive 0 points. Make sure to name your file properly: StudentID_HW#.ipynb
Practice Quizzes
Test your understanding with these interactive quizzes. Each quiz corresponds to lecture material and helps reinforce key concepts.
Lecture 2 Quiz: Python Basics
Week 2Test your understanding of Python fundamentals including variables, data types, and control structures.
- Variables and data types
- Conditional statements
- Loops and iteration
Lecture 3 Quiz: NumPy & Pandas
Week 3Quiz on Python data science libraries including NumPy arrays and Pandas DataFrames.
- NumPy arrays and operations
- Pandas DataFrames
- Data manipulation basics
Lecture 4 Quiz: Nhập và Lưu Trữ Dữ Liệu với Python
Week 430 câu hỏi trắc nghiệm về đọc và ghi dữ liệu từ CSV, JSON, Web, RSS/XML, Excel, API và SQL Database.
- CSV, JSON, Excel file operations
- Web scraping with read_html()
- RSS Feed parsing with BeautifulSoup
- Web API interaction
- SQL Database operations
Lecture 5 Quiz: Làm sạch và chuẩn bị dữ liệu
Week 530 câu hỏi trắc nghiệm về xử lý dữ liệu thiếu, trùng lặp, chuẩn hóa dữ liệu, xử lý chuỗi ký tự và mã hóa dữ liệu phân loại.
- Xử lý dữ liệu thiếu (Missing Data)
- Xử lý dữ liệu trùng lặp (Duplicate Data)
- Chuẩn hóa và biến đổi dữ liệu
- Xử lý chuỗi ký tự (String Processing)
- Mã hóa dữ liệu phân loại (Categorical Encoding)
Assessment & Grading
| Assessment Method | Week | Description | Weight |
|---|---|---|---|
| Attendance/participation | Weeks 1–15 | Full in-class participation; homework evaluation; in-class engagement | 10% |
| Knowledge Check 1 | Week 8 | Quiz/coding/presentation in class | 20% |
| Knowledge Check 2 | Week 15 | Quiz/coding/presentation in class | 20% |
| Final exam | Per university exam schedule | Computer-based multiple-choice exam | 50% |
Key Policies
📋 Eligibility
Students must achieve at least 5 points for class participation to be eligible for the final exam (per university regulations).
📅 Attendance
Students are responsible for attending all scheduled sessions. In cases of force majeure, students should self-study provided materials and complete assigned supplementary readings.
📝 Submissions
Failure to submit individual or group assignments as required will result in a score of 0 for that component. Late submissions are penalized by 1 point per day after the official deadline.
Course Resources
Software & Tools
- Python 13.0 or higher
- Jupyter Notebook or Google Colab
- NumPy, Pandas, Matplotlib, Seaborn, scikit-learn