Course Syllabus
Basic Data Science in Economics and Business
Course Code: FDA.6.1.2.01.V
Credits: 3
Department: Faculty of Data Science and Artificial Intelligence
Institution: National Economics University
Course Description
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.
Prerequisites
Students should have completed:
- 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.
Learning Outcomes (CLOs)
Objective G1: Understanding Data Science
- CLO1.1 (Level II): Present definitions and characteristics of data science
- CLO1.2 (Level II): Explain the role of data science in business domains
- CLO1.3 (Level III): Describe the main steps in the lifecycle of a data science project
- CLO1.4 (Level III): Distinguish common data types in business analytics
- CLO1.5 (Level III): Relate practical application examples of data science in economics and finance
Objective G2: Python Programming
- CLO2.1 (Level II): Write Python code using variables, data types, and basic loops
- CLO2.2 (Level I): Use NumPy to manipulate one- and two-dimensional arrays
- CLO2.3 (Level II): Operate with Pandas DataFrame: filtering, grouping, joining, computations
- CLO2.4 (Level II): Run notebooks in Jupyter or Google Colab
- CLO2.5 (Level II): Read and modify sample Python code for simple data tasks
Objective G3: Data Collection & Wrangling
- CLO3.1 (Level III): Read data from CSV, Excel, and simple web pages
- CLO3.2 (Level II): Identify missing data and apply suitable cleaning techniques
- CLO3.3 (Level II): Transform data types and create new features for analysis
- CLO3.4 (Level II): Perform aggregation, grouping, and pivoting
- CLO3.5 (Level II): Merge and combine multiple tables into one analytical dataset
Objective G4: Visualization & Reporting
- CLO4.1 (Level II): Plot basic charts such as histogram, scatter plot, bar chart, and boxplot with Matplotlib/Seaborn
- CLO4.2 (Level II): Choose appropriate chart types for data and presentation objectives
- CLO4.3 (Level II): Interpret insights from charts and visual analysis
- CLO4.4 (Level NA): Present analysis results as Markdown or PDF reports
- CLO4.5 (Level I): Apply data storytelling in presenting results
Objective G5: Machine Learning
- CLO5.1 (Level II): Apply linear regression to predict continuous variables in business
- CLO5.2 (Level II): Train simple binary classifiers such as decision trees and random forests
- CLO5.3 (Level II): Split data into training and test sets
- CLO5.4 (Level I): Evaluate model performance using MAE, RMSE, R², confusion matrix
- CLO5.5 (Level II): Interpret predictions and apply them to real contexts
Assessment & Grading
| Assessment Method | Week | Weight | Description |
|---|---|---|---|
| Attendance/participation | Weeks 1–15 | 10% | Full in-class participation; homework evaluation; in-class engagement |
| Knowledge Check 1 | Week 8 | 20% | Quiz/coding/presentation in class |
| Knowledge Check 2 | Week 15 | 20% | Quiz/coding/presentation in class |
| Final exam | Per university exam schedule | 50% | Computer-based multiple-choice exam |
Weekly Schedule
| Week | Type | Topic | Materials |
|---|---|---|---|
| 1 | Lecture | Introduction to Data Science and Applications | Chapters 1, 2, 3 |
| 2 | Lecture | Python Programming Language | Chapter 4 |
| 3 | Practice | Python Programming Practice | Basic Python practice |
| 4 | Lecture | Python Libraries for Data Science | Chapters 5, 6 |
| 5 | Practice | Python with NumPy and Pandas | NumPy & Pandas practice |
| 6 | Lecture | Data Input and Storage | Chapter 7 |
| 7 | Practice | Data Input and Storage Practice | Practice materials |
| 8 | Midterm | Midterm 1 & Data Preprocessing Lecture | — |
| 9 | Practice | Data Preprocessing Practice | Preprocessing practice |
| 10 | Lecture | Data Transformation and Feature Engineering | Chapter 8 |
| 11 | Practice | Data Transformation Practice | Transformation practice |
| 12 | Lecture | Data Visualization | Chapter 10 |
| 13 | Practice | Data Visualization Practice | Visualization practice |
| 14 | Lecture | Modeling with Data (Machine Learning) | Chapter 11 |
| 15 | Midterm & Practice | Midterm 2 & ML Modeling Practice | Modeling practice |
Textbook
Data Science in Economics and Business (Python Applications)
Authors: Nguyen Quang Huy, Tran Thi Bich, Pham Xuan Lam, Nguyen Trung Thanh, Nguyen Thi Bach Tuyet
Publisher: National Economics University (2025)
Software Requirements
- Python 13.0 or higher
- Jupyter Notebook or Google Colab
- Required libraries: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn
Course Policies
Please refer to the Policies page for detailed information on:
- Eligibility requirements
- Attendance policy
- Classroom conduct
- Assignment submission guidelines