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 data science roles and project lifecycle in business contexts.
  • G2: Use Python programming with NumPy and Pandas for data processing.
  • G3: Collect, clean, and wrangle data using transformations and aggregations.
  • G4: Create data visualizations and present results in reports.
  • G5: Apply machine learning models and evaluate performance metrics.

💻 Required Software

  • Python 13.0 or higher
  • Jupyter Notebook or Google Colab
  • NumPy, Pandas, Matplotlib, Seaborn, scikit-learn

This Week (Week 1)

📌 Week Highlights

  • 📚 Welcome to Basic Data Science in Economics and Business course
  • 🎯 Introduction to Data Science concepts and workflow
  • 📖 Review course syllabus and learning objectives
  • 💻 Set up your development environment (Jupyter Notebook or Google Colab)
  • 📓 Download Lecture 1 notebook from course schedule
  • 🔍 Explore course materials and resources

📢 Announcements

Welcome to the Course!

2025-01-10

Welcome to Basic Data Science in Economics and Business! Please review the course syllabus, familiarize yourself with the schedule, and set up your development environment. Download the Lecture 1 notebook to get started.

Course Resources Available

2025-01-10

All course materials including notebooks, lecture slides, and resources are now available on the course website. Make sure to check the Schedule section for weekly materials.

🗓️ Coming Up

  • Week 2: Introduction to Python - Next week
  • Week 3: Python Practice Session - In 2 weeks

Instructors

Dr. Nguyen Trong Nghia

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.

MSc. Nguyen Thi Minh Trang

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).

MSc. Dam Tien Thanh

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).

Course Learning Outcomes (CLOs)

By the end of this course, students will be able to:

G1: Understand data science roles and project lifecycle in business contexts.

CLO1.1 Present definitions and characteristics of data science. Level II
CLO1.2 Explain the role of data science in business domains. Level II
CLO1.3 Describe the main steps in the lifecycle of a data science project. Level III
CLO1.4 Distinguish common data types in business analytics. Level III
CLO1.5 Relate practical application examples of data science in economics and finance. Level III

G2: Use Python programming with NumPy and Pandas for data processing.

CLO2.1 Write Python code using variables, data types, and basic loops. Level II
CLO2.2 Use NumPy to manipulate one- and two-dimensional arrays. Level I
CLO2.3 Operate with Pandas DataFrame: filtering, grouping, joining, computations. Level II
CLO2.4 Run notebooks in Jupyter or Google Colab. Level II
CLO2.5 Read and modify sample Python code for simple data tasks. Level II

G3: Collect, clean, and wrangle data using transformations and aggregations.

CLO3.1 Read data from CSV, Excel, and simple web pages. Level III
CLO3.2 Identify missing data and apply suitable cleaning techniques. Level II
CLO3.3 Transform data types and create new features for analysis. Level II
CLO3.4 Perform aggregation, grouping, and pivoting. Level II
CLO3.5 Merge and combine multiple tables into one analytical dataset. Level II

G4: Create data visualizations and present results in reports.

CLO4.1 Plot basic charts such as histogram, scatter plot, bar chart, and boxplot with Matplotlib/Seaborn. Level II
CLO4.2 Choose appropriate chart types for data and presentation objectives. Level II
CLO4.3 Interpret insights from charts and visual analysis. Level II
CLO4.4 Present analysis results as Markdown or PDF reports. Level NA
CLO4.5 Apply data storytelling in presenting results. Level I

G5: Apply machine learning models and evaluate performance metrics.

CLO5.1 Apply linear regression to predict continuous variables in business. Level II
CLO5.2 Train simple binary classifiers such as decision trees and random forests. Level II
CLO5.3 Split data into training and test sets. Level II
CLO5.4 Evaluate model performance using MAE, RMSE, R², confusion matrix. Level I
CLO5.5 Interpret predictions and apply them to real contexts. Level II

Course Schedule

Week Type Topic Materials Assessment
1 Lecture Introduction to Data Science and Applications in Economics and Business
  • Core concepts in data science
  • Data analysis vs. data science
  • What is modeling?
  • Introduction to Python
 Chapters 1, 2, 3
📓 Notebook 		In-class discussion
2 Lecture Python Programming Language
  • Variables in Python
  • Vectors and sequential data types
  • Conditional statements
  • Loops
  • Functions
 Chapter 4
📓 Notebook 		In-class discussion; homework
3 Practice Python Programming Practice
  • Variables in Python
  • Vectors and sequential data types
  • Conditional statements
  • Loops
  • Functions
 Basic Python practice
📓 Notebook 		In-class discussion; homework
4 Lecture Python Libraries for Data Science
  • Data science libraries overview
  • NumPy arrays
  • Pandas DataFrame
 Chapters 5, 6
📓 Notebook
📄 Supply: Python NumPy & Pandas Essentials (PDF) 		In-class discussion; homework
5 Practice Python with NumPy and Pandas
  • Work with NumPy arrays
  • Evaluate NumPy performance vs. native arrays
  • Practice basic Pandas DataFrame operations
 NumPy practice; Pandas practice In-class discussion; homework
6 Lecture Data Input and Storage
  • Read and write text formats
  • Web data collection
  • Read from Microsoft Excel
  • Interact with Web APIs
  • Interact with databases
 Chapter 7
7 Practice Data Input and Storage Practice
  • Read and write text formats
  • Web scraping/collection
  • Read Excel
  • Interact with Web APIs
  • Interact with databases
 Practice on data input and storage
8 Midterm Midterm 1
  • Midterm Exam 1
 Midterm 1
8 Lecture Data Preprocessing Lecture
  • Format data aligned to research goals
  • Handle outliers and missing values
  • Data cleaning techniques
  • Feature engineering
 Chapter 9 In-class discussion
9 Practice Data Preprocessing Practice
  • Clean data, transform to desired formats
  • Detect and handle outliers with boxplots, Z-scores
  • Handle missing data by deletion, imputation, etc.
 Preprocessing practice In-class discussion; homework
10 Lecture Data Transformation and Feature Engineering
  • Reshape data between wide and long formats
  • Encode categorical variables
  • Normalize quantitative variables
  • Create new features from raw attributes
 Chapter 8 In-class discussion; homework
11 Practice Data Transformation Practice
  • Reshape wide/long
  • Encode categorical variables
  • Normalize quantitative variables
  • Create new features
 Data transformation practice In-class discussion; homework
12 Lecture Data Visualization
  • Basic charts: histogram, scatter, bar
  • Principles for selecting appropriate charts for one or multiple variables
  • Use of color, shape, and size to enhance interpretability
  • Interpret data via visualization
 Chapter 10 In-class discussion; homework
13 Practice Data Visualization Practice
  • Plot with Matplotlib and Seaborn
  • Apply visualization in analysis
  • Create simple dashboards in Python
 Visualization practice In-class discussion; homework
14 Lecture Modeling with Data (Machine Learning)
  • Core ML concepts
  • Linear models for regression and classification
  • Decision trees
  • Tree ensembles
  • Model evaluation metrics
 Chapter 11 In-class discussion; homework
15 Midterm & Practice Midterm 2 & Machine Learning Modeling Practice
  • Midterm Exam 2
  • Linear models for regression and classification
  • Decision trees
  • Tree ensembles
 Modeling practice Midterm 2

Note: Schedule is subject to change. Check course announcements for updates.

Practice Quizzes

Test your understanding with these interactive quizzes. Each quiz corresponds to lecture material and helps reinforce key concepts.

Lecture 1 Quiz: Giới thiệu về Khoa học dữ liệu

Week 1

30 câu hỏi trắc nghiệm về khái niệm Khoa học dữ liệu, quy trình phân tích, các lĩnh vực chuyên sâu, loại dữ liệu, và ứng dụng trong kinh tế & kinh doanh.

Topics covered:
  • Khái niệm Khoa học dữ liệu và 3 trụ cột
  • Khoa học dữ liệu trong Kinh tế & Kinh doanh
  • Các lĩnh vực chuyên sâu (Data Engineering, Analytics, ML)
  • Quy trình Khoa học dữ liệu (7 bước)
  • Các loại dữ liệu (Structured, Semi-structured, Unstructured)
  • Vai trò trong Khoa học dữ liệu (Data Engineer, Analyst, Scientist)
  • Học máy (Supervised, Unsupervised, Reinforcement)
  • Công cụ và ngôn ngữ lập trình
Start Quiz

Lecture 2 Quiz: Python Basics

Week 2

Test your understanding of Python fundamentals including variables, data types, and control structures.

Topics covered:
  • Variables and data types
  • Conditional statements
  • Loops and iteration
Start Quiz

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.

View Full Policies

Course Resources

📚

Course Textbook

Data Science in Economics and Business (Python Applications)

Access Textbook
📊

Slide Deck

Weekly lecture slides and presentation materials

View Slides
💻

GitHub Repository

Code examples, datasets, and supplementary materials

Visit GitHub
🔧

Software & Tools

  • Python 13.0 or higher
  • Jupyter Notebook or Google Colab
  • NumPy, Pandas, Matplotlib, Seaborn, scikit-learn
Setup Guide