Course Resources

Basic Data Science in Economics and Business

Primary 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)

📖 Access Online Textbook

This comprehensive textbook covers all course topics with practical Python examples tailored for economics and business applications.

Course Materials

📊 Lecture Slides

Access weekly lecture slides and presentation materials on the course platform:

🔗 SmartDoc Platform

• Updated before each lecture
• Include code examples and visualizations
• Downloadable in PDF format

💻 GitHub Repository

All code examples, datasets, and supplementary materials:

🔗 Course GitHub Repository

Contents include:

• Jupyter notebooks for each chapter
• Practice datasets
• Solution templates
• Additional exercises

Software & Tools

Python Installation

Required Version: Python 13.0 or higher

Installation Options:

1. Anaconda Distribution (Recommended for beginners)

   • Download from: anaconda.com
   • Includes Python, Jupyter, and common libraries
   • Easy package management with Conda

2. Official Python

   • Download from: python.org
   • Manual library installation via pip
   • More lightweight installation

Development Environments

Jupyter Notebook (Recommended)

• Interactive coding environment
• Supports markdown and visualization
• Install via Anaconda or: pip install notebook
• Launch with: jupyter notebook

Google Colab (Alternative)

• Web-based, no installation required
• Free GPU access
• Access at: colab.research.google.com

VS Code (For advanced users)

• Full-featured IDE
• Python extension available
• Download from: code.visualstudio.com

Required Python Libraries

Install these libraries for the course:

# Using pip
pip install numpy pandas matplotlib seaborn scikit-learn

# Using conda
conda install numpy pandas matplotlib seaborn scikit-learn

Core Libraries

NumPy

• Numerical computing with arrays
• Mathematical operations
• Documentation: numpy.org

Pandas

• Data manipulation and analysis
• DataFrame operations
• Documentation: pandas.pydata.org

Matplotlib

• Basic plotting and visualization
• Documentation: matplotlib.org

Seaborn

• Statistical data visualization
• Built on Matplotlib
• Documentation: seaborn.pydata.org

scikit-learn

• Machine learning algorithms
• Model evaluation tools
• Documentation: scikit-learn.org

Additional Learning Resources

Python Tutorials

• Official Python Tutorial: docs.python.org/3/tutorial/
• Real Python: realpython.com - Comprehensive Python tutorials
• Python for Data Science Handbook: jakevdp.github.io/PythonDataScienceHandbook/

Data Science Resources

• Kaggle Learn: kaggle.com/learn - Free micro-courses
• DataCamp: datacamp.com - Interactive data science courses
• Coursera - Applied Data Science: Various Python data science specializations

Visualization

• From Data to Viz: data-to-viz.com - Chart selection guide
• Python Graph Gallery: python-graph-gallery.com - Visualization examples

Machine Learning

• Scikit-learn Tutorials: scikit-learn.org/stable/tutorial/
• Google's Machine Learning Crash Course: developers.google.com/machine-learning/crash-course

Practice Datasets

Recommended Sources

Kaggle Datasets

• URL: kaggle.com/datasets
• Wide variety of real-world datasets
• Business and economics focus available

UCI Machine Learning Repository

• URL: archive.ics.uci.edu/ml
• Classic datasets for learning
• Well-documented

World Bank Open Data

• URL: data.worldbank.org
• Economic indicators
• Global development data

Vietnam Government Data

• General Statistics Office: gso.gov.vn
• Economic and social statistics

Troubleshooting & Help

Common Issues

Installation Problems

• Check Python version: python --version
• Update pip: pip install --upgrade pip
• Use virtual environments to avoid conflicts

Library Import Errors

• Verify installation: pip list
• Reinstall if needed: pip install --force-reinstall [library-name]

Jupyter Notebook Issues

• Clear output and restart kernel
• Update Jupyter: pip install --upgrade notebook
• Check browser compatibility (Chrome/Firefox recommended)

Getting Help

1. Course Forum/Discussion Board: Post questions and help classmates
2. Office Hours: Meet with instructors for personalized help
3. Stack Overflow: stackoverflow.com - Use tag [python] [pandas] etc.
4. Teaching Assistants: Email TAs for assignment-specific questions

Study Tips

Weekly Workflow

1. Before Class: Read assigned textbook chapters
2. During Class: Take notes, run example code
3. After Class: Review slides, practice exercises
4. Weekly: Complete homework assignments
5. Ongoing: Practice with additional datasets

Code Practice

• Code daily: Even 30 minutes helps build skills
• Type code manually: Don't just copy-paste
• Experiment: Modify examples to see what happens
• Debug systematically: Read error messages carefully
• Comment your code: Explain your logic

Exam Preparation

• Review practice quizzes: Identify weak areas
• Redo homework: Ensure you understand solutions
• Create cheat sheets: Summarize key concepts
• Form study groups: Teach concepts to others
• Ask questions: Clarify doubts before exams

Contact & Support

Instructors

Dr. Nguyen Trong Nghia (Lecture)
📧 nghiant@neu.edu.vn

MSc. Nguyen Thi Minh Trang (Tutorial)
📧 ntmtrang@neu.edu.vn

MSc. Dam Tien Thanh (Tutorial)
📧 thanhtd@neu.edu.vn

Technical Support

For platform or technical issues, contact:

• University IT Help Desk
• Course platform support team

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