DS for Business National Economics University

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 (Perfect for Beginners!)

Google Colab is a free, web-based Python environment that requires no installation. It's perfect for students who are new to programming!

🎯 Why Choose Google Colab?

✅ No Installation Required - Works in any web browser
✅ Free to Use - No cost, no credit card needed
✅ Pre-installed Libraries - All course libraries already available
✅ Cloud Storage - Your work is automatically saved
✅ Shareable - Easy to share notebooks with instructors
✅ Mobile Friendly - Works on phones and tablets

📋 Step-by-Step Setup Guide

Step 1: Access Google Colab

  1. Open your web browser (Chrome, Firefox, or Safari)
  2. Go to: colab.research.google.com
  3. You'll see the Google Colab welcome page

Step 2: Sign In (Required)

  1. Click the "Sign in" button in the top-right corner
  2. Use your Google account (Gmail address)
    • If you don't have a Google account, create one for free
  3. After signing in, you'll see the Colab interface

Step 3: Create Your First Notebook

  1. Click "New notebook" button (blue button on the left)
  2. A new notebook will open with a single cell
  3. You'll see a text box with "Code" written above it

Step 4: Test Python Installation

  1. Click inside the code cell (the text box)
  2. Type this simple code:
    print("Hello, Data Science!")
  3. Press Shift + Enter (or click the play button ▶️)
  4. You should see: Hello, Data Science!

Step 5: Install Required Libraries

  1. Create a new cell by clicking "+ Code"
  2. Copy and paste this code:
    !pip install numpy pandas matplotlib seaborn scikit-learn
  3. Press Shift + Enter to run
  4. Wait for installation to complete (may take 1-2 minutes)

Step 6: Verify Libraries Work

  1. Create another new cell
  2. Copy and paste this test code:
    import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

print("✅ All libraries imported successfully!")
print("NumPy version:", np.__version__)
print("Pandas version:", pd.__version__)
  3. Run the cell - you should see success messages

🎨 Understanding the Colab Interface

Main Components:

  1. Menu Bar (Top)

    • File: Save, download, share
    • Edit: Copy, paste, find
    • View: Show/hide different panels
    • Insert: Add new cells
    • Runtime: Run code, restart

  2. Toolbar (Below menu)

    • ▶️ Run: Execute current cell
    • ⏹️ Stop: Stop running code
    • 🔄 Restart: Restart the environment
    • 📁 Files: Access your files

  3. Code Cells (Main area)

    • White boxes where you write Python code
    • Each cell can be run independently
    • Output appears below each cell

  4. Left Sidebar (Optional)

    • 📁 Files: Your uploaded files
    • 📊 Table of Contents: Navigate your notebook
    • 🔍 Search: Find text in your notebook

📝 Basic Operations Guide

Adding New Cells:

  • Click "+ Code" to add a code cell
  • Click "+ Text" to add a text cell (for notes)

Running Code:

  • Shift + Enter: Run current cell and move to next
  • Ctrl + Enter: Run current cell and stay on it
  • Click ▶️: Run current cell

Saving Your Work:

  • Ctrl + S: Save automatically
  • File → Save: Manual save
  • Your work is saved to your Google Drive

Sharing Your Notebook:

  1. Click "Share" button (top-right)
  2. Copy the link and send to instructors
  3. Set permissions: "Anyone with the link can view"

🚀 Your First Data Science Code

Try this example to see Colab in action:

# Step 1: Import libraries
import pandas as pd
import matplotlib.pyplot as plt

# Step 2: Create sample data
data = {
    'Student': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Score': [85, 92, 78, 96]
}

# Step 3: Create a DataFrame
df = pd.DataFrame(data)
print("Student Scores:")
print(df)

# Step 4: Create a simple chart
plt.figure(figsize=(8, 4))
plt.bar(df['Student'], df['Score'], color='skyblue')
plt.title('Student Test Scores')
plt.xlabel('Students')
plt.ylabel('Scores')
plt.show()

💡 Pro Tips for Beginners

1. Start Simple

  • Begin with basic print statements
  • Don't worry about complex code initially
  • Focus on understanding the interface first

2. Use Text Cells for Notes

  • Add text cells to explain your code
  • Write down what you learned
  • Keep track of important concepts

3. Save Frequently

  • Colab auto-saves, but manual saves are good practice
  • Download important notebooks to your computer

4. Don't Panic About Errors

  • Red error messages are normal when learning
  • Read the error message carefully
  • Ask instructors for help with confusing errors

5. Explore the Interface

  • Try different menu options
  • Experiment with different cell types
  • Use the help menu if you get stuck

🔧 Troubleshooting Common Issues

Problem: "Module not found" error

  • Solution: Run the pip install command again
  • Code: !pip install [library-name]

Problem: Code runs but no output

  • Solution: Make sure you have print() statements
  • Check: Look for output below the cell

Problem: Notebook is slow

  • Solution: Restart runtime (Runtime → Restart)
  • Tip: Close other browser tabs to free memory

Problem: Can't find my notebook

  • Solution: Check your Google Drive
  • Location: Drive → Colab Notebooks folder

📚 Next Steps

Once you're comfortable with Colab:

  1. Upload Course Notebooks: Download .ipynb files from course schedule
  2. Practice Daily: Spend 15-30 minutes coding each day
  3. Join Study Groups: Share notebooks with classmates
  4. Ask Questions: Use the course discussion forum

🆘 Need Help?

Remember: Everyone starts as a beginner. Don't be afraid to make mistakes - they're part of learning! 🎓

VS Code (For advanced users)

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

Seaborn

scikit-learn

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

Additional Learning Resources

Python Tutorials

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

Machine Learning

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

World Bank Open 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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