Programming Resources for Data Analysts and Accountants

What subjects should you learn?

• Probability and statistics
• Excel / SQL / Databases
• Programming basics
• Data visualization

Which programming language should you use?

• Python — Most popular for data analysis, easy to learn, rich ecosystem.
• R — Widely used in statistics and academia.

Where to find courses?

• 中国大学MOOC (Chinese University MOOC)
• 学堂在线 (XuetangX)
• MIT OCW
• Udemy
• edX
• Coursera

Recommended Courses

Programming Basics

• Python Programming (Chinese, MOOC)
• 2022 Complete Python Bootcamp From Zero to Hero in Python (Udemy)

SQL and Databases

• MySQL Database Technology (Chinese, MOOC)

Data Visualization

• Data Visualization—Python Applications (Chinese, MOOC)
• Accounting Data Analytics Specialization (Coursera)
• Business Data Analysis and Applications (Chinese, MOOC)
• Python Data Analysis and Visualization (Chinese, MOOC)

Books

• Head First Python
• MySQL Basics
• Data Preprocessing from Beginner to Practice: Based on SQL, R, Python
• Python Data Analysis Practice (2nd Edition)
• Python Crash Course (Turing)
• Excel + Python: Fast Data Analysis and Processing

For buying books, consider Kongfz Used Books for affordable second-hand options—most are nearly new but much cheaper.

Tools

• VS Code — Code editor
• Git — Version control
• Linux — Operating system for data work
• Shell Programming — Useful for automation and data processing

Additional Tips

• Practice with real datasets (e.g., Kaggle).
• Learn basic data cleaning and preprocessing.
• Understand basic data visualization principles (matplotlib, seaborn, Excel charts).
• Familiarize yourself with Jupyter Notebook for interactive analysis.

Summary

Recommended learning order:
Python, Git, Shell → SQL → Data Visualization

Start with programming basics, then move to databases and data visualization. Practice regularly and build small projects to reinforce your skills.