Background
Data analysis is essential in today’s digital age, where enormous amounts of data are generated across every industry. Python, with its simple syntax and robust data libraries, empowers you to transform raw data into clear, actionable insights that drive informed decision-making.
Learning contents
This course is designed to introduce data analytics using Python through a series of practical modules. It begins with the basics, covering Python syntax, variables, data types, and fundamental operations, as well as writing simple scripts to automate your workflow. You'll work with Python's core data structures, such as lists, dictionaries, and sets, which form the backbone of efficient data management.
As the course progresses, you'll learn to import and manipulate data using popular libraries like Pandas and NumPy. You'll gain hands-on experience cleaning, transforming, and wrangling datasets, and you'll explore methods for reading from and writing to common file formats like CSV and Excel. Additionally, you'll enhance your programming skills by developing custom functions for routine analytic tasks.
In the later stages, the course covers basic statistical techniques—including regression analysis and principal component analysis (PCA)—to help you extract meaningful insights from your data. Finally, you'll build your skills.
Learning objectives
- Python Fundamentals: Understand Python syntax, variables, data types, and control structures to build a strong coding foundation.
- Core Data Structures: Gain proficiency with lists, dictionaries, sets, and other essential data structures to effectively store and manipulate data.
- Script Automation: Learn to write and run scripts that automate repetitive tasks and streamline your workflow.
- Data Handling with Libraries: Develop hands-on experience with key Python libraries like Pandas and NumPy to import, clean, transform, and export data from common file formats such as CSV and Excel.
- Custom Function Development: Build the skills to write custom functions that simplify and automate common analytical tasks.
- Statistical Analysis: Apply basic statistical techniques, including regression analysis and principal component analysis (PCA), to extract meaningful insights from data.
- Data Visualization: Master the use of visualization libraries like Seaborn to create clear and effective data visualizations that communicate results
Prior knowledge
- Basic Computer Skills: Participants should be comfortable using a computer for file management and simple tasks
- No Programming Experience Required: This beginner course is designed with the assumption that learners have little or no prior experience in programming or Python
- Basic Data Handling Familiarity (Optional): While not essential, familiarity with basic data organization—such as using spreadsheets (e.g., CSV or Excel files)—can be beneficial.
- Eagerness to Learn: A willingness to engage with hands-on exercises and explore new tools is key to success in this course
Technical requirements
- Own laptop
- Connection to the Wifi
Recommended literature / materials
- Python cheat sheet: https://ehmatthes.github.io/pcc_3e/cheat_sheets
- Quick ref: https://quickref.me/python.html
