Programming in Python for Data Science

This course is part of the UBC Certificate in Key Capabilities in Data Science. You must complete this course before starting any electives.

Learn the foundations of programming in Python for data science, and how to conduct data analysis. Work with powerful Python packages made for data science, including Pandas for processing tabular data, Altair for data visualization and NumPy for working with numerical data types. Grow your capabilities in Python programming to process raw data into a format suitable for analysis, write your own analysis functions, and derive data-driven insights by creating visualizations and summary tables.

Come away from this online data science course knowing how to write human-readable code that sets a foundation of best practices and coding style.

By the end of the course, you’ll know:

• the definition of tidy data and explain why it is an optimal format for data analysis
• how to clean, filter, manipulate (wrangle) and summarize data using Python libraries for more effective data analysis
• key data structures, iteration, flow control and program design relevant to data exploration and analysis
• fundamental programming concepts such as loops, conditionals and data structures that create a solid foundation in data science programming

Course outline

Week 1: Python and Pandas

Week 2: Not so Scary Wrangling – Table Manipulation and Chaining

Week 3: Tidy Data and Joining Dataframes

Week 4: Basic Python Data Types and Structures

Week 5: Making Choices and Repeating Iterations

Week 6: Functions Fundamentals and Best Practices

Week 7: Importing Files and Coding Style Guides

Week 8: NumPy and Advanced Data Wrangling

How am I assessed?

Each course module includes an auto-graded assignment. In weeks 5 and 9, you take an online 45-minute open-book quiz that covers materials from modules 1–4 and 5–8 respectively. At the end of the 8th module, you complete a final project using the skills you learned in the course. You must obtain an overall grade of 70% or higher, and complete the final project, to pass the course.

Expected effort

Expect to spend 8–12 hours per week to complete weekly modules, auto-graded quizzes, open-book quizzes and the final project.

Technology requirements

To take this course, and for the best experience, we recommend you have access to:

• an email account
• a computer, laptop or tablet
• the latest version of a web browser (or previous major version release)
• a reliable internet connection.

For virtual office hours, you’ll also need:

• a video camera and microphone.

One day before the start of your course, we’ll email you step-by-step instructions for accessing your course.

Course format

This course is 100% online and facilitator supported with weekly facilitator office hours. Course work is done independently and at your own pace within deadlines set by your facilitator. Log in anytime to your course to access the modules.

Course virtual office hours (subject to change)

• Mondays: 5:15 - 6:15pm Pacific Time
• Wednesdays: 5:15 - 6:15pm Pacific Time

Join your facilitator and classmates by video conferencing to discuss course materials and assignments, receive feedback and ask questions.