DSCI011ProgramminginPythonforDataScience

DSCI 011 - Programming in Python for Data Science

Welcome to DSCI 011! This course is part of ABC's Mid-Careers Learning program and will teach you how to conduct data analysis in Python. During the course, you will work with powerful Python packages made for data-science, including Pandas for processing tabular data, Altair for interactive data visualization and NumPy for working with numerical data types. You will also learn about iteration, flow control, and the data types relevant to data exploration and analysis. You will leave this course capable of processing raw data into a format suitable for analysis, writing your own analysis functions, and derive data-driven insights via the creation of interactive visualizations and summary tables.

Course prerequisites: None

Module 0: Welcome to Programming in Python for Data Science

Course introduction and summary of course learning outcomes

Module 1: Python & Pandas - An Unexpected Friendship

In this module, you will be introduced to dataframes and the Pandas Python package.

Module 2: Not So Scary Wrangling (Table Manipulation and Chaining)

In this module, you will learn how to import different types of files, perform more advanced table manipulations (modifying and creating new columns) as well as method chaining conventions (style, including multi-line).

Module 3: Tidy Data and Joining Dataframes

In this Module, you will learn about tidy data and how to transform your dataset into a tidy format. It will also focus on how to combine and stack multiple dataframes.

Module 4: Python Without the "Eek" (Basic Python)

In this module, you will learn about basic Python data types and structures. You will explore what data types and structures are used to create a Pandas dataframe and how understanding column dtypes is important to data analysis.

Module 5: Making Choices and Repeating Iterations

In this module, you will learn how to write conditionals and learn the fundamentals of how to create code that efficiently repeats the same operations following the DRY principle.

Module 6: Functions Fundamentals and Best Practices

In this module, you will expand your knowledge on the concept of functions that were introduced in Module 5. This module covers how to develop good habits when writing functions like including docstrings, defensive programming, test-driven development and how to compose useful functions.

Module 7: Importing Files and the Coding Style Guide

In this module you will learn about how to import files and libraries from other directories and stylize your code for optimal readability

Module 8: Vogue Code the Style Guide (Readable Code and Libraries)

In this module you will learn about ...

Module Closing Remarks

Well done on finishing DSCI 011- Programming in Python for Data Science

About this course

Basic programming in Python. Overview of iteration and flow control and data types relevant to data exploration and analysis. When and how to exploit pre-existing libraries. Numerical data types with Numpy and tabular data with Pandas.

About the program

The University of British Columbia (UBC) is a comprehensive research-intensive university, consistently ranked among the 40 best universities in the world. The MDS Mid Career Learners program was launched in September 2020 and is offered by the MDS program who are a collaboration between the UBC Department of Computer Science and Department of Statistics.