Course Topics and Schedule
Course Topics and Schedule#
The following is a top-level description of the topics covered in this course. For more details, you can skim the exercise solutions to see the exercise content.
Data: Data-management is an often-neglected part of a data science education; in this course you will learn a variety of tools and techniques to make your data useful.
Visualization: Data visualization is an extraordinarly powerful tool for making sense of a dataset. You will learn the grammar of graphics: a flexible and expressive language for constructing data visualizations. You will also get a lot of practice constructing graphs of different types, and using those graphs to make sense of data.
Statistics: A dataset is a lens to help understand the world, but that lense is necessarily limited. We will learn the fundamentals of statistical thinking to help protect ourselves against making erroneous conclusions. We will also learn powerful tools for detecting patterns in a dataset that would otherwise remain hidden.
Grama: We will learn the python software package grama; a grammar of model analysis. This toolkit will help us implement and study engineering models so we can make sense of physical scenarios.
The course materials are divided into exercises and challenges:
Exercises are provided as your first-contact with new ideas. Rather than listening to a lecture, the exercises are designed as interactive readings—through doing an exercise you will learn new data science skills. The exercises are provided with solutions—these will help you if you get stuck, and serve as a reference you can return to in the future.
Note: The exercises are grouped into the four categories above; see each exercise’s prefix to tell which belongs to which category.
Challenges are a chance to test your skills on realistic problems. These are provided without a solution; you will have to use what you learned from the Exercises to complete these challenges.