• Unit 3: The numpy Module

    Data science must deal with the various forms that data can take on when it is collected. While Python lists and dictionaries are powerful, data can often come in the form of numerical arrays or spreadsheets. This unit introduces the numpy module, which is useful for performing matrix operations on data contained within arrays. When you finish this unit, you will be able to implement a host of matrix operations relevant to data science.

    Much of the syntax used for lists, such as indexing and slicing, naturally carries over to numpy arrays. Array operations in numpy are typical of what you would expect from basic linear algebra (such as matrix addition and multiplication, systems of linear equations, determinants, and matrix inverses). Additionally, methods within the matplotlib module can accept numpy arrays as input. Finally, we will also take a look at file handling using numpy, since having this skill is often useful for machine learning applications.

    Completing this unit should take you approximately 6 hours.

    • 3.1: Constructing Arrays

    • 3.2: Indexing

    • 3.3: Array Operations

    • 3.4: Saving and Loading Data

    • Unit 3 Assessment

      • Receive a grade