Python for Data

Python for Data

Video Training

With a gentle learning curve, Python is readable, writeable, and endlessly powerful. Its simplicity lets you become productive quickly. This Learning Path provides a solid introduction to Python, and then teaches you about algorithms, data modeling, data structures and other tools that make Python the ideal choice for working with data.

Prerequisite:

Some programming experience is recommended.

Below are the video training courses included in this Learning Path.

1

Introduction to Python

Presented by Jessica McKellar 3 hours 27 minutes

Get a solid introduction to Python’s core concepts and data types through hands-on exercises, as open source developer Jessica McKellar shows you what’s possible with Python and gets you started writing programs of your own.

2

Learning iPython Notebook

Presented by James Powell 3 hours 1 minute

iPython Notebook is an interactive computational environment, that lets you combine code execution, rich text, mathematics, plots, and rich media. In this course, you’ll complete two projects after which you’ll be fully capable of using iPython Notebook as a tool for programming in Python and be able to perform data analyses in Python.

3

Working with Algorithms in Python

Presented by George T. Heineman 8 hours 40 minutes

Learn how to make your Python code more efficient by using algorithms to solve a variety of tasks or computational problems. In this course, you’ll learn algorithm basics and then tackle a series of problems—such as determining the shortest path through a graph and the minimum edit distance between two genomic sequences—using existing algorithms.

4

Python Data Structures

Presented by James Powell 4 hours 1 minute

Get a solid understanding of the built-in data types in Python. You’ll start with data structures and then learn how to interact with the set type, such as type constructing, comprehension, and indexing. Finally, you’ll move on to list and tuple types, including the list type, how to create a list and tuple, and semantics.