In 2011, Marc Andreesen claimed that software is eating the world.

With hindsight, we can be more specific: Python is eating the world. The open source language is more popular than ever and sees no end in sight.

While it may sound intimidating and like too much work to some analysts, I believe that data analysts should learn a scripting language like Python. After all, it’s part of the data analytics stack.

Welcome to Planet Python

The workshop focuses on the basic data structures most important to data analysts, while it ends on packages and modules.

One of the most powerful features of Python is also one of the most confusing to newcomers. Python includes a universe of free packages: imagine the App Store, but for data! This workshop will help students make sense of the dizzying array, how to install them, and how packages work with other packages.

The workshop is conducted in the popular Jupyter notebook interface and includes an orientation to this exciting tool.

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Lesson 1: Up and running with Python + Jupyter

Objective: Student can create, navigate and download Jupyter notebooks for Python

Description:

• Why Python for data analysis
• A tour of Jupyter notebooks
• Assining the first variable

Exercises: Drills

Assets needed: None

Time: 30 minutes

Lesson 2: Introduction to Python programing

Objective: Student can assign variables and perform basic operations on variables

Description:

• Assigning, printing and modifying variables
• Operating arithmetically on variables
• Checking a variable’s type

Exercises: Drills

Assets needed: None

Time: 45 minutes

Lesson 3: Working with lists

Objective: Student can create, inspect and modify lists

Description:

• Variable types and lists
• Creating lists
• Slicing and subsetting lists
• Manipulating lists

Exercises: Drills

Assets needed: None

Time: 45 minutes

Lesson 4: Working with functions and methods

Objective: Student can pass lists into functions and methods

Description:

• Numeric functions and methods
• String functions and methods
• Method chaining
• Manipulating lists with functions and methods

Exercises: Drills

Assets needed: None

Time: 40 minutes

Lesson 5: Working with modules

Objective: Student can install, explore and implement elements of a module

Description:

• Installing a module
• Exploring features of a module
• Importing modules and elements of modules
• Aliasing modules

Exercises: Drills

Assets needed: None

Time: 40 minutes

Lesson 6: Capstone

Objective: Student can create and analyze lists using Python modules, methods and functions

Exercises: Extended drill

Assets needed: None

Time: 30 minutes

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