Python for Data Science Primer

This course introduces data analysts, business analysts, and anyone interested in Data Science to the Python programming language as it s often used in Data Science in web notebooks. The goal is to provide you with a baseline understanding of core concepts that will serve as a platform of knowledge to follow-up with more in-depth training and real-world practice.

The course begins with a quick overview of Python with demonstrations of both script-based and web notebook-based Python and then you ll dive into the essentials of Python necessary for a data scientist. The end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas, and Matplotlib.

This class is hands-on and includes light programming labs that introduce students to basic Python syntax and concepts applicable to using Python to work with data.

An Overview of Python

• Why Python?
• Python in the Shell
• Python in Web Notebooks (iPython, Jupyter, and Zeppelin)
• Demo: Python, Notebooks, and Data Science

Getting Started

• Using variables
• Built-in functions
• Strings
• Numbers
• Converting among types
• Writing to the screen
• Command line parameters

Flow Control

• About flow control
• White space
• Conditional expressions
• Relational and Boolean operators
• While loops
• Alternate loop exits

Sequences, Arrays, Dictionaries, and Sets

• About sequences
• Lists and list methods
• Tuples
• Indexing and slicing
• Iterating through a sequence
• Sequence functions, keywords, and operators
• List comprehensions
• Generator Expressions
• Nested sequences
• Working with Dictionaries
• Working with Sets

Working with files

• File overview
• Opening a text file
• Reading a text file
• Writing to a text file
• Reading and writing raw (binary) data

Functions

• Defining functions
• Parameters
• Global and local scope
• Nested functions
• Returning values

Essential Demos

• Sorting
• Exceptions
• Importing Modules
• Classes
• Regular Expressions

The standard library

• Math functions
• The string module

Dates and times

• Working with dates and times
• Translating timestamps
• Parsing dates from text
• Formatting dates
• Calendar data

Python and Data Science

• Data Science Essentials
• Pandas Overview
• NumPy Overview
• SciKit Overview
• MatPlotLib Overview
• Working with Python in Data Science

No prior programming experience is required.

Business Analysts, Data Analysts, and anyone interested in data science who is comfortable working with numerical data in Excel or other spreadsheet environments.