Data Analysis with Python

Prerequisites

Basic Python programming experience. In particular working with strings; working with lists, tuples and dictionaries; loops and conditionals; and writing your own functions

Learning Outcomes

• At the conclusion of this course, attendees will be able to:
• • Use the Jupyter and Pycharm Environments
• • Use basic and advanced NumPy (Numerical Python) features
• • Get started with data analysis tools in the pandas library
• • Use high-performance tools to load, clean, transform, merge, and reshape data
• • Create scatter plots and static or interactive visualizations with matplotlib and Seaborn
• • Apply the pandas groupby facility to slice, dice, and summarize datasets
• • Measure data by points in time, whether it’s specific instances, fixed periods, or intervals
• • Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

Python Review

• Data Types and Variables
• Flow of Control
• Functions
• Lists, Tuples and Dictionaries
• Files
• Exceptions

Classes

• Class variables and methods
• Working with Properties
• Special Class methods
• Working with decorators

Jupyter Interactive Environment

• Magic commands
• Timing code
• Loading sample books from the web

Numerical Computing with Python – The Numpy Array

• Reasons for Numpy
• Creating ndarrays
• Indexing and Slicing
• Boolean Indexing
• Fancy Indexing
• Universal Functions
• Using Scipy Functions with Numpy

Pandas

• Introducing Pandas Series and Dataframes
• Operating on Data in Pandas
• Handling Missing Data
• Hierarchical Indexing
• Combining Datasets: Concat and Append
• Combining Datasets: Merge and Join
• Aggregation and Grouping
• Pivot Tables