Professional Training

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