Data Analysis with Python
Course Description
This course is intended to give attendees an insight into many of Python’s capabilities for data analysis, and the tools and techniques available to derive insights from data. Attendees are expected to have prior python programming experience, or would benefit from first attending our Python Programming Introduction course, if they do not.
Duration: 2 days
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