With data now being created at the rate of 2.5 quintillion bytes a day, there is a tremendous demand for people who can explore vast amounts of data. In this lab-based course, you will learn how to glean empirical truth from data using Python with Pandas, how to make the right decisions, and how to bring order from chaos.

Experience Python's straight-forward syntax, built-in data types, and object-oriented programming (OOP) and make your own data types. Learn how Python's brilliant architecture allows you to jump into any of more than 300,000 libraries provided for Python. In this course you work with the Pandas, Numpy, and Matplotlib libraries to inspect data, manipulate data, calculate statistics, and provide informative and beautiful visual representations for data sets via interactive Jupyter Notebooks.

Learning Outcomes
At the conclusion of the course, you should be able to:

• Describe Python's underlying object model, operators, and syntax
• Employ Pandas, Numpy, and Matplotlib through Python and Jupyter Notebooks
• Clean, manipulate, analyze, and graph data
• Create Python functions to customize the behavior of data transformations
• Grasp and emulate the online Python/Pandas/Matplotlib data analysis examples

Topics Include

• Pandas, DataFrames and Series for data sets:
* Cleaning
* Collating
* Extrapolating
* Analyzing
* Dealing with timed data
* Discovering cycles

• Matplotlib for presenting graphs
• Python for using the data libraries effectively

Skills Needed

Helpful, but not required, are a basic experience in any programming language and a rudimentary knowledge of statistics.