Course Overview

Geared for scientists and engineers with potentially light practical programming background or experience, Applied Python for Data Scientists is a hands-on Python course that provides a ramp-up to using Python for scientific and mathematical computing. Students will explore basic Python scripting skills and concepts, and then move to the most important Python modules for working with data, from arrays, to statistics, to plotting results.

Key Learning Areas

  • Create and run basic programs
  • Design and code modules and classes
  • Implement and run unit tests
  • Use benchmarks and profiling to speed up programs
  • Process XML and JSON
  • Manipulate arrays with numpy
  • Get a grasp of the diversity of subpackages that make up scipy
  • Use iPython notebooks for ad hoc calculations, plots, and what-if?
  • Manipulate images with PIL
  • Solve equations with sympy

Course Outline

  • The Python Environment
  • Flow Control
  • Sequences
  • Working with Files
  • Dictionaries and Sets
  • Functions
  • Errors and Exception Handling
  • OS Services
  • Pythonic Idioms
  • Modules and Packages
  • Classes
  • Developer Tools
  • XML and JSON
  • iPython
  • numpy
  • scipy
  • A Tour of scipy subpackages
  • pandas
  • matplotlib
  • The Python Imaging Library (PIL)

Who Benefits

This course is geared for data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.

 

Prerequisites

While there are no specific programming prerequisites, students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting.