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Python 3 for Engineers and Data Scientists

  • Course Code: PY3ED
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days

Overview

This course takes beginning or intermediate Python 3 developers into the world of Python 3 for scientific and mathematical computing. It presents the most important Python 3 modules for working with data, from arrays, to statistics, to plotting results. The material is geared towards scientists and engineers.

This is a hands-on programming class.  All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs, which helps students retain the earlier material.

Prerequisites

Students should be comfortable writing basic Python 3 (or Python 2) scripts, including file I/O, basic data structures, and creating classes.

Exercises

Python 3 for Scientists is 35% hands-on, 65% lecture, with the longest lecture segments lasting for around 45 minutes. Students “learn by doing,” with immediate opportunities to apply the material they learn to real-world problems.

Objectives

  • Use benchmarks and profiling to speed up programs
  • Process XML and JSON
  • Manipulate arrays with numpy
  • Grasp the diversity of subpackages that make up scipy
  • Use iPython notebooks for ad hoc calculations, plots, and what-if?
  • Import and analyze data with pandas
  • Create a wide variety of data plots with matplotlab
  • Manipulate images with PIL

Course Outline

1 – Python Refresher

  • Data types
  • Sequences
  • Mapping types
  • Program structure
  • Files and console I/O
  • Conditionals
  • Loops
  • Builtins
  • Classes

2 – Pythonic idioms

  • Lambda functions
  • Sorting
  • Packing and unpacking sequences
  • List Comprehensions
  • Generator Expressions

3 – Modules and Packages

  • Writing functions
  • Variable scope
  • Module overview
  • Creating modules
  • Creating and using packages

4 – Serializing Data

  • XML
  • JSON
  • CSV
  • Pickle

5 – Working with Excel

  • Using openpyxl
  • Reading an existing spreadsheet
  • Creating a new spreadsheet
  • Updating a spreadsheet
  • Working with styles and formatting

6 – iPython/Jupyter

  • iPython basics
  • Terminal and GUI shells
  • Creating and using Jupyter notebooks
  • Saving and loading notebooks

7 – Developer tools

  • Virtual Environments
  • Debugging applications
  • Benchmarking code
  • Profiling applications

8 – numpy

  • numpy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks

9 – scipy

  • The Python scientific stack
  • What can scipy do?
  • Getting help
  • Where to find things
  • What is available?
  • Brief tour of scipy subpackages

10 – pandas

  • pandas overview
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets

11 – matplotlib

  • Creating a basic plot
  • Commonly used plots
  • Ad hoc data visualization
  • Advanced usage
  • Exporting images

12 – Pilllow – an imaging library

  • pillow overview
  • Core image library
  • Image processing
  • Displaying images
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