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Applied Python for Scientists & Engineers (TTPS4870)

GK# 4266 Vendor# TTPS4870

Course Overview

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In this course, you will learn how to use Python for scientific and mathematical computing. Starting with the basics, this course progresses to the most important Python modules for working with data-from arrays, to statistics, to plotting results.

Schedule

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  • Delivery Format:
  • Date:
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What You'll Learn

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  • 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

Outline

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Viewing outline for:

Virtual Classroom Live Outline

1. The Python environment

  • About Python
  • Starting Python
  • Using the interpreter
  • Running a Python script
  • Python scripts on UNIX/Windows
  • Using the Spyder editor

2. Getting started

  • Using variables
  • Built-in functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • String formatting
  • Command line parameters

3. Flow control

  • About flow control
  • White space
  • Conditional expressions (if,else)
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits

4. Sequences

  • About sequences
  • Lists and tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions, keywords, and operators
  • List comprehensions
  • Generator expressions
  • Nested sequences

5. Working with files

  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Raw (binary) data

6. Dictionaries and sets

  • Creating dictionaries
  • Iterating through a dictionary
  • Creating sets
  • Working with sets

7. Functions

  • Defining functions
  • Parameters
  • Variable scope
  • Returning values
  • Lambda functions

8. Errors and exception handling

  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions

9. OS services

  • The O module
  • Environment variables
  • Launching external commands
  • Walking directory trees
  • Paths, directories, and filenames
  • Working with file systems
  • Dates and times

10. Pythonic idioms

  • Small Pythonisms
  • Lambda functions
  • Packing and unpacking sequences
  • List Comprehensions
  • Generator Expressions

11. Modules and packages

  • Initialization code
  • Namespaces
  • Executing modules as scripts
  • Documentation
  • Packages and name resolution
  • Naming conventions
  • Using imports

12. Classes

  • Defining classes
  • Constructors
  • Instance methods and data
  • Attributes
  • Inheritance
  • Multiple inheritance

13. Developer tools

  • Analyzing programs with pylint
  • Creating and running unit tests
  • Debugging applications
  • Benchmarking code
  • Profiling applications

14. XML and JSON

  • Using ElementTree
  • Creating a new XML document
  • Parsing XML
  • Finding by tags and XPath
  • Parsing JSON into Python
  • Parsing Python into JSON

15. iPython

  • iiPython basics
  • Terminal and GUI shells
  • Creating and using notebooks
  • Saving and loading notebooks
  • Ad hoc data visualization

16. numpy

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

17. scipy

  • What can scipy do?
  • Most useful functions
  • Curve fitting
  • Modeling
  • Data visualization
  • Statistics

18. A tour of scipy subpackages

  • Clustering
  • Physical and mathematical Constants
  • FFTs
  • Integral and differential solvers
  • Interpolation and smoothing
  • Input and Output
  • Linear Algebra
  • Image Processing
  • Distance Regression
  • Root-finding
  • Signal Processing
  • Sparse Matrices
  • Spatial data and algorithms
  • Statistical distributions and functions
  • C/C++ Integration

19. pandas

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

20. matplotlib

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

21. The Python Imaging Library (PIL)

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

Labs

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Virtual Classroom Live Labs

This course is about 50% hands-on lab and 50% lecture, with extensive programming exercises designed to reinforce advanced Python programming skills, concepts and best practices learned in the lessons. Students will write numerous Python scripts to reinforce the major concepts covered in this course. The courses will increase in complexity as more sophisticated techniques are introduced. At the end of each lesson, students will be tested witha set of review questions to ensure that he/she fully grasps the topic. Our courses include ample materials and labs to ensure all students are either appropriately challenged, or assisted, at all times-no matter their skill level.

Who Should Attend

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Non-object oriented developers looking to apply Python to their scientific or engineering related job roles

Course Delivery

This course is available in the following formats:

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 5 day

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