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Python 2.x Training for Scientists and Engineers

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This Python 2.x Training for Scientists and Engineers class provides a ramp-up to using Python for scientific and mathematical computing. Starting with the basics, it progresses to the most important Python modules for working with data, from arrays, to statistics, to plotting results. The material is geared toward 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.

Location

Public Classes: Delivered live online via WebEx and guaranteed to run . Join from anywhere!

Private Classes: Delivered at your offices , or any other location of your choice.

Goals
  1. Create and run basic programs.
  2. Design and code modules and classes.
  3. Implement and run unit tests.
  4. Use benchmarks and profiling to speed up programs.
  5. Process XML and JSON.
  6. Manipulate arrays with numpy.
  7. Get a grasp of the diversity of subpackages that make up scipy.
  8. Use iPython notebooks for ad hoc calculations, plots, and what-if?.
  9. Manipulate images with PIL.
  10. Solve equations with sympy.
Outline
  1. The Python Environment
    1. Starting Python
    2. If the Interpreter Is Not in Your PATHs
    3. Using the Interpreter
    4. Trying Out a Few Commands
    5. The help() Function
    6. Running a Python Script
    7. Python Scripts on Unix
    8. Python Scripts on Windows
    9. Python Editors and IDEs
  2. Getting Started
    1. Using Variables
    2. Keywords
    3. Built-in Functions
    4. Variable Typing
    5. Strings
    6. Single-quoted String Literals
    7. Triple-quoted String Literals
    8. Raw String Literals
    9. Unicode Literals
    10. String Operators and Methods
    11. Numeric Literals
    12. Math Operators and Expressions
    13. Converting among Types
    14. Writing to the Screen
    15. String Formatting
    16. Legacy String Formatting
    17. Command Line Parameters
    18. Reading from the keyboard
  3. Flow Control
    1. About Flow Control
    2. What's with the White Space?
    3. if and elif
    4. Conditional Expressions
    5. Relational Operators
    6. Boolean Operators
    7. while Loops
    8. Alternate Ways to Exit a Loop
  4. Lists and Tuples
    1. About Sequences
    2. Lists
    3. Tuples
    4. Indexing and Slicing
    5. Iterating through a Sequence
    6. Functions for All Sequences
    7. Using enumerate()
    8. Operators and Keywords for Sequences
    9. The xrange() Function
    10. Nested Sequences
    11. List Comprehensions
    12. Generator Expressions
  5. Working with Files
    1. Text file I/O
    2. Opening a Text File
    3. The with Block
    4. Reading a Text File
    5. Writing to a Text File
    6. "Binary" (Raw, or Non-delimited) Data
  6. Dictionaries and Sets
    1. About Dictionaries
    2. When to Use Dictionaries
    3. Creating Dictionaries
    4. Getting Dictionary Values
    5. Iterating through a Dictionary
    6. Reading File Data into a Dictionary
    7. Counting with Dictionaries
    8. About Sets
    9. Creating Sets
    10. Working with Sets
  7. Functions
    1. Defining a Function
    2. Function Parameters
    3. Global Variables
    4. Variable Scope
    5. Returning Values
  8. Exception Handling
    1. Syntax Errors
    2. Exceptions
    3. Handling Exceptions with Try
    4. Handling Multiple Exceptions
    5. Handling Generic Exceptions
    6. Ignoring Exceptions
    7. Using else
    8. Cleaning Up with finally
    9. Re-raising Exceptions
    10. Raising a New Exception
    11. The Standard Exception Hierarchy
  9. OS Services
    1. The os Module
    2. Environment Variables
    3. Launching External Processes
    4. Paths, Directories, and Filenames
    5. Walking Directory Trees
    6. Dates and Times
    7. Sending email
  10. Pythonic Idioms
    1. Pythonic Idioms
    2. The Zen of Python
    3. Common Python Idioms
    4. Packing and Unpacking
    5. Lambda Functions
    6. List Comprehensions
    7. Generators vs Iterators
    8. Generator Expressions
    9. String Tricks
  11. Modules and Packages
    1. What Is a Module?
    2. The Import Statement
    3. Where Did that pyc File Come from?
    4. Module Search Path
    5. Zipped Libraries
    6. Creating Modules
    7. Packages
    8. Module Aliases
    9. When the Batteries Aren't Included
  12. Classes
    1. Defining Classes
    2. Instance Objects
    3. Instance Attributes
    4. Methods
    5. init
    6. Properties
    7. Class Data
    8. Inheritance
    9. Multiple INHERITANCE
    10. Base Classes
    11. Special Methods
    12. Pseudo-private Variables
    13. Static Methods
  13. Developer Tools
    1. Program Development
    2. Comments
    3. pylint
    4. Customizing pylint
    5. Unit Testing
    6. The unittest Module
    7. Creating a Test Class
    8. Establishing Success or Failure
    9. Startup and Cleanup
    10. Running the Tests
    11. The Python Debugger
    12. Starting Debug Mode
    13. Stepping through a Program
    14. Setting Breakpoints
    15. Debugging Command Reference
    16. Benchmarking
  14. Chapter 14 – XML and JSON
    1. About XML
    2. Normal Approaches to XML
    3. Which Module to Use?
    4. Getting Started with ElementTree
    5. How ElementTree Works
    6. Creating a New XML Document
    7. Parsing an XML Document
    8. Navigating the XML Document
    9. Using XPath
    10. Advanced XPath
  15. iPython
    1. About iPython
    2. Features of iPython
    3. Starting iPython
    4. Tab Completion
    5. Magic Commands
    6. Benchmarking
    7. External Commands
    8. Enhanced Help
    9. Notebooks
  16. numpy
    1. Python's scientific Stack
    2. numpy Overview
    3. Creating Arrays
    4. Creating Ranges
    5. Working with Arrays
    6. Shapes
    7. Slicing and Indexing
    8. Indexing with booleans
    9. Stacking
    10. Iterating
    11. Tricks with Arrays
    12. Matrices
    13. Data Types
    14. numpy Functions
  17. scipy
    1. About scipy
    2. Polynomials
    3. Vectorizing Functions
    4. Subpackages
    5. Getting Help
    6. Weave
  18. A Tour of scipy subpackages
    1. cluster
    2. constants
    3. fftpack
    4. integrate
    5. interpolate
    6. io
    7. linalg
    8. ndimage
    9. odr
    10. optimize
    11. signal
    12. sparse
    13. spatial
    14. special
    15. stats
  19. pandas
    1. About pandas
    2. Pandas Architecture
    3. Series
    4. DataFrames
    5. Data Alignment
    6. Index Objects
    7. Basic Indexing
    8. Broadcasting
    9. Removing Entries
    10. Time Series
    11. Reading Data
  20. matplotlib
    1. About matplotlib
    2. matplotlib Architecture
    3. matplotlib Terminology
    4. matplotlib Keeps State
    5. What Else Can You Do?
  21. Python Imaging Library
    1. The PIL
    2. Supported Image File Types
    3. The Image Class
    4. Reading and Writing
    5. Creating Thumbnails
    6. Coordinate System
    7. Cropping and Pasting
    8. Rotating, Resizing, and Flipping
    9. Enhancing
Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following would be useful for this Python class:

  • While there are no programming prerequisites, programming experience is helpful. Students should be comfortable working with files and folders, and should not be afraid of the command line.

Training for your Team

Length: 5 Days
  • Private Class for your Team
  • Online or On-location
  • Customizable
  • Expert Instructors
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What people say about our training

Very useful!
Yaxi Liu
Southwest Research Institute
Instructor was great! She explained things in a way that was easily understood. I will definitely take more courses through Webucator!
Brittany Stuhmer
Health Plan of San Joaquin
The class is a must have if you are a loyal employee looking to improve productivity within your organization.
Sheila Lofton
The Permanente Medical Group
Great pace to the training, 2 days well spent.
Brian Mokaya
Ideaca Knowledge Services
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Students rated our Python 2.x Training for Scientists and Engineers trainers 9.29 out of 10 based on 11 reviews

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