Python Training for Scientists and Engineers

4.6 out of 5 - Read Testimonials

This Python class teaches scientists and engineers to use 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.

This is a hands-on programming class. All concepts are reinforced with informal practice during the lecture followed by lab exercises.

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.

Course Topics

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 Jupyter notebooks for ad hoc calculations, plots, and what-if?.

Outline

The Python Environment
1. Starting Python
2. Using the Interpreter
3. Running a Python Script
4. Python Scripts on Unix
5. Python Scripts on Windows
6. Python Editors and IDEs
Getting Started
1. Using Variables
2. Built-in Functions
3. Strings
4. Numbers
5. Converting among Types
6. Writing to the Screen
7. String Formatting
8. Command Line Parameters
Flow Control
1. About Flow Control
2. What's with the White Space?
3. if and else
4. Conditional Expressions
5. Relational Operators
6. Boolean Operators
7. while Loops
8. Alternate Ways to Exit a Loop
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. Operators and Keywords for Sequences
8. Nested Sequences
9. List Comprehensions
10. Generator Expressions
Working with Files
1. Text file I/O
2. Opening a Text File
3. Reading a Text File
4. Writing to a Text File
5. "Binary" (Raw, or Non-delimited) Data
Dictionaries and Sets
1. About Dictionaries
2. When to Use Dictionaries
3. Creating Dictionaries
4. Iterating through a Dictionary
5. About Sets
6. Creating Sets
7. Working with Sets
Functions
1. Defining a Function
2. Function Parameters
3. Variable Scope
4. Returning Values
5. Lambda Functions
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
OS Services
1. The os Module
2. Environment Variables
3. Launching External Commands
4. Paths, Directories, and Filenames
5. Walking Directory Trees
6. Dates and Times
Modules and Packages
1. Initialization code
2. Namespaces
3. Executing modules as scripts
4. Documentation
5. Packages and name resolution
6. Naming conventions
7. Using imports
Classes
1. Defining Classes
2. Constructors
3. Instance methods and data
4. Attributes
5. Inheritance
6. Multiple Inheritance
Programmer 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. Debugging
12. Benchmarking
13. Profiling Applications
Excel Spreadsheets
1. openpyxl module
2. Reading an Existing Spreadsheet
3. Creating a Spreadsheet
4. Modifying a Spreadsheet
XML and JSON
1. Creating XML Files
2. Parsing XML
3. Tags and XPath
4. Reading and Wiritng JSON
iPython and Jupyter
1. About iPython and Jupyter
2. iPython Basics
3. Jupyter Basics
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
SciPy
1. About SciPy
2. SciPy Packages
3. SciPy Examples
pandas
1. About pandas
2. Series
3. DataFrames
4. Reading and Writing Data
5. Indexing and Slicing
6. Merging and Joining Data Sets
matplotlib
1. Creating a plot
2. Commonly Used Plots
3. Customizing Styles
4. Ad hoc data visualization
5. Advanced Usage
6. Saving Images

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.

Python Training for Scientists and Engineers

Training for your Team

What people say about our training