Python Programming for Scientists, Engineers and Analysts – PYT450 – 3 Days
This unique Python training course is specifically designed with scientists, engineers and analysts types in mind. This 3 day Python programming training for Scientists, Engineers and Analysts course is for those participants that have the equivalent skills that are learned in our Introduction to Python Programming training.
Python Online Course Available For Scientists
Python Programming for Scientists, Engineers and Analysts – PYT450 – 3 Days Request a Class Date
This course is for experienced Python programmers that need to use Python to work with crunching data, manipulating arrays, performing statistical calculations, and plotting results.
Additional Multi-Student Enrollment Discounts at Check-Out
This Python for Scientist starts with some essential Python programming fundamentals and then quickly focuses specifically on the Python, NumPy, SciPy, SymPy, and other specific skill areas scientists, engineers, statisticians and mathematicians need to work with crunching data, manipulating arrays, performing statistical calculations, and plotting results.
This is truly Python training for scientific computing. This is a hands-on python programming class. Each training module is reinforced with informal practice and dedicated by lab exercises.
Python for Scientist is offered as a live, instructor-led public or private online, onsite or classroom style instruction. Since many of Firebox Training’s Python training courses are delivered on-site at our clients’ facilities for small groups, we can often tailor fit the course to meet specific learning objectives. Many of our clients have multiple locations. So, we offer this course as a live, instructor-led online training course for small groups. Additionally the Python Programming Training for Scientist, Engineers and Analyst is available as live, online public classes for individuals. Remember our online courses work for those with multiple locations and time zones, while saving money on the instructor’s or students’ travel expenses.
Expected Outcomes of Python for Scientist Course
By the end of the course participants can expect to 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, gain 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, and solve equations with SymPy.
Course ID: PYT450 Duration: 3 days
Audience:Participants must of the equivalent skill found in our Introduction to Python Programming training course. This course is designed for those that have a need to use Python to work with crunching data, manipulating arrays, performing statistical calculations, and plotting results.
Prerequisites: Introduction to Python topic are covered in the first three days of the class. Certainly previous object-oriented programming experience would be helpful, but not required.
Python Programming for Scientists Course Outline
Working with Files
- Reading from Files
- Reading Lines from Files
- Reading JSON from Files
- Writing and Appending to Files
- Using ‘with’ to Manage Resources
- File Attributes
- Hands-on Lab Exercises
Python Classes
- Introduction to Object-Oriented Python
- Creating Your First Class
- Inheritance
- Multiple Inheritance and Method Resolution Order
- Accessing Attributes
- Superclass Methods
- Method Overloading
- Class Attributes
- Static and Class Methods
- Hands-on Lab Exercises
Unit Testing
- Introduction to Unit Testing
- The unittest Module
- Assertions
- Test Suites
- Hands-on Lab Exercises
Day 2
Introduction to Scientific Python
- Introduction to Scientific Python (SciPy)
- SciPy Distributions
- SciPy Resources
- Overview of iPython
- Launching iPython
- Common iPython Commands
- Tab Completion
- Magics
- %run and %edit Magics
- %paste and %cpaste
- %reset and %xdel. Debugging
- Command History and External Commands
- iPython Notebook
- Creating a Notebook
- iPython Notebook Operations
- Hands-on Lab Exercises
NumPy
- NumPy Basics
- Working with ndarray
- Creating Arrays
- Shaping Arrays
- Referencing Arrays
- Printing Arrays
- Copying Arrays
- Performing Operations on Array
- Example – Operations on Matrices
- The numpy.random Modul
- Aggregate Functions on Arrays
- Universal Function
- Universal Functions – Some Examples
- Array Broadcasting
- Working with Boolean Arrays
- Slicing Arrays
- Broadcasting with Slices
- Iterating Over Arrays
- Stacking Arrays
- Splitting Arrays
- Hands-on Lab Exercises
- Introduction to the Pandas Module
- Data Structures in pandas
- Creating a Simple Series Data Structure
- Series with Custom Labels
- Index Objects
- The dateTimeIndex Object
- Slicing Series
- More Series Operations
- Data Frames
- Constructing Data Frames
- More DataFrame Constructors
- Adding DataFrame Columns
- Deleting and Inserting DataFrame Columns
- Selecting from Data Frames – Quick Cheatsheet
- Dataframe Addition and Subtraction
- Boolean Operations
- Transposing DataFrames
- Reading Data from Sources
- Hands-on Lab Exercises
Day 3
The scipy Submodules
- Overview of SciPy Subpackages
- Getting More Information
- Polynomials
- scipy.special
- scipy.cluster
- scipy.constants
- Fast Fourier Transformations
- Integration
- Interpolation
- IO
- Linear Algebra
- The ndimage Module
- Optimize
- Signal
- Sparse
- Spatial
- Special
- Stats
- Weave
- Hands-on Lab Exercises
Matplotlib
- Matplotlib Overview
- Matplotlib Architecture
- Basic Matplotlib Concepts
- Creating a Simple Plot
- Multiple Lines on a Plot
- Line Properties
- Multiple Plots
- Hands-on Lab Exercises
The Imaging Library
- Overview of the Imaging Library
- Supported File Types
- The Image Class
- Creating Images
- Drawing Text on Images
- The Coordinate System
- Creating Thumbnails
- Cropping, Pasting, and Transposing Images
- Hands-on Lab Exercises
