Python Programming for Scientists

PYTH-120 (5 Days)
4.4 out of 5 (110 reviews)  

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Python Training Overview

Accelebrate's Python Programming for Scientists training course teaches scientists, mathematicians, statisticians, and engineers to use Python for scientific and mathematical computing. Attendees learn the basics as well as the most important Python modules for working with data, from arrays, to statistics, to plotting results. The material is geared towards scientists and engineers.

Location and Pricing

Most Accelebrate courses are delivered as private, customized, on-site training at our clients' locations worldwide for groups of 3 or more attendees and are custom tailored to their specific needs. Please visit our client list to see organizations for whom we have delivered private in-house training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote for private training at your site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

Python Training Objectives

  • 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?

Python Training Outline

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The Python Environment
  • About Python
  • Starting Python
  • Using the interpreter
  • Running a Python script
  • Python scripts on Unix/Windows
  • Using the Spyder editor
Getting Started
  • Using variables
  • Builtin functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • String formatting
  • Command line parameters
Flow Control
  • About flow control
  • White space
  • Conditional expressions (if,else)
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits
Sequences
  • About sequences
  • Lists and tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions, keywords, and operators
  • List comprehensions
  • Generator expressions
  • Nested sequences
Working with files
  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Raw (binary) data
Dictionaries and Sets
  • Creating dictionaries
  • Iterating through a dictionary
  • Creating sets
  • Working with sets
Functions
  • Defining functions
  • Parameters
  • Variable scope
  • Returning values
  • Lambda functions
Errors and Exception Handling
  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions
OS Services
  • The os module
  • Environment variables
  • Launching external commands
  • Walking directory trees
  • Paths, directories, and filenames
  • Working with file systems
  • Dates and times
Modules and packages
  • Initialization code
  • Namespaces
  • Executing modules as scripts
  • Documentation
  • Packages and name resolution
  • Naming conventions
  • Using imports
Classes
  • Defining classes
  • Constructors
  • Instance methods and data
  • Attributes
  • Inheritance
  • Multiple inheritance
Programmer tools
  • Analyzing programs with pylint
  • Creating and running unit tests
  • Debugging applications
  • Benchmarking code
  • Profiling applications
Excel spreadsheets
  • The openpyxl module
  • Reading an existing spreadsheet
  • Creating a spreadsheet from scratch
  • Modifying an existing spreadsheet
Serializing Data
  • Creating XML Files
  • Parsing XML
  • Finding by tags and XPath
  • Reading JSON files
  • Writing JSON
iPython and Jupyter
  • About iPython and Jupyter
  • iPython basics
  • Magic commands
  • About Jupyter
  • Documentation cells
NumPy
  • NumPy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks
pandas
  • Pandas overview
  • Series and Dataframes
  • Reading and writing data
  • Advanced indexing and slicing
  • Merging and joining data sets
SciPy
  • What is SciPy
  • What you do with SciPy?
  • Tour of SciPy packages
  • Simple SciPy examples
Matplotlib
  • Creating a basic plot
  • Commonly used plots
  • Customizing styles
  • Ad hoc data visualization
  • Advanced usage
  • Saving images
Conclusion
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Lecture percentage

35%

Lecture/Demo

Lab percentage

65%

Lab

Course Number:

PYTH-120

Duration:

5 Days

Prerequisites:

While there are no programming prerequisites, programming experience is helpful. Students should have a strong mathematical background and should be comfortable working with files and folders, and be familiar with the command line in Linux, Windows, or Mac OS.

Training Materials:

In addition to the 400+ page course manual, students will receive quick references for Python and iPython.

Software Requirements:

  • Any Windows, Linux, or Mac OS X operating system
  • Python language
  • Python libraries: NumPy, SciPy, matplotlib, PIL, Jupyter, SymPy (we recommend Anaconda, a cross-platform Python bundle that already includes the necessary modules)
  • An IDE with Python support (PyCharm Community Edition is an excellent free option, but there are several other good ones)

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

Contact Us Train For Us

Toll-free in US/Canada:
877 849 1850
International:
+1 678 648 3113

Toll-free in US/Canada:
866 566 1228
International:
+1 404 420 2491

925B Peachtree Street, NE
PMB 378
Atlanta, GA 30309-3918
USA

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