Python Training for Scientists and Engineers (PYT401)

Our Python Training for Scientists and Engineers course covers a wide range of topics, from fundamental concepts to advanced programming techniques and data manipulation. It's designed for beginners and experienced developers looking to enhance their Python skills and expand their knowledge in various Python libraries and tools.

The course begins with The Python Environment, introducing you to starting Python, using the interpreter, running scripts, and understanding Python on different operating systems. You'll also learn about various Python editors and IDEs to enhance your coding efficiency.

In Getting Started, you'll explore variables, built-in functions, strings, numbers, and type conversions. This module also covers essential concepts like writing output, string formatting, and command-line parameters.

Flow Control delves into managing program logic with conditional statements, relational and boolean operators, and loops. You'll learn how Python uses whitespace for code structure and explore different ways to exit loops.

The module on Lists and Tuples introduces Python's sequence types, including lists and tuples. You'll learn about indexing, slicing, iteration, and functions applicable to all sequences, as well as advanced topics like list comprehensions and generator expressions.

In Working with Files, you'll learn how to handle file input/output, including reading from and writing to text files, and working with binary data.

Dictionaries and Sets explores these essential data structures. You'll learn when and how to use dictionaries, iterate through them, and work with sets for efficient data storage and manipulation.

The Functions module teaches you to define and use functions, manage parameters, understand variable scope, return values, and work with lambda functions.

Exception Handling covers Python's error management capabilities, including syntax errors, exceptions, and how to handle them with try, except, else, and finally clauses.

You'll explore OS Services through the os module, managing environment variables, executing external commands, and navigating file systems, including walking directory trees and handling dates and times.

The module on Modules and Packages introduces Python's modular structure, including initialization code, namespaces, documentation, and the use of imports to manage complex projects effectively.

In Classes, you'll learn object-oriented programming with Python, including defining classes, constructors, instance methods, attributes, inheritance, and multiple inheritance.

Programmer Tools explores tools like pylint for code quality, unit testing with the unittest module, debugging, benchmarking, and profiling Python applications to optimize performance.

The course also covers working with Excel Spreadsheets using the openpyxl module to read, create, and modify spreadsheets.

XML and JSON teaches you how to create and parse XML files, use XPath for navigating XML, and read and write JSON data, essential for working with APIs and data exchange formats.

The iPython and Jupyter module introduces interactive computing tools, covering the basics of iPython and Jupyter for enhanced data analysis and visualization.

NumPy explores Python's scientific stack, focusing on creating and manipulating arrays, understanding array shapes, slicing, indexing, and leveraging NumPy's powerful mathematical functions.

You'll dive into SciPy, learning about its packages and functions for scientific computing, including practical examples for numerical analysis.

The module on pandas provides a deep dive into this powerful data manipulation library, covering Series, DataFrames, reading and writing data, and merging and joining datasets.

Finally, in matplotlib, you'll learn to create plots, customize styles, visualize data in various formats, and save images, providing powerful tools for presenting your data effectively.