Scientific Computing

Get to grips with aspects of numerical computing and understand NumPy, the powerful numerical computing module, using Python!

Perform advanced data manipulation tasks using pandas and become an expert data analyst.

Build high-performance, distributed, and concurrent applications in Python

Use algorithm design, hardware features, and parallelism to build fast, accurate, and efficient scientific code

Enhance the power of NumPy and start boosting your scientific computing capabilities

Gain hands-on experience in data analysis and visualization with IPython and Jupyter Notebook

Get to grips Python’s popular packages that make Data Analysis possible

Master the capabilties of SciPy and put them to use to solve your numeric and scientific computing problems

Learn to solve scientific computing problems using Scala and its numerical computing, data processing, concurrency, and plotting libraries

Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack

Get started with Python for data analysis and numerical computing in the Jupyter notebook

Explore the Moodle Gradebook and discover how to set up and customize it to track students' progress

Compute scientific data and execute code interactively with NumPy and SciPy

Learn to use SymPy's symbolic engine to simplify Python calculations