Course Outline

Introduction

  • Free and General Purpose vs Not Free or General Purpose

Setting up a Python Development Environment for Data Science

The Power of Matlab for Numerical Problem Solving

Python Libraries and Packages for Numerical Problem Solving and Data Analysis

Hands-on Practice with Python Syntax

Importing Data into Python

Matrix Manipulation

Math Operations

Visualizing Data

Converting an Existing Matlab Application to Python

Common Pitfalls when Transitioning to Python

Calling Matlab from within Python and Vice Versa

Python Wrappers for Providing a Matlab-like Interface

Summary and Conclusion

Requirements

  • Experience with Matlab programming.

Audience

  • Data scientists
  • Developers
 14 Hours

Need help picking the right course?

Testimonials (5)

The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)

Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask

Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)

Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI

It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course

Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask

Trainer develops training based on participant's pace

Farris Chua
Course - Data Analysis in Python using Pandas and Numpy

As I was the only participant the training could be adapted to my needs.

Kevin THIERRY
Course - Web Development with Web2Py

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