Statistical Methods and Applied Mathematics in Data Science [Video]

5 (1 reviews total)
By Cyrille Rossant
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  1. Statistical Data Analysis

About this video

Machine learning and data analysis are the center of attraction for many engineers and scientists. The reason is quite obvious: its vast application in numerous fields and booming career options. And Python is one of the leading open source platforms for data science and numerical computing. IPython, and its associated Jupyter Notebook, provide Python with efficient interfaces to for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. If you are among those seeking to enhance their capabilities in machine learning, then this course is the right choice.

Statistical Methods and Applied Mathematics in Data Science provides many easy-to-follow, ready-to-use, and focused recipes for data analysis and scientific computing. This course tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. You will apply state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. In short, you will be well versed with the standard methods in data science and mathematical modeling.

The code bundle for the video course is available at: https://github.com/PacktPublishing/Statistical-Methods---Applied-Mathematics-in-Data-Science.

Style and Approach

This practical, hands-on course will teach you how to analyze and visualize all kinds of data in Jupyter Notebook.

Publication date:
July 2018
Publisher
Packt
Duration
2 hours 38 minutes
ISBN
9781789539219

About the Author

  • Cyrille Rossant

    Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College London. He is a graduate of École Normale Supérieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and Collège de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization.

    He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.

    Browse publications by this author

Latest Reviews

(1 reviews total)
Because it did provide me with what I needed to know, Cheers big ears.

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