More Information
  • Master all Jupyter Notebook features
  • Visualize data and create interactive plots in Jupyter Notebook
  • Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn)
  • Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV
  • Simulate deterministic and stochastic dynamical systems in Python
  • Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory

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:

Style and Approach

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

  • Get insights into data, then learn and make predictions from it
  • Become an expert in high-performance computing and visualization for data analysis and scientific modeling
  • Comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations
Course Length 2 hours 38 minutes
ISBN 9781789539219
Date Of Publication 29 Jul 2018


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.