anhnguyendepocen / statistical-methods---applied-mathematics-in-data-science

Statistical Methods & Applied Mathematics in Data Science, Published by Packt

Statistical Methods and Applied Mathematics in Data Science [Video]

This is the code repository for Statistical Methods and Applied Mathematics in Data Science [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

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.

What You Will Learn

• 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

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
This course is intended for anyone interested in machine learning and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of calculus, linear algebra, and probability theory (real-valued functions; integrals and derivatives; differential equations; matrices; vector spaces; probabilities; random variables; and more) is expected.

Technical Requirements

This course has the following software requirements:
Jupyter notebook, IPython, Python 3.6

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