Statistical Methods and Applied Mathematics in Data Science

Video Description

Use IPython and Jupyter Notebook to sharpen your skills for your data analysis and visualization tasks

About This Video

  • 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

In Detail

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:

Table of Contents

  1. Chapter 1 : Statistical Data Analysis
    1. The Course Overview 00:04:54
    2. Exploring a Dataset with pandas and Matplotlib 00:05:41
    3. Estimating the Correlation Between two Variables 00:06:03
    4. Fitting a Probability Distribution to Data with the Maximum Likelihood Method 00:06:47
    5. Estimating a Probability Distribution Non-parametrically 00:03:54
    6. Analyzing Data with the R Programming Language 00:04:55
  2. Chapter 2 : Machine Learning
    1. Getting Started with scikit-learn 00:06:35
    2. Learning to Recognize Handwritten Digits 00:03:44
    3. Using Support Vector Machines for Classification Tasks 00:03:09
    4. Using a Random Forest to Select Important Features for Regression 00:04:43
    5. Detecting Hidden Structures in a Dataset with Clustering 00:02:36
  3. Chapter 3 : Numerical Optimization
    1. Finding the Root of a Mathematical Function 00:03:38
    2. Minimizing a Mathematical Function 00:05:52
    3. Fitting a Function to Data with Nonlinear Least Squares 00:03:15
    4. Finding the Equilibrium State of a Physical System 00:04:17
  4. Chapter 4 : Signal Processing
    1. Analyzing the Frequency Components of a Signal 00:05:53
    2. Applying a Linear Filter to a Digital Signal 00:05:39
    3. Computing the Autocorrelation of a Time Series 00:05:00
  5. Chapter 5 : Image and Audio Processing
    1. Manipulating the Exposure of an Image 00:03:23
    2. Applying Filters on an Image 00:02:05
    3. Segmenting an Image 00:03:28
    4. Finding Points of Interest in an Image 00:03:24
    5. Applying Digital Filters to Speech Sounds 00:02:34
    6. Creating a Sound Synthesizer in the Notebook 00:01:49
  6. Chapter 6 : Deterministic Dynamical Systems
    1. Plotting the Bifurcation Diagram of a Chaotic Dynamical System 00:05:12
    2. Simulating an Elementary Cellular Automaton 00:04:32
    3. Simulating an Ordinary Differential Equation with SciPy 00:04:01
    4. Simulating a Partial Differential Equation — Reaction-Diffusion Systems and Turing Patterns 00:04:59
  7. Chapter 7 : Stochastic Dynamical Systems
    1. Simulating a Discrete-time Markov Chain 00:06:46
    2. Simulating a Poisson Process 00:04:14
    3. Simulating a Brownian Motion 00:03:49
    4. Simulating a Stochastic Differential Equation 00:04:58
  8. Chapter 8 : Graphs, Geometry, and Geographic Information Systems
    1. Manipulating and Visualizing Graphs with NetworkX 00:03:06
    2. Drawing Flight Routes with NetworkX 00:04:30
    3. Resolving Dependencies in a Directed Acyclic Graph 00:03:05
    4. Computing Connected Components in an Image 00:03:12
    5. Manipulating Geospatial Data with Cartopy 00:02:48

Product Information

  • Title: Statistical Methods and Applied Mathematics in Data Science
  • Author(s): Cyrille Rossant
  • Release date: July 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789539219