Numerical Computing with Python: Harness the power of Python to analyze and find hidden patterns in the data

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.

You will learn how to use Pandas, Python’s popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.

By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.

This Learning Path includes content from the following Packt products:

Statistics for Machine Learning by Pratap Dangeti
Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim
Pandas Cookbook by Theodore Petrou
What you will learn
Understand the statistical fundamentals to build data models
Split data into independent groups
Apply aggregations and transformations to each group
Create impressive data visualizations
Prepare your data and design models
Clean up data to ease data analysis and visualization
Create insightful visualizations with Matplotlib and Seaborn
Customize the model to suit your own predictive goals
Who this book is for
If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.

Table of Contents
Journey from Statistics to Machine Learning
Tree-Based Machine Learning Models
K-Nearest Neighbors and Naive Bayes
Unsupervised Learning
Reinforcement Learning
Hello Plotting World!
Visualizing Online Data
Visualizing Multivariate Data
Adding Interactivity and Animating Plots
Selecting Subsets of Data
Boolean Indexing
Index Alignment
Grouping for Aggregation, Filtration, and Transformation
Restructuring Data into a Tidy Form
Combining Pandas Objects