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

by Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou

Overview

Understand, explore, and effectively present data using the powerful data visualization techniques of Python

Key Features

• Use the power of Pandas and Matplotlib to easily solve data mining issues
• Understand the basics of statistics to build powerful predictive data models
• Grasp data mining concepts with helpful use-cases and examples

Book Description

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.

Product Details

ISBN-13:	9781789957228
Publisher:	Packt Publishing
Publication date:	12/21/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	682
File size:	35 MB
Note:	This product may take a few minutes to download.

About the Author

Pratap Dangeti is currently working as a Senior Data Scientist at Bidgely Technologies Bangalore. He has a vast experience in analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies. Allen Yu, PhD, is a Chevening Scholar, 2017-18, and an MSC student in computer science at the University of Oxford. He holds a PhD degree in Biochemistry from the Chinese University of Hong Kong, and he has used Python and Matplotlib extensively during his 10 years of bioinformatics experience. Apart from academic research, Allen is the co-founder of Codex Genetics Limited, which aims to provide a personalized medicine service in Asia through the use of the latest genomics technology. Claire Chung is pursuing her PhD degree as a Bioinformatician at the Chinese University of Hong Kong. She enjoys using Python daily for work and lifehack. While passionate in science, her challenge-loving character motivates her to go beyond data analytics. She has participated in web development projects, as well as developed skills in graphic design and multilingual translation. She led the Campus Network Support Team in college, and shared her experience in data visualization in PyCon HK 2017. Aldrin Yim is a PhD candidate and Markey Scholar in the Computation and System Biology program at Washington University, School of Medicine. His research focuses on applying big data analytics and machine learning approaches in studying neurological diseases and cancer. He is also the founding CEO of Codex Genetics Limited, which provides precision medicine solutions to patients and hospitals in Asia. Theodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data. Some of his projects included using targeted sentiment analysis to discover the root cause of part failures from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid mispricing sales items. Ted received his Masters degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow.
Theodore Petrou is the founder of Dunder Data, a training company dedicated to helping teach the Python data science ecosystem effectively to individuals and corporations. Read his tutorials and attempt his data science challenges at the Dunder Data website.

Table of Contents

Table of Contents

1. Journey from Statistics to Machine Learning
2. Tree-Based Machine Learning Models
3. K-Nearest Neighbors and Naive Bayes
4. Unsupervised Learning
5. Reinforcement Learning
6. Hello Plotting World!
7. Visualizing Online Data
8. Visualizing Multivariate Data
9. Adding Interactivity and Animating Plots
10. Selecting Subsets of Data
11. Boolean Indexing
12. Index Alignment
13. Grouping for Aggregation, Filtration, and Transformation
14. Restructuring Data into a Tidy Form
15. Combining Pandas Objects