Best Python Data Science Books to Learn From Beginner to Advance
Data Science is the emerging new hot tech field, which is a blend of different disciplines including statistics, machine learning, and computer science. Python is increasingly becoming the language for data science. It has a strong set of libraries such as Numpy, Pandas, scikitlearn, Matplotlib, Ipython and Scipy to support its usage in this field.
If you're looking for some books that will help you to understand the basic and advanced concepts of data science, you've come to the right place. Using a data mining application, I've gathered a list of some widely reviewed data science books with Python. So scroll down quickly and pick up the books! read more Here you'll get some best books on Data science using Python.
This is a useful book for the beginner in Data Science. It shows you how fundamental data science tools and algorithms work by implementing them from scratch. With this book, you'll learn how to use data science libraries, frameworks, modules, and toolkits. If you're a bit weak in mathematics and programming, no problem. You'll get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist.
What You Will Learn

A crash course of Python

Basics of linear algebra, statistics, and probability

Exploring and collecting data

How to clean, munge, and manipulate data

Fundamentals of machine learning

Building models such as knearest Neighbors and Naive Bayes

Implementing linear and logistic regression

Building decision trees and neural networks

Working with recommender systems

Natural language processing

Network analysis

Working with MapReduce and databases
Python for Data Analysis offers every aspect of manipulating, processing, cleaning, and crunching data in Python. Following a practical approach, it provides a modern introduction to scientific computing in Python. And helps you to design dataintensive applications. With this book, you’ll explore and learn the Python language and libraries you need to effectively solve a broad set of data analysis problems.
What You Will Learn

Using the IPython interactive shell as your primary development environment

Understanding basic and advanced NumPy (Numerical Python) features

Working with the data analysis tools in the Pandas library

Using highperformance tools to load, clean, transform, merge, and reshape data

Applying the panda's group facility to slice, dice, and summarize datasets

Building scatter plots and static or interactive visualizations with matplotlib

Solving problems in web analytics, social sciences, finance, and economics
It is an essential reference book for scientific computing in Python. Because it describes all Python Data Science tools, including IPython, NumPy, Pandas, Matplotlib, ScikitLearn, and other related tools. With this book, you'll learn how to manipulate, transform, and clean data. And visualizing different types of data.
What You Will Learn

Using IPython and Jupyter for computational environments

Using NumPy for efficient storage and manipulation of dense data arrays

Using Pandas for manipulation of labeled/columnar data

Working with Matplotlib for a flexible range of data visualizations

Using ScikitLearn for efficient and clean Python implementations of machine learning algorithms

Using data to build statistical or machine learning models
Introduction to Machine Learning with Python will teach you how effectively you can use machine learning for commercial applications and research projects. It shows you the steps necessary to create a successful machinelearning application with Python and the scikitlearn library. It mainly focuses on the practical aspects of using machine learning algorithms, rather than the math behind them. With this book, you can build your own machine learning solutions easily.
What You Will Learn

Fundamental concepts and applications of machine learning

Advantages and shortcomings of widely used machine learning algorithms

Advanced methods for model evaluation

Useful techniques for parameter tuning

Using pipelines for chaining models

Encapsulating your workflow

Working with text data, including textspecific processing techniques

Representing data processed by machine learning
Python for Data Science For Dummies is useful to beginners who have a little knowledge about Python and data science. It provides you with a basic understanding of Python data analysis programming and statistics to help you build a solid foundation in data science concepts. With the help of this book, you’ll get familiar with the Python development environment. Completing this guide, you can manipulate data, design compelling visualizations, and solve scientific computing challenges.
What You Will Learn:

Basic data science concepts including probability, random distributions, hypothesis testing, and regression models

Understanding the role of objects, functions, modules, and libraries in data analysis

Conditioning and shaping your data

Understanding of MatPlotLib

Using NumPy, SciPy, BeautifulSoup, and Pandas

Visualizing the data

Clustering foundation

Logistic regression

Using nonlinear transformation
This is a comprehensive data science book that covers the analytics, programming, and business skills necessary to master the discipline. It addresses classical statistics to help you think critically about the interpretation of data and its common pitfalls. It also describes classic machine learning algorithms, from their mathematical foundations to realworld applications. With this book, you'll mainly learn the practical use of the tools, however, when theory is required, it is done in an intuitive way to encourage your critical thinking and creativity.
What You Will Learn

Overview of the languages you'll need to learn

String manipulation, Regular expression, and data cleaning

Fundamentals of machine learning

Unsupervised learning including clustering and dimensionality reduction

Foundation of big data

Working with databases

Natural language processing

Time series analysis

Basics of probability and statistics

Understanding of computer memory and data structure
This book will teach you everything about data science from simple to the most complex algorithms available in Data Science. It shows you how to mine data and derive intelligence from it. For each concept, it provides simple and efficient Python recipes that will not only show you how to implement these algorithms but also clarify the underlying concept thoroughly. With this book, you'll understand the concepts of data mining with extensive coverage of machine learning methods. You'll also learn a number of Python libraries available to help implement machine learning and data mining routines effectively.
What You Will Learn

Understanding the complete range of Data Science algorithms

How to analyze your data with Python

Useful tricks to create the most accurate data science models

Using Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively

Create meaningful features to solve realworld problems

Advanced Regression methods for model building and variable selection

Understanding and implementation of Ensemble methods

Solving realworld problems using a variety of different datasets from numerical and text data modalities

Understanding widely used algorithms such as Gradient Boosting, Random Forest, and Rotation Forest
Practical Data Science Cookbook is an essential guide to learn how to work with data. It'll give you a deeper insight into a world of Big Data that promises to keep growing. It provides you with handson exploration of data science. With a comprehensive range of recipes on fundamental data science tasks, you'll uncover practical steps to produce powerful insights into Big Data using R and Python.
What You Will Learn

Preparing Your Data Science Environment

How to tackle every step in the data science pipeline

Understanding to acquire, clean, analyze, and visualize data

Effective data visualization with an automobile fuel efficiency data project

Data modeling with stock market data project

Numerical computing with NumPy and SciPy

Creating data simulations

Producing sharp insights into social media data

Visually Exploring Employment Data

Creating Applicationoriented Analyses Using Tax Data

Working with Social Graphs
This book offers you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll learn how to deal with data at scale and gain a solid foundation in data science. It gives you practical experience with the most popular Python data science libraries, Scikitlearn and StatsModels. Completing this book, you’ll have the solid foundation you need to start a career in data science.
What You Will Learn

Overview of data science

Understanding the data science process

Introduction to Machine learning

Using Python to work with data

Handling large data on a single computer

Making effective data visualization models

Using NoSQL

Working with graph databases

Text mining and text analytics

Writing data science algorithms
Python Data Science Essentials will guide you across all the data munging and preprocessing phases. It'll explain all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results.
What You Will Learn

Setting up your data science toolbox

Making data ready for your data science project

Introduction to all the essential tools in data science

Manipulate, fix, and explore data in order to solve data science problems

Setting up an experimental pipeline to test your data science hypothesis

Using the most suitable algorithm for your data science tasks

Optimizing your machine learning models to get the best performance

Social Network Analysis
This onestop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and nonrelational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of mediumscale projects that are fun to work on and look good on your resume.
Keep this handy quick guide at your side whether you're a student, an entrylevel data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option.
About This Book

Take your first steps in the world of data science by understanding the tools and techniques of data analysis

Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods

Learn how to use Apache Spark for processing Big Data efficiently
What You Will Learn

Learn how to clean your data and ready it for analysis

Implement the popular clustering and regression methods in Python

Train efficient machine learning models using decision trees and random forests

Visualize the results of your analysis using Python’s Matplotlib library

Use Apache Spark’s MLlib package to perform machine learning on large datasets
This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books, the authors present these concepts through real case studies. The application topics span from Foodwebs to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to the definition and implementation of the most important network models.
The text provides information on the structure of the data and on the quality of available datasets. Furthermore, it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose, the authors have set up a dedicated website where readers can download and test the codes. The whole project is aimed as a learning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks.
About This Book

Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data

Get beyond the theory and implement realworld projects in data science using R and Python

Easytofollow recipes will help you understand and implement the numerical computing concepts
What You Will Learn

Learn and understand the installation procedure and environment required for R and Python on various platforms

Prepare data for analysis by implementing various data science concepts such as acquisition, cleaning and munging through R and Python

Build a predictive model and an exploratory model

Analyze the results of your model and create reports on the acquired data

Build various treebased methods and Build random forest
Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKitlearn, Pandas, Numpy, and others.
The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference.
The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book.
Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book.
Realworld data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis.
Offering detailed, stepbystep instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using thirdparty tools to facilitate access and shareability, from Google Docs to GitHub.
Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.