# 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, scikit-learn, 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.

* 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 data-intensive 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 high-performance 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

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 k-nearest 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

It is an essential reference book for scientific computing in Python. Because it describes all Python Data Science tools, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, 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 Scikit-Learn 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 machine-learning application with Python and the scikit-learn 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 text-specific 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 the 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 non-linear 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 real-world 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

**About This Book**

- Second edition of the bestselling book on Machine Learning
- A practical approach to key frameworks in data science, machine learning, and deep learning
- Use the most powerful Python libraries to implement machine learning and deep learning
- Get to know the best practices to improve and optimize your machine learning systems and algorithms

**What you will learn**

- Understand the key frameworks in data science, machine learning, and deep learning
- Harness the power of the latest Python open source libraries in machine learning
- Explore machine learning techniques using challenging real-world data
- Master deep neural network implementation using the TensorFlow library
- Learn the mechanics of classification algorithms to implement the best tool for the job
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Delve deeper into textual and social media data using sentiment analysis

This book will teach you everything of data science from simple to the most complex algorithms available in the 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 real-world problems
- Advanced Regression methods for model building and variable selection
- Understanding and implementation of Ensemble methods
- Solving real-world 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

**Author:**Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta

**Published at:**29/09/2014

**ISBN:**1783980249

**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 a hands-on 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 Application-oriented Analyses Using Tax Data
- Working with Social Graphs

This book offers you how to accomplish the fundamental tasks that occupy data scientist. 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, Scikit-learn 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 one-stop 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 non-relational 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 medium-scale 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 entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option.

Python is the most popular programming language in scientific computing today. This series is for people who want to start using Python 3 and its popular extension libraries quickly. I assume you are familiar with Python. This short introductory volume 1 is intended to get you started with scientific Python distribution necessary to run examples from other volumes.

**What You Will Learn:**

- Obtain and install Winpython or Anaconda Python distribution
- Start a Jupyter (formerly IPython) notebook
- Use IDLE and Spyder integrated development environment
- Gives an overview of the topics covered in the following volumes

**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 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.

**Author:**Prabhanjan Tattar,Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta

**Published at:**29/06/2017

**ISBN:**1787129624

**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 real-world projects in data science using R and Python
- Easy-to-follow 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 tree-based 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 SciKit-learn, 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.

**Real-world 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**, step-by-step 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 third-party 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.