June 24, 2021

Decrypt Info

Because we care, we're security aware.

Best 8 Books to Learn Data Science for Beginners and Experts

0 0
6 min read
0 0
Read Time:6 Minute, 29 Second

Data Science is the most revolutionary field in the tech industry these days! Companies, whether small businesses or tech giants, use data science to understand the market trends to retain their competitive edge. This article covers the best books to learn Data Science for both who are new to the field and those who just want to revise!

1. PYTHON FOR DATA ANALYSIS

Book Description

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You will learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. This book is a practical, modern introduction to data science tools in Python. Itís ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

Book details

  • Author Name: Wes McKinney
  • Paperback : 544 pages
  • ISBN-10 : 9789352136414
  • ISBN-13 : 978-9352136414
  • Product Dimensions : 24 x 18 x 1 cm
  • Publisher : Shroff/O’Reilly
  • Language: English

2. Practical Statistics for Data Scientists

Book Description:

Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

With this you’ll learn:

  • Why exploratory data analysis is a key preliminary step in data science
  • How random sampling can reduce bias and yield a higher quality dataset, even with big data
  • How the principles of experimental design yield definitive answers to questions
  • How to use regression to estimate outcomes and detect anomalies
  • Key classification techniques for predicting which categories a record belongs to
  • Statistical machine learning methods that “learn” from data

Book details

  • Author Names: Peter Bruce, Andrew Bruce
  • Paperback: 320 pages
  • ISBN-10: 9789352135653
  • ISBN-13: 978-9352135653
  • Publisher: Shroff/O’Reilly
  • Language: English

3. R for Data Science

Book Description:

This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

You’ll learn how to:

  • Wrangle—transform your datasets into a form convenient for analysis
  • Program—learn powerful R tools for solving data problems with greater clarity and ease
  • Explore—examine your data, generate hypotheses, and quickly test them
  • Model—provide a low-dimensional summary that captures true “signals” in your dataset
  • Communicate—learn R Markdown for integrating prose, code, and results.

 

Book details

  • Author Names: Hadley Wickham, Garrett Grolemund
  • Paperback : 520 pages
  • ISBN-10 : 1491910399
  • ISBN-13 : 978-1491910399
  • Publisher : O’Reilly Media
  • Language: English

4. Fundamentals of Machine Learning for Predictive Data Analytics

Book Description:

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.

 

 

 

Book details

  • Author Names: John D. Kelleher, Brian Mac Namee, Aoife D’Arcy 
  • Hardcover : 624 pages
  • ISBN-10 : 0262029448
  • ISBN-13 : 978-0262029445
  • Publisher : The MIT Press
  • Language: English

5. Introduction to Machine Learning with Python

Book Description:

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques

Book details

  • Author Names: Andreas C. Miller, Sarah Guido
  • Paperback : 400 pages
  • ISBN-10 : 1449369413
  • ISBN-13 : 978-1449369415
  • Publisher : O’Reilly Media
  • Language: English

6. Python Data Science Handbook

Book description:

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

You’ll learn how to use:
IPython and Jupyter: provide computational environments for data scientists using Python
NumPy: includes the array for efficient storage and manipulation of dense data arrays in Python
Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
Matplotlib: includes capabilities for a flexible range of data visualizations in Python
Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms.

Book details

  • Author Name: Jake VanderPlas
  • Paperback : 548 pages
  • ISBN-10 : 1491912057
  • ISBN-13 : 978-1491912058
  • Publisher : O’Reilly Media
  • Language: English

7. Deep Learning

Book description:

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human-computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

 

 

Book details

  • Author Names: Ian Goodfellow, Yoshua Bengio, Aaron Courville
  • Hardcover : 800 pages
  • ISBN-10 : 0262035618
  • ISBN-13 : 978-0262035613
  • Publisher : The MIT Press
  • Language: English

8. Mining of Massive Datasets

Book description

This book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

 

 

Book Details

  • Author Names: Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
  • Hardcover : 565 pages
  • ISBN-10 : 1108476341
  • ISBN-13 : 978-1108476348
  • Publisher : Cambridge University Press
  • Language: English

 

 

Happy
Happy
0 %
Sad
Sad
0 %
Excited
Excited
0 %
Sleepy
Sleepy
0 %
Angry
Angry
0 %
Surprise
Surprise
0 %

Average Rating

5 Star
0%
4 Star
0%
3 Star
0%
2 Star
0%
1 Star
0%

Leave a Reply