Best Books for Data Science in 2021 for all Level of People
Statistical methods play a key role in data science. There are some excellent introductory and advanced level textbooks for data scientists which explains how to apply various statistical methods to data science, how to avoid their misuse and gives you advice on what's important and what's not.
Here you will get some of the best books for data science in 2021. Check them out and find the perfect ones for you!
In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.
You’ll learn how to:
- Think statistically and understand the role variation plays in your life and decision making
- Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace
- Understand what’s really going on with machine learning, text analytics, deep learning, and artificial intelligence
- Avoid common pitfalls when working with and interpreting data
This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.
- Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more
- Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot
- Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment
- Tie everything together into a repeatable machine learning operations pipeline
- Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka
- Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond the simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits your data, and clearly communicate your message.
The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer science and data science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.
- Includes a concise introduction to Python 3 and linear algebra
- Provides a thorough introduction to data visualization and regular expressions
- Covers NumPy, Pandas, R, and SQL
- Introduces probability and statistical concepts
- Features numerous code samples throughout
- Companion files with source code and figures
This book covers:
- Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management
- Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
- Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
- Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management
- Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
- NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
This is a great informative book for those who are newer, and a little more experienced. This is a good introduction to practical statistics which provided a number of excellent practical logical explanations. People who are interested in statistics and data science find this book very helpful.
Charles Wheelan clarifies key concepts such as
- Regression analysis
- Randomized experiments
- Hypothesis tests
- Issues related to confidence level and p-value.
The writer reveals how biased or careless parties can manipulate or misrepresent data. He again shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
Statistics with R is a great book for beginning data analysis. A beginner will quickly be able to use data analysis tools such as ggplot2 and dplyr etc. Students and working professionals find this book very informative. It provides an integrated treatment of statistical inference techniques in data science using the R statistical software.
So we can say that this is an awesome resource for all levels who want to reach the depth of statistics and data science.
An Introduction to Statistical Learning provides you the right amount of theory and practice. This data science book requires no prior knowledge of calculus or linear algebra though it is an outstanding introduction to statistical learning.
This book provides you
- An accessible overview of the field of statistical learning
- An essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics
- Some of the most important modeling and prediction techniques, along with relevant applications
- Linear regression
- Resampling methods
- Shrinkage approaches
- Tree-based methods
- Support vector machines, clustering, and more
Each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open-source statistical software platform.
Practical Statistics for Data Scientists is an excellent introductory textbook for data scientists which explains how to apply various statistical methods to data science, how to avoid their misuse, and gives you advice on what's important and what's not. This is a good reference book as all the explanations are very clear.
This book includes
- Python code
- The curse of dimensionality
- A discussion of neural networks.
You’ll learn from this book
- 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
- Unsupervised learning methods for extracting meaning from unlabeled data
What You'll Learn
- Navigating the software
- Input and output
- Data structures
- Data transformations
- Strings and date
- General statistics
- Linear regression an ANOVA
- Useful tricks
- Beyond basic numerics and statistics
- Time series analysis.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan is a nice and short introduction to statistical modeling. In this book, the author includes the basics of regression to multilevel models. He also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
- The Golem of Prague
- Small worlds and large worlds
- Sampling the imaginary
- Linear models
- Multivariate linear models
- Overfitting and model comparison
- Markov chain Monte Carlo Estimation
- Big entropy and the generalized linear model
- Counting and classification
- Monsters and mixtures
- Multilevel models
- Adventures in covariance
- Missing data and other opportunities
Statistics: The Art and Science of Learning from Data includes a chapter summary and chapter problems at the end of every chapter. It includes an online review for a better understanding of the topics. It also explores data with graphs and numerical summaries, the association between two categorical variables, two quantitative variables, good and poor ways to sample and experiment, probability distributions and much much more.
This book is divided into four parts. These are
- Gathering and exploring data
- Probability, probability distribution, and sampling distributions
- Inferential statistics
- Analyzing association and extended statistical method
This is the best book for data science with excellent data about data analysis. It provides useful advice that applies in the real world jobs and techniques. This book is divided into five parts which are the research process and data collection, describing data, testing hypotheses, exploring relationships, and writing a research paper.
Key coverage is
- The research process
- Sampling techniques
- Questionnaire design
- An introduction to Stata
- Preparing and transforming your data
- Descriptive statistics
- The normal distributions
- Linear regression analysis and diagnostics
- Regression analysis with categorical dependent variables.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists but also how to participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest neighbours, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.
With this handbook, 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 labelled/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
You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- Explore the machine learning landscape, particularly neural nets
- Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the Tensor Flow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.
It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts.
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.
While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect:
- Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value
- Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL
- Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things
- Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate