Dawn Griffiths began her career as a mathematician at a prestigious UK institution. She received a First-Class Honours degree in Mathematics and was awarded a university scholarship to pursue a PhD in differential equations of extremely unusual breeds. She left academics after realizing that people would cease talking to her at gatherings, and instead pursued a job in software development. She currently juggles IT consulting, writing, and mathematics.

Wouldn't it be amazing if there was a statistics book that made histograms, probability distributions, and chi square analysis as fun as going to the dentist? Head First Statistics brings statistics to life through puzzles, stories, quizzes, visual aids, and real-world applications.

Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly methodology will help you learn fundamental concepts and put them to use. Learn how to visually portray data with charts and graphs; understand the difference between taking the average with mean, median, and mode and why it matters; calculate probability and expectation; and much more.

Head First Statistics is appropriate for high school and college statistics students, and it meets the College Board's Advanced Placement (AP) Statistics Exam standards. You'll learn how to:

• Examine the entire scope of first-year statistics topics.
• Take on difficult statistical ideas with Head First's dynamic, graphically rich approach, which has been shown to enhance learning and help you retain knowledge.
• Investigate real-world events ranging from casino gambling to prescription drug testing in order to put statistical ideas into context.
• Learn how to calculate chances, measure spread, and grasp the normal, binomial, geometric, and Poisson distributions.
• Perform sampling, correlation and regression analysis, hypothesis testing, chi square analysis, and other tasks.

You'll not only have learned statistics before you realize it, but you'll also understand how they work in practice. Head First Statistics can help you pass your statistics course while also providing you with a solid understanding of the subject that you may utilize throughout your life.

Author: Dawn Griffiths

Link to buy: https://www.amazon.com/dp/0596527586

Ratings: 4.4 out of 5 stars (from 202 reviews)

Best Sellers Rank: #340,203 in Books

#334 in Statistics (Books)

#568 in Probability & Statistics (Books)

Peter Bruce developed and grew Statistics.com's Institute for Statistics Education, which today provides around 100 statistics courses, roughly a third of which are directed at data scientists. Peter has created a comprehensive vision of the target market as well as his own expertise in reaching it by engaging prominent authors as lecturers and building a marketing strategy to reach professional data scientists.

Andrew Bruce has worked in statistics and data science for over 30 years in university, government, and business. He holds a Ph.D. in statistics from the University of Washington and has multiple publications published in peer-reviewed journals.

Although statistical tools are an important aspect of data science, very few data scientists have formal statistics training. Basic statistics courses and publications rarely approach the subject from a data science standpoint. Practical Statistics for Data Scientists teaches how to apply various statistical methods to data science, how to avoid misusing them, and what's important and what's not.

Many data science resources include statistical approaches but do not provide a comprehensive statistical perspective. If you're familiar with the R programming language and have some experience with statistics, this brief reference bridges the gap in an easy-to-understand way.

Practical Statistics for Data Scientists will teach you:

• Why is exploratory data analysis such an important first step in data science?
• How random sampling can eliminate bias and produce a higher quality dataset even with large amounts of data
• How experimental design concepts offer definitive answers to questions
• How to Estimate Outcomes and Detect Inconsistencies Using Regression
• The most important categorization strategies for determining which category a document belongs to
• Methods of statistical machine learning that "learn" from data
• Methods for extracting meaning from unlabeled data using unsupervised learning.

Author: Peter Bruce and Andrew Bruce

Link to buy: https://www.amazon.com/Practical-Statistics-Data-Scientists-Essential/dp/1491952962

Ratings: 4.5 out of 5 stars (from 396 reviews)

Best Sellers Rank: #328,477 in Books

#94 in Data Warehousing (Books)

#122 in Mathematical & Statistical Software

#165 in Software Testing

Andreas Müller earned his doctorate in machine learning at the University of Bonn. He recently joined the Center for Data Science at New York University after working as a machine learning researcher on computer vision applications at Amazon for a year.

Sarah is a data scientist with experience working with start-ups. She is passionate about Python, machine learning, massive amounts of data, and the computer sector. She is a skilled conference speaker who currently resides in New York City and attended graduate school at the University of Michigan.

Machine learning has become an essential component of many commercial applications and research efforts, but it is not limited to huge corporations with vast research teams. Among the best books on data science, Introduction to Machine Learning with Python will show you practical techniques to develop your own machine learning solutions if you use Python, even if you are a newbie. Machine learning applications are only limited by your creativity with all of the data available today.

You'll learn how to use Python and the scikit-learn library to build a successful machine-learning application. Andreas Müller and Sarah Guido, the authors, concentrate on the practical implications of employing machine learning algorithms rather than the mathematics underlying them. Knowledge of the NumPy and matplotlib libraries will help you get the most out of Introduction to Machine Learning with Python.

This book will teach you:

• Machine learning fundamentals and applications
• The benefits and drawbacks of widely used machine learning algorithms
• How to portray machine learning-processed data, including which data features to emphasize
• Advanced model evaluation and parameter tuning techniques
• The pipeline idea for chaining models and encapsulating your process
• Text data processing methods, including text-specific processing approaches
• Suggestions for enhancing your machine learning and data science abilities.

Author: Andreas Mueller and Sarah Guido

Link to buy: https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413

Ratings: 4.6 out of 5 stars (from 538 reviews)

Best Sellers Rank: #43,269 in Books

#8 in Computer Algorithms

#8 in Natural Language Processing (Books)

#14 in Programming Algorithms

Wes McKinney is a software engineer and entrepreneur based in New York. He moved on to do quantitative finance work at AQR Capital Management in Greenwich, CT after obtaining his undergraduate degree in mathematics at MIT in 2007. Frustrated with tedious data processing tools, he learnt Python and began developing the pandas project. He is now an active member of the Python data community and a proponent of Python's usage in data analysis, finance, and statistical computing applications.

Get detailed instructions in Python for manipulating, processing, cleaning, and crunching datasets. The second version of this hands-on guide, updated for Python 3.6, is jam-packed with practical case studies that show you how to address a wide range of data analysis problems successfully. In the process, you'll learn the most recent versions of pandas, NumPy, IPython, and Jupiter.

Python for Data Analysis provides a comprehensive, current introduction to Python data science tools written by Wes McKinney, the author of the Python pandas project. It is great for analysts who are new to Python as well as Python programmers who are new to data science and scientific computing. GitHub hosts data files and related materials.

• For exploratory computing, use the IPython shell and Jupiter notebook.
• Learn about NumPy's basic and advanced features (Numerical Python)
• Begin with the pandas library's data analysis tools.
• Load, clean, transform, merge, and reshape data with adaptable tools.
• Matplotlib can be used to create useful visualizations.
• To slice, dice, and summarize datasets, use the pandas group by facility.
• Analyze and alter time series data, both regular and irregular.
• With complete, detailed examples, learn how to address real-world data analysis challenges.

Author: Wes McKinney

Link to buy: https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython-dp-1491957662/dp/1491957662

Ratings: 4.6 out of 5 stars (from 1565 reviews)

Best Sellers Rank: #10,797 in Books

#5 in Data Modeling & Design (Books)

#7 in Data Processing

#9 in Python Programming

Charles Wheelan is the best-selling author of Naked Statistics and Naked Economics and a former Economist correspondent. He is a Dartmouth College professor of public policy and economics.

Statistics, often seen to be monotonous, is swiftly transforming into a discipline that Hal Varian, Google's senior economist, has actually called "sexy." The real-world use of statistics continues to expand by leaps and bounds, from batting averages and political polls to game shows and medical studies. How can we catch schools who use standardized examinations to their advantage? How does Netflix know which films you'll enjoy? What is causing the increase in autism cases? As best-selling author Charles Wheelan demonstrates in Naked Statistics, the correct data and a few well-chosen statistical methods can assist us in answering these and other issues.

This book is a lifeline for people who slept through Stats 101. Wheelan cuts through the jargon and technical jargon to get to the basic intuition that drives statistical analysis. He explains fundamental concepts like inference, correlation, and regression analysis, highlights how biased or negligent parties can distort or misrepresent data, and demonstrates how clever and imaginative academics are using great data from natural experiments to answer difficult questions.

And there isn't a dull page in sight, in Wheelan's trademark style. You'll come across brilliant Schlitz Beer marketers utilizing simple probability, an International Sausage Festival revealing the tenets of the central limit theorem, and a mind-boggling choice from the popular game show Let's Make a Deal, and you'll walk away with new ideas each time. Wheelan overcomes the odds once more by bringing to life another crucial, previously unglamorous discipline with the wit, accessibility, and simple enjoyment that made Naked Economics a bestseller. It is regarded as one of the best books on data science.

Author: Charles Wheelan

Link to buy: https://www.amazon.com/dp/039334777X

Ratings: 4.6 out of 5 stars (from 2197 reviews)

Best Sellers Rank: #8,286 in Books

#3 in Statistics (Books)

#7 in Business Statistics

EMC is a global pioneer in transforming businesses and service providers' operations and delivering IT as a service. Cloud computing is essential to this transformation. EMC accelerates the road to cloud computing with revolutionary technologies and services that enable IT departments to store, manage, protect, and analyze their most precious asset — information — in a more agile, trustworthy, and cost-effective manner.

Data Science and Big Data Analytics are concerned with leveraging the power of data to gain fresh insights. The book covers a wide range of activities, methodologies, and tools used by Data Scientists. The content focuses on concepts, principles, and practical applications that are applicable to any industry and technology context, and the learning is supported and explained using examples that can be replicated using free and open-source software.

Data Science and Big Data Analytics will assist you in the following ways:

• Become a member of a data science team.
• Use a structured lifecycle approach to solving data analytics concerns.
• Analyze huge data using proper analytic techniques and tools.
• Learn how to use statistics to make a compelling tale that will motivate company action.
• Prepare to take the EMC Proven Professional Data Science Certification Exam.
• Begin meaningfully discovering, analyzing, visualizing, and presenting data now!

Author: EMC Education Services

Link to buy: https://www.amazon.com/Data-Science-Big-Analytics-Discovering/dp/111887613X

Ratings: 4.3 out of 5 stars (from 178 reviews)

Best Sellers Rank: #550,484 in Books

#136 in Business Operations Research (Books)

#208 in Mathematical & Statistical Software

#305 in Data Mining (Books)

Hadley Wickham is a Rice University Assistant Professor and the Dobelman Family Junior Chair in Statistics. He is an active member of the R community, having written and contributed to over 30 R packages. He was awarded the John Chambers Award for Statistical Computing for his work developing data reshaping and visualization tools.

Garrett Grolemund is a R developer, statistician, and teacher who presently works at RStudio. Data analysis, he believes, is a largely untapped source of value for both industry and science. Garrett earned his Ph.D. at Hadley Wickham's group at Rice University, where his study traced the beginnings of data analysis as a cognitive process and identified how attentional and epistemological concerns influence all data analysis.

Learn how to use R to transform raw data into insight, knowledge, and comprehension. Among the best books on data science, R for Data Science will teach you to R, RStudio, and the tidyverse, a set of R programs that work together to make data science quick, fluent, and enjoyable. R for Data Science is intended for readers with no prior programming expertise and is intended to get you started with data science as soon as possible.

Hadley Wickham and Garrett Grolemund walk you through the steps of importing, wrangling, examining, modeling, and communicating the outcomes of your data. You'll receive a comprehensive, big-picture grasp of the data science cycle, as well as the fundamental tools you'll need to manage the specifics. Each section of the book is accompanied by exercises to help you put what you've learned into practice.

You'll discover how to:

• Wrangle—transform your datasets into an analysis-friendly format.
• 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—create a low-dimensional description of your dataset's true "signals."
• Communicate—learn R Markdown for integrating prose, code, and results.

Author: Hadley Wickham and Garrett Grolemund

Link to buy: https://www.amazon.com/dp/1491910399

Ratings: 4.7 out of 5 stars (from 1320 reviews)

Best Sellers Rank: #21,420 in Books

#4 in Mathematical & Statistical Software

#6 in Statistics (Books)

#12 in Data Processing

Cole Nussbaumer Knaflic uses statistics to tell tales. She is the founder and CEO of storytelling with data (SWD) and the author of the best-selling book, storytelling with data: a data visualization guide for business professionals, which has been translated into a dozen languages, is used as a textbook at over 100 universities, and is the course book for tens of thousands of SWD workshop participants.

Storytelling with Data teaches you the principles of data visualization as well as how to successfully communicate with data. You'll learn about the power of storytelling and how to use data to tell your tale. The ideas in this illuminating text are theoretically based but made accessible through several real-world examples that may be applied immediately to your next graph or presentation.

Storytelling is not a natural skill, especially when it comes to data visualization, and the tools at our disposal make it even more difficult. Storytelling with Data shows how to go beyond traditional methods to get to the heart of your data, and how to utilize your data to tell an interesting, informative, and captivating story.

You will specifically learn how to:

• Recognize the significance of context and audience.
• Choose the best type of graph for your situation.
• Recognize and remove the clutter that is obscuring your information.
• Draw the attention of your viewers to the most crucial aspects of your data.
• Consider yourself a designer, and apply design concepts to data visualization.
• Use the power of narrative to help your message reach your target audience.

The lessons in this book will help you turn your data into high impact visual narrative that your audience will remember. Remove ineffective graphs from your life, one exploding 3D pie chart at a time. There is a narrative in your data—Storytelling with Data will teach you how to tell it.

Author: Cole Nussbaumer Knaflic

Link to buy: https://www.amazon.com/Storytelling-Data-Visualization-Business-Professionals/dp/1119002257/

Ratings: 4.6 out of 5 stars (from 3129 reviews)

Best Sellers Rank: #3,991 in Books

#1 in Business Marketing

#1 in Information Management (Books)

#1 in Business Mathematics

Joel Grus works at the Allen Institute for Artificial Intelligence as a research engineer. He previously worked at Google as a software engineer and at various startups as a data scientist. He lives in Seattle, where he routinely attends data science happy hours.

To truly grasp data science, you must not only master the tools (data science libraries, frameworks, modules, and toolkits), but also the ideas and principles that underpin them. This second edition of Data Science from Scratch, one of the best books on data science, updated for Python 3.6, demonstrates how these tools and techniques function by implementing them from scratch.

If you have a mathematical aptitude and some programming abilities, author Joel Grus will help you become acquainted with the arithmetic and statistics at the heart of data science, as well as the hacking skills required to get started as a data scientist. This updated book shows you how to locate the diamonds in today's jumbled overflow of data, with new material on deep learning, statistics, and natural language processing.

• Take a Python crash course.
• Learn the fundamentals of linear algebra, statistics, and probability, as well as how and when they are applied in data science.
• Data collection, exploration, cleaning, munging, and manipulation
• Explore the principles of machine learning.
• Models such as k-nearest neighbors, Nave Bayes, linear and logistic regression, decision trees, neural networks, and clustering should be implemented.
• Investigate recommender systems, natural language processing, network analysis, MapReduce, and database technologies.
  

Author: Joel Grus

Link to buy: https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130

Ratings: 4.4 out of 5 stars (from 568 reviews)

Best Sellers Rank: #32,766 in Books

#5 in Computer Programming Structured Design

#5 in Enterprise Data Computing

#6 in Computer Algorithms

Jiawei Han is a computer science professor at the University of Illinois at Urbana-Champaign. He is well-known for his work in data mining and database systems, and he has received numerous honors for his contributions to the field, including the 2004 ACM SIGKDD Innovations Award.

Micheline Kamber is a researcher who enjoys writing in simple language. She graduated from Concordia University in Canada with a master's degree in computer science (with a focus on artificial intelligence).

Jian Pei is a Canada Research Chair (Tier 1) in Big Data Science and a Professor in Simon Fraser University's School of Computing Science. He is also an associate member of the Statistics and Actuarial Science Department. He is a well-known leading researcher in data science, big data, data mining, and database systems in general.

The growing volume of data in modern business and science necessitates the development of more complicated and sophisticated instruments. Although developments in data mining technology have made large amounts of data collection considerably easier, the technology is still growing, and there is an ongoing need for innovative techniques and tools to help us translate this data into meaningful information and knowledge.

Great strides have been made in the field of data mining since the last edition's publication. The third edition of Data Mining: Concepts and Techniques not only continues the tradition of providing you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, but it also focuses on new and important topics in the field, including data warehouses and data cube technology, mining streams, mining social networks, and mining spatial, multimedia, and other complex data. Each chapter is a stand-alone guide to a vital topic, presenting tried-and-true methods and solid implementations that can be used directly or with strategic adjustment against live data. If you want to apply today's most sophisticated data mining techniques to real-world business difficulties, this is the material you need.

• Hundreds of methods and implementation examples are presented in pseudo-code and are suited for use in real-world, large-scale data mining applications.
• Mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in various industries are among the advanced subjects covered.
• Gives you a complete, hands-on look at the principles and strategies you'll need to make the most of your data.

Author: Jiawei Han, Micheline Kamber and Jian Pei

Link to buy: https://www.amazon.com/Data-Mining-Concepts-Techniques-Management-dp-0123814790/dp/0123814790

Ratings: 4.3 out of 5 stars (from 235 reviews)

Best Sellers Rank: #432,460 in Books

#115 in Management Information Systems

#178 in Artificial Intelligence (Books)

#249 in Data Mining (Books)