Download or read book Beginner s Guide to Correlation Analysis written by Lee Baker and published by Lee Baker. This book was released on with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your correlation results are probably wrong. Sorry, but they are. You see, there is one really important thing to know about your correlations that mean that whatever results you get you can’t be sure they are correct. This book fixes that. Correlation is a way of describing how a pair of variables change together as a result of their connection. In other words, if one of your variables changes, the other is likely to change too, and correlations measure by how much. Correlation analysis is one of the most used – and misunderstood – statistical techniques. Most correlation results are wrong, and for one very good reason. In this book we’re going to understand just why this is, and learn how to fix it. Beginner’s Guide to Correlation Analysis explains how to look at correlations with a focus on understanding the data, how to work with it, choose the right ways to analyse it, select the correct statistical tools and how to interpret the results in a way that is easy to understand. Best of all, there is no technical or statistical jargon – it is written in plain English. It is packed with visually intuitive examples and makes no assumptions about your previous experience with statistics or correlations – in short, it is perfect for beginners! Discover the world of correlation analysis. Get this book, TODAY!

Download or read book Beginner s Guide for Data Analysis using R Programming written by Jeeva Jose and published by KHANNA PUBLISHING HOUSE. This book was released on with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: R programming is an efficient tool for statistical analysis of data. Data science has become critical to each field and the popularity of R is skyrocketing. Organization as large and diverse as Google, Facebook, Microsoft, Bank of America, Ford Motor Company, Mozilla, Thomas Cook, The New York Times, The National Weather Service, Twitter, ANZ Bank, Uber, Airbnb etc . have turned to R for reporting, analyzing and visualization of data, this book is for students and professionals of Mathematics, Statistics, Physics, Chemistry, Biology, Social Science and Medicine, Business, Engineering, Software, Information Technology, Sales, Bio Informatics, Pharmacy and any one, where data needs to be analyzed and represented graphically.

Download or read book Correlation Is Not Causation written by Lee Baker and published by Lee Baker. This book was released on with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Correlation Is Not Causation. You know it and I know it, and yet we are constantly having to be reminded of it because we can’t seem to help but get it wrong. How many times have you heard someone really smart say something like ‘wow, this correlation has a p-value of 0.000001 so A must be causing B…’? It’s not our fault though – we’re only human. We seek explanation for patterns and events that happen around us, and if something defies logic, we try to find a reason why it might make sense. If something doesn’t add up, we make it up. OK, so if correlation does not necessarily imply causation, there must be a reason for that, and there must be something that is causing what we observe. That is what this book is all about. If we discover a correlation between a pair of variables there are five alternatives to one being the direct cause of the other, and we’ll unmask all five in this book. Then, once we understand each of these alternatives, we’ll formulate a plan to discover whether we have a direct causal link or whether there is some other explanation. Correlation Is Not Causation explains how to systematically test for the five most common correlation-causation pitfalls that even the pros fall into (occasionally). We’ll learn to create strategies to analyse the data and interpret the results in a way that is easy to understand. Best of all, there is no technical or statistical jargon – it is written in plain English. It is packed with visually intuitive examples and makes no assumptions about your previous experience with correlations – in short, it is perfect for beginners! Discover the world of correlation and causation. Get this book, TODAY!

Download or read book A Beginner s Guide to Structural Equation Modeling written by Randall E. Schumacker and published by Psychology Press. This book was released on 2004-06-24 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition features: a CD with all of the book's Amos, EQS, and LISREL programs and data sets; new chapters on importing data issues related to data editing and on how to report research; an updated introduction to matrix notation and programs that illustrate how to compute these calculations; many more computer program examples and chapter exercises; and increased coverage of factors that affect correlation, the 4-step approach to SEM and hypothesis testing, significance, power, and sample size issues. The new edition's expanded use of applications make this book ideal for advanced students and researchers in psychology, education, business, health care, political science, sociology, and biology. A basic understanding of correlation is assumed and an understanding of the matrices used in SEM models is encouraged.

Download or read book Introductory R A Beginner s Guide to Data Visualisation Statistical Analysis and Programming in R written by Robert J. Knell and published by Robert Knell. This book was released on 2014-05-14 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include: A walkthrough of the basics of R's command line interface Data structures including vectors, matrices and data frames R functions and how to use them Expanding your analysis and plotting capacities with add-in R packages A set of simple rules to follow to make sure you import your data properly An introduction to the script editor and advice on workflow A detailed introduction to drawing publication-standard graphs in R How to understand the help files and how to deal with some of the most common errors that you might encounter. Basic descriptive statistics The theory behind statistical testing and how to interpret the output of statistical tests Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models What the assumptions behind the analyses mean and how to test them using diagnostic plots Explanations of the summary tables produced for statistical analyses such as regression and ANOVA Writing your own functions in R Using table operations to manipulate matrices and data frames Using conditional statements and loops in R programmes. Writing longer R programmes. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Download or read book Unlocking the Power of Data A Beginner s Guide to Data Analysis written by Balasubramanian Thiagarajan and published by Otolaryngology online. This book was released on 2023-06-13 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the world of data analysis! In today's data-driven era, the ability to effectively analyze and derive insights from data has become a vital skill for individuals and organizations across various domains. This book aims to serve as your comprehensive guide to understanding and performing data analysis, from the fundamental concepts to the practical applications. Chapter 1 introduces you to the fascinating realm of data analysis. We delve into the importance of data analysis in decision-making processes and highlight its role in gaining valuable insights and making informed choices. Understanding the power of data analysis sets the foundation for your journey ahead. Chapter 2 focuses on data entry, a crucial step in the data analysis process. We explore different methods and techniques for entering data accurately, ensuring the reliability and integrity of your dataset. Effective data entry practices are essential for obtaining meaningful results. In Chapter 3, we explore the different types of data analysis. Whether it's exploratory, descriptive, diagnostic, predictive, or prescriptive analysis, you will gain an understanding of each type and when to employ them in various scenarios. This chapter equips you with the knowledge to choose the appropriate analysis technique for your specific needs. To lay the groundwork for your data analysis journey, Chapter 4 familiarizes you with the basic terminology commonly used in the field. From variables and observations to measures of central tendency and variability, this chapter ensures you have a solid grasp of the foundational concepts necessary for effective data analysis. Chapter 5 focuses on setting up your data analysis environment. We guide you through the process of installing the necessary software and configuring your data workspace. Creating an optimal environment is crucial for seamless and efficient data analysis. Data preprocessing takes center stage in Chapter 6. We delve into the essential steps of data cleaning, transformation, and handling missing values. By mastering these techniques, you will be able to prepare your data for analysis, ensuring its quality and usability. In Chapter 7, we explore the exciting world of data exploration and visualization. Understanding the distribution of data and identifying relationships between variables are key aspects of uncovering meaningful insights. We delve into creating various charts and graphs to visually represent data, aiding in its interpretation and analysis. Chapter 8 introduces you to statistical analysis techniques. Descriptive statistics help us summarize and describe data, while inferential statistics enable us to make inferences and draw conclusions about populations based on sample data. Additionally, hypothesis testing allows us to validate our assumptions and test specific predictions. Predictive analytics takes the spotlight in Chapter 9. We explore techniques such as linear and logistic regression, decision trees, and clustering algorithms. These techniques empower you to make predictions and forecasts based on historical data, providing valuable insights for decision-making. Chapter 10 is dedicated to machine learning, an exciting field within data analysis. We introduce the fundamentals of machine learning, including supervised and unsupervised learning algorithms. Understanding these concepts opens doors to more advanced data analysis techniques and applications. Ethics in data analysis takes center stage in Chapter 11. We delve into the critical considerations of privacy concerns, data bias, and fairness in data analysis. Ethical data practices are crucial to ensure the responsible and ethical use of data in analysis. Chapter 12 explores the wide-ranging applications of data analysis. We delve into the domains of business analytics, healthcare analytics, sports analytics, and social media analytics, highlighting how data analysis drives insights and informs decision-making in these fields. Finally, Chapter 13 serves as a conclusion and sets you on the path for further learning and development. We recap the key concepts covered in the book, provide tips for advancing your data analysis skills, and discuss future trends and innovations in the field. We hope this book serves as a valuable resource in your data analysis journey. Whether you are a student, professional, or data enthusiast, we believe that understanding and applying data analysis.

Download or read book Machine Learning and Its Application A Quick Guide for Beginners written by Indranath Chatterjee and published by Bentham Science Publishers. This book was released on 2021-12-22 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include the types of machine learning algorithms and the statistical knowledge required for devising relevant computer algorithms. The book also covers advanced topics such as deep learning and feature engineering. Key features: - 8 organized chapters on core concepts of machine learning for learners - Accessible text for beginners unfamiliar with complex mathematical concepts - Introductory topics are included, including supervised learning, unsupervised learning, reinforcement learning and predictive statistics - Advanced topics such as deep learning and feature engineering provide additional information - Introduces readers to python programming with examples of code for understanding and practice - Includes a summary of the text and a dedicated section for references Machine Learning and Its Application: A Quick Guide for Beginners is an essential book for students and learners who want to understand the basics of machine learning and equip themselves with the knowledge to write algorithms for intelligent data processing applications.

Download or read book A Beginner s Guide to Structural Equation Modeling written by Randall E. Schumacker and published by Routledge. This book was released on 2012-10-12 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: This best-seller introduces readers to structural equation modeling (SEM) so they can conduct their own analysis and critique related research. Noted for its accessible, applied approach, chapters cover basic concepts and practices and computer input/output from the free student version of Lisrel 8.8 in the examples. Each chapter features an outline, key concepts, a summary, numerous examples from a variety of disciplines, tables, and figures, including path diagrams, to assist with conceptual understanding. The book first reviews the basics of SEM, data entry/editing, and correlation. Next the authors highlight the basic steps of SEM: model specification, identification, estimation, testing, and modification, followed by issues related to model fit and power and sample size. Chapters 6 through 10 follow the steps of modeling using regression, path, confirmatory factor, and structural equation models. Next readers find a chapter on reporting SEM research including a checklist to guide decision-making, followed by one on model validation. Chapters 13 through 16 provide examples of various SEM model applications. The book concludes with the matrix approach to SEM using examples from previous chapters. Highlights of the new edition include: A website with raw data sets for the book's examples and exercises so they can be used with any SEM program, all of the book's exercises, hotlinks to related websites, and answers to all of the exercises for Instructor’s only New troubleshooting tips on how to address the most frequently encountered problems Examples now reference the free student version of Lisrel 8.8 Expanded coverage of advanced models with more on multiple-group, multi-level, & mixture modeling (Chs. 13 & 15), second-order and dynamic factor models (Ch. 14), and Monte Carlo methods (Ch. 16) Increased coverage of sample size and power (Ch. 5) and reporting research (Ch. 11) New journal article references help readers better understand published research (Chs. 13 – 17) and 25 % new exercises with answers to half in the book for student review. Designed for introductory graduate level courses in structural equation modeling or factor analysis taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. An understanding of correlation is assumed. To access the website visit the book page or the Textbook Resource page at http://www.psypress.com/textbook-resources/ for more details.

Download or read book Statistics for Psychology written by Roger Watt and published by SAGE. This book was released on 2019-03-30 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise, easy-to-understand and highly visual book helps students to understand the principles behind the many statistical practices. This text helps students to build a mental map to enable them to work their way through tests and procedures with a better level of understanding (and ultimately feel more confident and get better grades). Statistical analysis will also be covered in the book in the same simple-to-follow way, without messy details or complicated formulae. However, this approach does not lead to simple understanding. Instead it allows students to really grasp how to use, and be creative with, statistics. Key features: A principles-based approach, helping students to apply and adapt their skills to a variety of situation Test out principles in practice on the companion website with statistics scenarios Carefully designed graphics to explain statistical principles Links to relevant sources / further reading for statistical packages, so the book can be used as a portal to/ springboard for further study. Developed in conjunction with students means this book answers the key challenges students face. Based on a BPS commended programme Supported by a wealth of online resources at www.sagepub.co.uk/statisticsforpsychology

Download or read book Cause and Correlation in Biology written by Bill Shipley and published by Cambridge University Press. This book was released on 2002-08 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.

Download or read book A Beginners Guide to Scanning Electron Microscopy written by Anwar Ul-Hamid and published by Springer. This book was released on 2018-10-26 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book was developed with the goal of providing an easily understood text for those users of the scanning electron microscope (SEM) who have little or no background in the area. The SEM is routinely used to study the surface structure and chemistry of a wide range of biological and synthetic materials at the micrometer to nanometer scale. Ease-of-use, typically facile sample preparation, and straightforward image interpretation, combined with high resolution, high depth of field, and the ability to undertake microchemical and crystallographic analysis, has made scanning electron microscopy one of the most powerful and versatile techniques for characterization today. Indeed, the SEM is a vital tool for the characterization of nanostructured materials and the development of nanotechnology. However, its wide use by professionals with diverse technical backgrounds—including life science, materials science, engineering, forensics, mineralogy, etc., and in various sectors of government, industry, and academia—emphasizes the need for an introductory text providing the basics of effective SEM imaging.A Beginners’ Guide to Scanning Electron Microscopy explains instrumentation, operation, image interpretation and sample preparation in a wide ranging yet succinct and practical text, treating the essential theory of specimen-beam interaction and image formation in a manner that can be effortlessly comprehended by the novice SEM user. This book provides a concise and accessible introduction to the essentials of SEM includes a large number of illustrations specifically chosen to aid readers' understanding of key concepts highlights recent advances in instrumentation, imaging and sample preparation techniques offers examples drawn from a variety of applications that appeal to professionals from diverse backgrounds.

Download or read book A Beginner s Guide to Using Open Access Data written by Saif Aldeen Saleh AlRyalat and published by CRC Press. This book was released on 2019-02-12 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Open Access Data is emerging as a source for cutting edge scholarship. This concise book provides guidance from generating a research idea to publishing results. Both young researchers and well-established scholars can use this book to upgrade their skills with respect to emerging data sources, analysis, and even post-publishing promotion. At the end of each chapter, a tutorial simulates a real example, allowing readers to apply what they learned about accessing open data, and analyzing this data to reach the results. This book can be of use by established researchers analyzing data, publishing, and actively promoting ongoing and research. Key selling features: Describes the steps, from A-Z, for doing open data research Includes interactive tutorials following each chapter Provides guidelines for readers so that they can use their own accessed open data Reviews recent software and websites promoting and enabling open data research Supplements websites which update recent open data sources

Download or read book A Beginner s Guide to Structural Equation Modeling written by Tiffany A. Whittaker and published by Routledge. This book was released on 2022-04-27 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Beginner’s Guide to Structural Equation Modeling, fifth edition, has been redesigned with consideration of a true beginner in structural equation modeling (SEM) in mind. The book covers introductory through intermediate topics in SEM in more detail than in any previous edition. All of the chapters that introduce models in SEM have been expanded to include easy-to-follow, step-by-step guidelines that readers can use when conducting their own SEM analyses. These chapters also include examples of tables to include in results sections that readers may use as templates when writing up the findings from their SEM analyses. The models that are illustrated in the text will allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models, up to testing higher order models as well as multiple group modeling techniques. Updated information about methodological research in relevant areas will help students and researchers be more informed readers of SEM research. The checklist of SEM considerations when conducting and reporting SEM analyses is a collective set of requirements that will help improve the rigor of SEM analyses. This book is intended for true beginners in SEM and is designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and healthcare sciences. This book also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.

Download or read book Technical Analysis A precise guide for beginners written by The Financial Edits and published by by Mocktime Publication. This book was released on 2023-04-02 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technical Analysis - A precise guide for beginners Chapter 1. Introduction to Technical Analysis: Origins, Evolution, and Key Concepts Chapter 2. Charting Techniques: Understanding Price and Volume Data Chapter 3. Candlestick Patterns: Decoding Market Psychology Chapter 4. Trend Analysis: Identifying and Following Market Trends Chapter 5. Support and Resistance: The Backbone of Technical Analysis Chapter 6. Moving Averages: Smoothing Price Data for Clarity Chapter 7. Momentum Indicators: Gauging Market Strength and Direction Chapter 8. Oscillators: Detecting Overbought and Oversold Conditions Chapter 9. Volume Analysis: Interpreting Trading Activity and Liquidity Chapter 10. Price Patterns: Classic Chart Formations and Breakouts Chapter 11. Fibonacci Tools: Unraveling Market Structure with Mathematics Chapter 12. Elliott Wave Theory: Understanding Market Cycles and Wave Patterns Chapter 13. Ichimoku Cloud: Visualizing Market Equilibrium and Trends Chapter 14. Market Profile: Analyzing Price Distribution and Value Areas Chapter 15. Point and Figure Charting: Focusing on Price Action and Reversals Chapter 16. Harmonic Trading: Identifying High-Probability Patterns Chapter 17. Relative Strength Index (RSI): Measuring Market Momentum and Divergences Chapter 18. Bollinger Bands: Analyzing Volatility and Market Dynamics Chapter 19. Trading Strategies: Developing and Implementing Effective Techniques Chapter 20. Risk Management: Protecting Capital and Minimizing Losses Chapter 21. Technical Analysis in Different Asset Classes: Stocks, Forex, and Cryptocurrencies Chapter 22. Algorithmic Trading and Quantitative Analysis: The Intersection with Technical Analysis Chapter 23. Combining Fundamental and Technical Analysis: A Synergistic Approach

Download or read book Forex for Beginners written by Adam Kritzer and published by Apress. This book was released on 2013-02-03 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Backed by a comprehensive list of studies, this book is a brilliant contribution on the connections between exchange rates and economics."—Francesc Riverola, CEO and Founder of FXstreet.com "Adam Kritzer has been covering the forex market for years as a prominent but accessible industry expert. In a market sector full of pitfalls for the novice, this book will help many new traders avoid costly mistakes and get started on the path to success."—Andy Hagans, Co-founder of ETF Database "Adam Kritzer is not only one of my favorite forex writers but also one of the best ... This book will likely become required reading for those getting into the forex market."—Zachary Storella, Founder of CountingPips.com Forex for Beginners: A Comprehensive Guide to Profiting from the Global Currency Markets is a guide for those who want to earn extra income trading currencies without committing large amounts of time or money. This book will introduce global investors to the basics of forex (foreign exchange) trading and provide them with a solid framework for analyzing currencies and profiting from their fluctuations. Topics covered include the forces that cause exchange rates to fluctuate, an overview of the mechanics of trading, analytical and forecasting tools, how to profit from pricing trends, and common pitfalls that often ensnare traders. While most books make grandiose promises of instant success and large profits, Forex for Beginners represents an alternative approach to investing in forex. The forex market is dominated by institutional capital and algorithmic trading, making it unrealistic to think that day traders can beat the market by relying on charts and technical indicators alone. Thus, the emphasis here is on fundamental analysis—using economic concepts to spot currency misalignments—and staking out positions to profit from them over a period of weeks and months. If you’re eager to tap into the world’s largest financial market on a part-time basis, this is the book for you. You will gain an understanding of how currency markets work and use this knowledge to generate income.

Download or read book The Beginner s Guide to Data Science written by Robert Ball and published by Springer Nature. This book was released on 2022-11-15 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered. Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification. This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics: Data gathering and acquisition techniques including data creation Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded Metrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observations Recommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some manner Machine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analytics Natural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate manner Time series analysis, techniques to examine and generate forecasts about the progress and evolution of data over time Data science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book.