Data Analytics Made Accessible
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| Author | : Jay Cobb |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023-03-13 |
| Genre | : Computers |
| ISBN | : 9781806308606 |
This is constantly evolving, and the updated book continues to fill the need for a concise and conversational book on the hot and growing field of Data Science. Easy to read and informative, this lucid and constantly updated book covers everything important with concrete examples and invites the reader to join this field. The 2023 revised edition includes Data Lakes, Recommendation Engines, Sample Data Mining Projects, and more. The chapters in the book are organized for a typical one-semester course. The book contains case lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area and a solid toolset of the major data mining techniques and platforms. The 2022 edition has a summary chapter that encapsulates the entire book in just main 50 points in a few pages. Finally, it includes a tutorial for R and a tutorial for Python. It contains expanded primers on Big Data, Artificial Intelligence, Data Science careers, and Data Ownership and Privacy. The 2022 edition is updated with a relationship to Artificial Intelligence in many ways. It includes topics such as Data Lakes and Data sharing practices. This constantly evolving book has proved very popular throughout the world. Dozens of universities worldwide have adopted it as a textbook for their courses. Students across various academic disciplines, including business, computer science, statistics, engineering, and others, are attracted to discovering new insights and ideas from data and can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others, can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again as a reference book for insights and techniques.
| Author | : Anil Maheshwari |
| Publisher | : Business Expert Press |
| Total Pages | : 226 |
| Release | : 2014-12-31 |
| Genre | : Business & Economics |
| ISBN | : 1631571214 |
“This book is a splendid and valuable addition to this subject. The whole book is well written and I have no hesitation to recommend that this can be adapted as a textbook for graduate courses in Business Intelligence and Data Mining.” Dr. Edi Shivaji, Des Moines, Iowa “As a complete novice to this area just starting out on a MBA course I found the book incredibly useful and very easy to follow and understand. The concepts are clearly explained and make it an easy task to gain an understanding of the subject matter.” -- Mr. Craig Domoney, South Africa. Business Intelligence and Data Mining is a conversational and informative book in the exploding area of Business Analytics. Using this book, one can easily gain the intuition about the area, along with a solid toolset of major data mining techniques and platforms. This book can thus be gainfully used as a textbook for a college course. It is also short and accessible enough for a busy executive to become a quasi-expert in this area in a couple of hours. Every chapter begins with a case-let from the real world, and ends with a case study that runs across the chapters.
| Author | : Andrea De Mauro |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 407 |
| Release | : 2021-08-30 |
| Genre | : Business & Economics |
| ISBN | : 1801074585 |
Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards Key FeaturesEnhance your presentation skills by implementing engaging data storytelling and visualization techniquesLearn the basics of machine learning and easily apply machine learning models to your dataImprove productivity by automating your data processesBook Description Data Analytics Made Easy is an accessible beginner's guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don't worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You'll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You'll not only be able to understand data scientists' machine learning models; you'll be able to challenge them and build your own. Creating interactive dashboards – Follow the book's simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results. What you will learnUnderstand the potential of data and its impact on your businessImport, clean, transform, combine data feeds, and automate your processesInfluence business decisions by learning to create engaging presentationsBuild real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and moreCreate professional-looking and business-centric visuals and dashboardsOpen the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning modelsWho this book is for This book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required.
| Author | : Andrea de Mauro |
| Publisher | : |
| Total Pages | : 406 |
| Release | : 2021-08-30 |
| Genre | : |
| ISBN | : 9781801074155 |
Make informed decisions using data analytics, machine learning, and data visualizations Key Features: Take raw data and transform it to add value to your organization Learn the art of telling stories with your data to engage with your audience Apply machine learning algorithms to your data with a few clicks of a button Book Description: Data analytics has become a necessity in modern business, and skills such as data visualization, machine learning, and digital storytelling are now essential in every field. If you want to make sense of your data and add value with informed decisions, this is the book for you. Data Analytics Made Easy is an accessible guide to help you start analyzing data and quickly apply these skills to your work. It focuses on how to generate insights from your data at the click of a few buttons, using the popular tools KNIME and Microsoft Power BI. The book introduces the concepts of data analytics and shows you how to get your data ready and apply ML algorithms. Implement a full predictive analytics solution with KNIME and assess its level of accuracy. Create impressive visualizations with Microsoft Power BI and learn the greatest secret in successful analytics - how to tell a story with your data. You'll connect the dots on the various stages of the data-to-insights process and gain an overview of alternative tools, including Tableau and H20 Driverless AI. By the end of this book, you will have learned how to implement machine learning algorithms and sell the results to your customers without writing a line of code. What You Will Learn: Understand the potential of data and its impact on any business Influence business decisions with effective data storytelling when delivering insights Use KNIME to import, clean, transform, combine data feeds, and automate recurring workflows Learn the basics of machine learning and AutoML to add value to your organization Build, test, and validate simple supervised and unsupervised machine learning models with KNIME Use Power BI and Tableau to build professional-looking and business-centric visuals and dashboards Who this book is for: Whether you are working with data experts or want to find insights in your business' data, you'll find this book an effective way to add analytics to your skill stack. No previous math, statistics, or computer science knowledge is required.
| Author | : Gábor Békés |
| Publisher | : Cambridge University Press |
| Total Pages | : 741 |
| Release | : 2021-05-06 |
| Genre | : Business & Economics |
| ISBN | : 1108483011 |
A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.
| Author | : Christian Tominski |
| Publisher | : CRC Press |
| Total Pages | : 313 |
| Release | : 2020-04-01 |
| Genre | : Computers |
| ISBN | : 1351648748 |
In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk For more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.
| Author | : Ken Steif |
| Publisher | : CRC Press |
| Total Pages | : 229 |
| Release | : 2021-08-18 |
| Genre | : Business & Economics |
| ISBN | : 100040157X |
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.
| Author | : Hadley Wickham |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 521 |
| Release | : 2016-12-12 |
| Genre | : Computers |
| ISBN | : 1491910364 |
Learn how to use R to turn raw data into insight, knowledge, and understanding. 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. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. 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
| Author | : John W. Foreman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 432 |
| Release | : 2013-10-31 |
| Genre | : Business & Economics |
| ISBN | : 1118839862 |
Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
| Author | : Annalyn Ng |
| Publisher | : Annalyn Ng & Kenneth Soo |
| Total Pages | : |
| Release | : 2017-03-24 |
| Genre | : |
| ISBN | : 9789811110689 |
Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.
