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Data Analytics Made Accessible
This book fills the need for a concise and accessible book on the topic of Business Intelligence and Data Mining. It is a conversational book that feels easy and informative. This short and lucid book covers everything important, with concrete examples, and invites the reader to join this field. 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 a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms.
Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others are attracted to the idea of discovering new insights and ideas from data. This book can also be gainfully used by executives, managers, analysts, professors, doctors, accountants, and other professionals to learn how to make sense of the data coming their way. This is a lucid flowing book that one can finish in one sitting, or can return to it again and again for insights and techniques.
Table of Contents
Chapter 1: Wholeness of Business Intelligence and Data Mining
Chapter 2: Business Intelligence Concepts & Applications
Chapter 3: Data Warehousing
Chapter 4: Data Mining
Chapter 5: Decision Trees
Chapter 6: Regression Models
Chapter 7: Artificial Neural Networks
Chapter 8: Cluster Analysis
Chapter 9: Association Rule Mining
Chapter 10: Text Mining
Chapter 11: Web Mining
Chapter 12: Big Data
Chapter 13: Data Modeling Primer
Appendix: Data Mining Tutorial using Weka
Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others are attracted to the idea of discovering new insights and ideas from data. This book can also be gainfully used by executives, managers, analysts, professors, doctors, accountants, and other professionals to learn how to make sense of the data coming their way. This is a lucid flowing book that one can finish in one sitting, or can return to it again and again for insights and techniques.
Table of Contents
Chapter 1: Wholeness of Business Intelligence and Data Mining
Chapter 2: Business Intelligence Concepts & Applications
Chapter 3: Data Warehousing
Chapter 4: Data Mining
Chapter 5: Decision Trees
Chapter 6: Regression Models
Chapter 7: Artificial Neural Networks
Chapter 8: Cluster Analysis
Chapter 9: Association Rule Mining
Chapter 10: Text Mining
Chapter 11: Web Mining
Chapter 12: Big Data
Chapter 13: Data Modeling Primer
Appendix: Data Mining Tutorial using Weka
454 pages, Kindle Edition
First published May 1, 2014
1422 people are currently reading
964 people want to read
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Displaying 1 - 28 of 28 reviews
February 8, 2016
A good introduction to using data for business intelligence. It was a little basic but did cover a lot of topics and gave a good overview of the different techniques used within data mining. There is an excellent set of tutorials in the appendix that help you get started doing your very first predictions.
July 23, 2018
Read as a textbook for an information security course. For a textbook, it's well written, but parts were, understandably, pretty dull. I am glad they started each chapter with a "caselet" for context. They also explained everything very well, but I did not miss my last statistics course. They also include a tutorial for R, which I should re-learn one of these days.
My biggest critique of this book has to do with its format. For whatever reason, it is only available on Amazon as an ebook. I never noticed that my Surface doesn't have the Amazon Kindle app, but now I know why. So, long post short, I was stuck reading this ebook on my smartphone. Not ideal. Print still has its advantages.
My biggest critique of this book has to do with its format. For whatever reason, it is only available on Amazon as an ebook. I never noticed that my Surface doesn't have the Amazon Kindle app, but now I know why. So, long post short, I was stuck reading this ebook on my smartphone. Not ideal. Print still has its advantages.
December 23, 2016
Data Analytics Made Accessible gives a pretty comprehensive whirlwind tour of all the major topics and considerations one might expect on the topic of Data Analysis. It feels quite complete and for that I’m more than happy to declare that I got good value for money from this book and will probably refer back to it again in the future. However, there are a couple of issues I have with how the book is written that cause me to knock its rating from a potential four to a possibly undeserved three stars.
To begin, the book sets out to make the topic accessible. I would argue that it does achieve this but uses a very formulaic structure that often feels like a checklist being completed 15-times over until completion. While I realise this shouldn’t necessarily be faulted, it does end up falling into the realm of being quite stale and the writing itself lacks any kind of injection of personality, for example through interesting anecdotes or curious counter-intuitive facts that have come about through the increased use of data for business and life in general. When all’s said and done, the book feels like it could have been written by a pretty smart A.I. referencing the most relevant generic sources of content for its research.
The reason for me landing on a poorly three-star rating is that despite the clinical tone, the book is heavily peppered with typos and grammatical errors throughout, which perhaps could be overlooked but equally cannot be due to their regularity and gravity. It could be my perception but they seemed to increase towards the end of the book, suggesting the author was in a hurry to be done instead of taking the time to make sure the finished product would be something he could be proud of. For me, on this occasion, the errors must be acknowledged and reflected in the final rating.
Having cleared the air with those potentially minor gripes, I must go back to the fact that this book really is a good starting point for anyone with a passing curiosity of what Data Analytics and Big Data really means. Maheshwari clearly has a deep understanding of each disparate aspect and can communicate that without resorting to jargon. I particularly like the handful of thought-provoking questions at the end of each chapter, designed to make the reader contemplate just how each topic applies in the real world.
A worthwhile read, just a pity about the lack of care and attention that went into the writing. Three Stars.
To begin, the book sets out to make the topic accessible. I would argue that it does achieve this but uses a very formulaic structure that often feels like a checklist being completed 15-times over until completion. While I realise this shouldn’t necessarily be faulted, it does end up falling into the realm of being quite stale and the writing itself lacks any kind of injection of personality, for example through interesting anecdotes or curious counter-intuitive facts that have come about through the increased use of data for business and life in general. When all’s said and done, the book feels like it could have been written by a pretty smart A.I. referencing the most relevant generic sources of content for its research.
The reason for me landing on a poorly three-star rating is that despite the clinical tone, the book is heavily peppered with typos and grammatical errors throughout, which perhaps could be overlooked but equally cannot be due to their regularity and gravity. It could be my perception but they seemed to increase towards the end of the book, suggesting the author was in a hurry to be done instead of taking the time to make sure the finished product would be something he could be proud of. For me, on this occasion, the errors must be acknowledged and reflected in the final rating.
Having cleared the air with those potentially minor gripes, I must go back to the fact that this book really is a good starting point for anyone with a passing curiosity of what Data Analytics and Big Data really means. Maheshwari clearly has a deep understanding of each disparate aspect and can communicate that without resorting to jargon. I particularly like the handful of thought-provoking questions at the end of each chapter, designed to make the reader contemplate just how each topic applies in the real world.
A worthwhile read, just a pity about the lack of care and attention that went into the writing. Three Stars.
August 2, 2023
This book is excellent. Every chapter goes straight to the point. All terms are clearly defined, each chapter has a little real world case study, a concise summary and some homeworks. At the end there is even a full case study analysed with R and with Python.
This is targeted at someone who knows nothing about Data Analytics and wants to get a quick overview over the entire field and I think it fully achieves that goal.
This is targeted at someone who knows nothing about Data Analytics and wants to get a quick overview over the entire field and I think it fully achieves that goal.
December 31, 2018
Basic. On a scale of 1-10 with 1 being an introductory book this is about a 2.
February 15, 2018
Data Analytics made accessible is written for the Data science newbie who is looking for an introductory book that is neither too technical nor too shallow but one that is engaging enough to cover most analytics topics.The book contains caselets on real world stories to introduce each chapter and ends with challenging exercises meant to reinforce the readers understanding of the discussed topics.
The book covers most of the topics on what data science entails today. Divided into three sections, Anil welcomes the reader by discussing topics related to business intelligence.The important concepts of Data warehousing, data mining and data visualization are all discussed in an accessible way.The book then progresses to discuss some of the common machine learning algorithms in the second section before winding with a brief on text mining, web mining,data modeling and big data.
Each chapter is introduced with a real world example and concluded with practice exercises. These make the concepts discussed more practical and give the reader the urge for further research.There are also lots of illustrations and discussed step by step walkthroughs on various algorithms.
Anil Maheshwari is an experienced business intelligence and data mining lecturer.He wrote the book from his class notes and made it in such a way that it covers everything important without being intimidating to the uninitiated. His chief aim is to invite the readers to join the field of data science.
I did not like how brief most of the chapters turned to out to be. Though this is meant to be an introductory book, the author is too brief in some of the chapters and thus the concepts a left floating on the readers head.This is more prevalent in chapters towards the end of the book.
This is the best introduction to data science book available. The author uses simple language to discuss a fairly complex topic and covers a wide spectrum of data science concepts.I would highly recommend the book to those very new to the field. This is not recommended for anyone with a fair amount experience.
The book covers most of the topics on what data science entails today. Divided into three sections, Anil welcomes the reader by discussing topics related to business intelligence.The important concepts of Data warehousing, data mining and data visualization are all discussed in an accessible way.The book then progresses to discuss some of the common machine learning algorithms in the second section before winding with a brief on text mining, web mining,data modeling and big data.
Each chapter is introduced with a real world example and concluded with practice exercises. These make the concepts discussed more practical and give the reader the urge for further research.There are also lots of illustrations and discussed step by step walkthroughs on various algorithms.
Anil Maheshwari is an experienced business intelligence and data mining lecturer.He wrote the book from his class notes and made it in such a way that it covers everything important without being intimidating to the uninitiated. His chief aim is to invite the readers to join the field of data science.
I did not like how brief most of the chapters turned to out to be. Though this is meant to be an introductory book, the author is too brief in some of the chapters and thus the concepts a left floating on the readers head.This is more prevalent in chapters towards the end of the book.
This is the best introduction to data science book available. The author uses simple language to discuss a fairly complex topic and covers a wide spectrum of data science concepts.I would highly recommend the book to those very new to the field. This is not recommended for anyone with a fair amount experience.
August 11, 2021
Fairly ok as a basic place to start on the topic. The author doesn't stick to one audience though - sometimes it is overly accessible, sometimes meant for people with very good understanding of the topics. A lot of errors, which good editing could have dealt with. It was particularly annoying when the text in his graphics was cut off by poor formatting.
The book definitely did introduce me well to many topics, but I was very glad to be done with it.
The book definitely did introduce me well to many topics, but I was very glad to be done with it.
March 19, 2018
Good Overview
Good high level overview on a large number of topics. Doesn't get too detailed but uses good examples to demonstrate concepts. Good for a basic understanding of the breadth of data analytics.
Good high level overview on a large number of topics. Doesn't get too detailed but uses good examples to demonstrate concepts. Good for a basic understanding of the breadth of data analytics.
August 31, 2018
Good for beginners
This is a good book for people who are just learning about data analytics and all its components. Each chapter was one aspect of data analytics and very informative with examples to illustrate the points.
This is a good book for people who are just learning about data analytics and all its components. Each chapter was one aspect of data analytics and very informative with examples to illustrate the points.
November 11, 2018
I found this book very informative. It filled in some of the gaps I had in understanding data science. I particularly liked that it explained some of the statistics involved. This was a good read and everything was understandable. I feel ready for my Data class.
February 24, 2019
Excelente libro para los que nos gusta la analítica de datos
Bien distribuido el libro. Super completo. Me gusto ya que te ayuda a conocer cuáles son las técnicas que se puede aplicar a la información y extraer conocimiento
Bien distribuido el libro. Super completo. Me gusto ya que te ayuda a conocer cuáles son las técnicas que se puede aplicar a la información y extraer conocimiento
June 1, 2019
Excellent and comprehensive introduction!
Great introductory book, easy to read and well explained. It covers all main topics, but providing recommendations on further reading.
Great introductory book, easy to read and well explained. It covers all main topics, but providing recommendations on further reading.
July 19, 2019
Review
This is a great book for those wanting to study data science. But as for me it went too deep
This is a great book for those wanting to study data science. But as for me it went too deep
March 25, 2020
Pleasant surprise!
Nice tour through analytics space. Not too deep, just what I needed. Well written with clear examples. Loved the exercises and thought provoking questions.
Nice tour through analytics space. Not too deep, just what I needed. Well written with clear examples. Loved the exercises and thought provoking questions.
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March 21, 2021Easy access to the data analytics world, but understanding complex mechanism by clear and cristal explanation
December 30, 2022
This field is difficult and interesting. Can I do this?
This entire review has been hidden because of spoilers.
May 16, 2023
Explains Data Analytics in very understandable way. Great examples throughout the book.
July 18, 2021
Favorite chapter was that on Decision Trees.
Required text for a Business Intelligence course at school. The book provides the foundations of several Data Mining techniques. Favorite chapter was that on Decision Trees.
Required text for a Business Intelligence course at school. The book provides the foundations of several Data Mining techniques. Favorite chapter was that on Decision Trees.
October 25, 2019
The book provides a high-level perspective of Business Intelligence, Data Mining, and Big Data.
Good for management and non-technical people.
Good for management and non-technical people.
April 24, 2020
I give this book 3.5 stars. It's a good reference book on data science and data analysis.
July 10, 2017
Employees on the business-side of the company and starters seem to get the most out of this book. It discloses various topics of Analytics / Data Science. The case-lets that illustrate the concepts are current and I could relate to some of them myself. The questions provided at the end of each chapter help you revise and solidify the techniques described in the chapter.
Decision Tree and Regression are very detailed and I thoroughly enjoyed learning those algorithms.
Simple language is required for a complex subject like Data Science and that's precisely what the author presents in this book.
Decision Tree and Regression are very detailed and I thoroughly enjoyed learning those algorithms.
Simple language is required for a complex subject like Data Science and that's precisely what the author presents in this book.
October 2, 2022
The book summarized various topics regarding the data analytics profession in a succinct and simple way that anyone can catch up. Of course it contained some technical materials such as statistics and probability models or database structure but one can follow up more easily by following examples and actual business practices mentioned every section. The author also provides insights into the career prospect of the sector as well as some good advices to anyone aspires to follow this profession. As data is growing more and more important in this era, a good data analysis skill set can be beneficial to anyone working in any domain.
January 1, 2020
Filled with mini case studies, pictographs, charts and review questions. This book aims to make data analytic concepts more accessible - but to whom? In reality this is a college textbook- not fun reading and not accessible to the basic person. But if you are majoring in Business, Science, Math or some other technical subject I'm sure it is quite fascinating. This book was not up my alley although I do want to know more about data analysis. I found the discussion on the future of artificial intelligence as it relates to its impact on the workforce to be interesting and concerning.
September 2, 2015
Great and concise book
Not just the book was great, the questions, examples and exercises were amazing. I truly recommend this book to anyone is planning to embark in an data analytics endeavor.
Not just the book was great, the questions, examples and exercises were amazing. I truly recommend this book to anyone is planning to embark in an data analytics endeavor.
February 29, 2016
This is a sort of everything you want to know about Business Intelligence and Data Mining. Lots of bullet form, and checklists of knowledge you need to remember.
Relatively easy read despite the subject matter.
Relatively easy read despite the subject matter.
June 25, 2016
Amazing book
The author of the book knows this subject very deeply, easy to understand , well explained. I recommend this book to everyone who wants to understand big data theoretically and practically.
The author of the book knows this subject very deeply, easy to understand , well explained. I recommend this book to everyone who wants to understand big data theoretically and practically.
January 5, 2018
General overview of Data Analytics. As a general overview or introductory book very good. if you expect something more in-depth you should probably look elsewhere
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May 9, 2019Como introducción al análisis de datos es bastante ameno, pero se queda corto en algunas técnicas.
Displaying 1 - 28 of 28 reviews