Part 1. Towards an understanding of big data: Are you ready? -- From data to big data: you must walk before you can run -- Big data: a revolution that changes the game -- Part 2. Big data analytics: a compilation of advanced analytics techniques that covers a wide range of data -- Building an understanding of big data analytics -- Why data analytics and when can we use it? -- Data analytics process: there's great work behind the scenes -- Part 3. Data analytics and machine learning: the relevance of algorithms -- Machine learning: a method of data analysis that automates analytical model building -- Supervised versus unsupervised algorithms: a guided tour -- Applications and examples.

Cover; Half-Title Page; Dedication; Title Page; Copyright Page; Contents; Acknowledgments; Preface; Introduction; Why this book?; Whom is this book for?; Organization of the book; Glossary; PART 1 Towards an Understanding of Big Data:Are You Ready?; 1. From Data to Big Data: You Must Walk Before You Can Run; 1.1. Introduction; 1.2. No analytics without data; 1.2.1. Databases; 1.2.2. Raw data; 1.2.3. Text; 1.2.4. Images, audios and videos; 1.2.5. The Internet of Things; 1.3. From bytes to yottabytes: the data revolution; 1.4. Big data: definition; 1.5. The 3Vs model.

1.6. Why now and what does it bring?1.7. Conclusions; 2. Big Data: A Revolution that Changes the Game; 2.1. Introduction; 2.2. Beyond the 3Vs; 2.3. From understanding data to knowledge; 2.4. Improving decision-making; 2.5. Things to take into account; 2.5.1. Data complexity; 2.5.2. Data quality: look out! Not all data are the right data; 2.5.3. What else? ... Data security; 2.6. Big data and businesses; 2.6.1. Opportunities; 2.6.2. Challenges; 2.7. Conclusions; PART 2 Big Data Analytics: A Compilation of Advanced Analytics Techniques that Covers a Wide Range of Data.

3. Building an Understanding of Big Data Analytics3.1. Introduction; 3.2. Before breaking down the process ... What is data analytics?; 3.3. Before and after big data analytics; 3.4. Traditional versus advanced analytics: What is the difference?; 3.5. Advanced analytics: new paradigm; 3.6. New statistical and computational paradigm within the big data context; 3.7. Conclusions; 4. Why Data Analytics and When Can We Use It?; 4.1. Introduction; 4.2. Understanding the changes in context; 4.3. When real time makes the difference; 4.4. What should data analytics address?

4.5. Analytics culture within companies4.6. Big data analytics application: examples; 4.7. Conclusions; 5. Data Analytics Process: There's Great Work Behind the Scenes; 5.1. Introduction; 5.2. More data, more questions for better answers; 5.2.1. We can never say it enough: "there is no good wind for those who don't know where they are going"; 5.2.2. Understanding the basics: identify what we already know and what we have yet to find out; 5.2.3. Defining the tasks to be accomplished; 5.2.4. Which technology to adopt?

5.2.5. Understanding data analytics is good but knowing how to use it is better! (What skills do you need?)5.2.6. What does the data project cost and how will it pay off in time?; 5.2.7. What will it mean to you once you find out?; 5.3. Next steps: do you have an idea about a "secret sauce"?; 5.3.1. First phase: find the data (data collection); 5.3.2. Second phase: construct the data (data preparation); 5.3.3. Third phase: go to exploration and modeling (data analysis); 5.3.4. Fourth phase: evaluate and interpret the results (evaluation and interpretation).