Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

by Jared Dean

With big data analytics comes big insights into profitabilityBig data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. "Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners" is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency.With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristicsDetails on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databasesComprehensive coverage of data mining, text analytics, and machine learning algorithmsA discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes"Big Data, Data Mining, and Machine Learning "provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

289 pages, ebook

First published January 1, 2014

Reviews

[Dominiek Leenknecht · Rating 2 out of 5]

Dit boek weet niet goed wat het wil. Enerzijds lijkt het erop gericht om een generalistisch overview te geven over Big Data Science, anderzijds verliest de auteur dit helikopterzicht meer dan eens zwaar uit het oog en verzandt hij in oeverloze details.
Een concreet voorbeeld: terwijl een onderwerp als "clusteren" bijzonder aanschouwelijk werd voorgesteld a.h.v. Rummikub en "textual analysis" op basis van een tv-programma als Jeopardy (Waagstuk), zijn er andere hoofdstukken die bijna puur uit wiskundige en statistische formules bestond en ik - hoe hard ik ook mijn best deed - er geen touw kon aan vastknopen.
Ook de case studies achter in dit boek zijn weinig bevredigend. Vaag gewauwel en een deel waarvan de auteur - waar je het net niet zou verwachten bij case studies - keihard in de theorie blijft plakken.

Ik ben net met de opleiding "Data Science" begonnen bij Coursera.org (on-line opleiding). Ik heb daar met de inleidingsles van een goed uur al meer inzicht verworven in het totaalplaatje van Big Data Science dan met het bovenstaande boek. En daarmee is alles wel gezegd.

[Willis · Rating 3 out of 5]

I know the author personally so I tried not to be too biased in reading the book. I've been interested in the topic and this book is a nice overview and introduction to data mining techniques. The last part of the book gives a number of examples of how data mining techniques can be applied in a variety of areas. These examples are given at a very high level so it is difficult to really determine how you might actually implement something like this for a real problem. I've found that there are a lot of books out there that tackle data mining and analytical techniques from a high level and I haven't found many that have enough detail to really be able to use them.