- Companion Website for “Data Mining and Analysis: Fundamental Concepts and Algorithms” - Nov 19, 2014.
Supplementary materials for the textbook Data Mining and Analysis: Fundamental Concepts and Algorithms are now available online and include figures, slides, datasets, videos, and more. Download them today.
- Book: Data Mining for Managers - Oct 14, 2014.
This book by a leading data mining consultant is meant for both practitioners and end users of data mining solutions, and it focuses more on the data and less on the math.
- Book: Modern Optimization with R - Oct 10, 2014.
Learn the most relevant concepts related to modern optimization methods and how to apply them using multi-platform, open source, R tools in this new book on metaheuristics.
- Predictive Analytics Using Oracle Data Miner - Sep 24, 2014.
Learn about basic data mining concepts and how to apply data mining techniques to your data using Oracle SQL and Data Miner tools in this new book.
- Book: Data Classification: Algorithms and Applications - Aug 2, 2014.
Learn a wide variety of data classification techniques and their methods, domains, and variations in this comprehensive survey of the area of data classification.
- Book: Probabilistic Approaches to Recommendations - Jul 28, 2014.
Learn about the challenges of the recommendation problem and common probabilistic solutions to it, then dive into state of the art techniques in Probabilistic Approaches to Recommendation.
- Book: Win With Advanced Business Analytics - Jun 26, 2014.
Written for the non-technical professional, this definitive guide shows you how to gain the most opportunity and value from every type of advanced business analytics.
- Book: Data Classification: Algorithms and Applications - Jun 14, 2014.
This new book explores the underlying algorithms of classification and applications in text, multimedia, social network, biological data, and other domains. 25% off with KDnuggets discount.
- Book: Data Mining and Analysis: Fundamental Concepts and Algorithms - May 27, 2014.
This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. Companion website has data, slides and other teaching material.
- Outlier Detection for Temporal Data - May 22, 2014.
Outlier Detection for Temporal Data covers topics in temporal outlier detection, which have applications in numerous fields. It starts with the basic topics then moves on to state of the art techniques in the field.
- New Book: Analytics in a Big Data World – The Essential Guide to Data Science - May 13, 2014.
For organizations looking to enhance their capabilities via data analytics, this book is the go-to reference for applying Data Science to make the right business decisions.
- Did Target Really Predict a Teen’s Pregnancy? The Inside Story - May 7, 2014.
We examine the origin and the facts behind this explosive story, the importance of headlines, and how unsubstantiated assumptions gain traction and mainstream attention and help create myths around Predictive Analytics.
- New Book: Social Media Mining – free PDF download - Apr 22, 2014.
Social Media Mining integrates social media, social network analysis, and data mining to enable students, practitioners, researchers, and managers to understand the basics and potentials of this field.
- Vincent Granville Data Science Book - Apr 12, 2014.
The Data Science book from Analytic Bridge founder Vincent Granville shows you what employers want and the skill set that separates the quality data scientist from other IT professionals.
- Book Review: Data Just Right - Apr 7, 2014.
An introduction to technology and software at play in the current quest to define the Big Data Analytics computing paradigm, the book Data Just Right is reviewed in detail here.
- Book: Visual Analytics of Movement - Apr 2, 2014.
This new book is about the exciting possibilities created by visual analytics for anyone interested in understanding movement, analyzing movement, or simply make decisions influenced by the way people, animals, and objects move.
- Top KDnuggets tweets, Mar 28-30: SAS vs R vs Python, ecosystem comparison; Practical Data Science with R - Mar 31, 2014.
SAS vs. R vs. Python - Which should you learn?; New Book: Practical Data Science with R ; Is Data Scientist the right career path for you? Candid advice; Must read books for people interested in Analytics.
- Top stories for Mar 23-29 - Mar 30, 2014.
Data Scientists Salary Poll: US, Canada, Australia lead; Is Data Scientist the right career path for you? New Book: Practical Data Science with R; Test your numbersense.
- New Book: Practical Data Science with R - Mar 29, 2014.
This new book will help you learn and apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
- Top KDnuggets tweets, Mar 14-16: Is Apache Spark the Next Big Thing? R Meta-Book – best CRAN posts assembled - Mar 17, 2014.
Apache Spark promises to be the Next Big Thing in #Big Data - 100x faster than #Hadoop; An R Meta-Book - best CRAN posts assembled; The Beauty of pi - the fastest (and most incomprehensible) formula; Tips for Hiring Data Scientists: look for quants with business hustle.
- New book: Big Data, Mining, and Analytics: Components of Strategic Decision Making - Mar 15, 2014.
This book ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data.
- LIONbook: Machine Learning + Intelligent Optimization – completed, free personal download - Mar 11, 2014.
This book combines two usually separated topics: machine learning and intelligent optimization, and does it with enough technical details to satisfy professionals, but also with concrete examples, vivid images, and fun. Buy a low-cost paperback or ebook (Kindle), or download a free PDF.
- Book: Ask, Measure, Learn – Social Media Analytics for Customer Behavior - Mar 3, 2014.
Ask-Measure-Learn is a rare book intended both for a manager and for a data scientist. It presents a framework that helps you ask the right questions, measure the right data, and then learn from the results.