Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods Summaries at the start of each chapter that supply an outline of key topicsThe book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material.

BOT TO THE FUTURE: Mastercard wants to make commerce'more conversational' Mastercard has unveiled plans to launch artificial intelligence (AI) bots for its merchant and bank partners, allowing consumers to use chat, messaging and natural language interfaces to shop and manage their finances. Mastercard KAI, the payments giant's bot for banks, will allow consumers to ask the bot questions about their accounts, review purchase history, monitor spending levels and receive contextual offers. Meanwhile, the Mastercard Bot for Merchants will allow consumer to shop and transact on messaging platforms and then check out with the Masterpass global digital payment service. According to research firm Gartner, nearly US$2bn in online sales will be performed exclusively through mobile digital assistants by the end of 2016. Kiki Del Valle, SVP at Mastercard, explained to NFC World the company was aiming to make commerce "more conversational by combining secure digital payments and artificial intelligence technology".

Add Dell to the list of retailers teasing customers with Black Friday deals early. The computer company on Tuesday followed e-tailer giant Amazon with the release of its Black Friday deals, offering an indication of tech discounts expected for the big post-Thanksgiving shopping day, especially on 4K TVs and video game systems. Among Dell's notable deals are a 43-inch 4K TV from LG for $299.99 that comes with an additional $30 Dell gift card. There's also a 55-inch Samsung 4K TV for $599.99 that comes with a $150 Dell gift card. The Samsung TV will be the featured "doorbuster" -- or headline -- deal available on November 24 at 8PM ET at Dell.com, while the LG will be available a few hours later at midnight ET.

Move over desktop: Mobile is the future of online purchases and commerce, thanks to its ability to connect with location-based and personalized data. Forrester's new report "Predictions 2017: Mobile is the Face of Digital," scheduled to be publicly released Tuesday, explains that mobile has become the new pathway for consumers find brands -- and it is moving past the traditional social media app. Advertisers will increasingly use mobile to connect next year using chatbots, other artificial intelligence-enabled platforms like Apple's Siri or Amazon's Alexa and messaging apps, the report said. The company previously said that people spend more than two hours a day on mobile, and that by 2019 the majority of our billion websites will be on mobile. "The magic of mobile is the immediacy," said Julie Ask, principal analyst at Forrester and co-author of the report.

R/GA Ventures, with partner Westfield Labs, has concluded its Connected Commerce Accelerator program with a demo event in San Francisco. The accelerator is a three-month, immersive, mentor driven program designed for startups developing connected hardware products and software services with the goal of helping them to build businesses and brands that can scale. The program taps into the emerging class of products that combine hardware, data, and digital services in compelling ways for consumers and businesses. Following 12 weeks of mentorship, pilots, and ongoing work with brand, technology, and business consultants from the R/GA Services team, Westfield Labs, and program partners, the ten participating companies presented their commerce and retail-focused businesses to investors and business partners. The participants represent the next wave of innovation in the commerce and retail space, ranging from innovative takes on fulfillment, delivery, and returns to retail workforce optimization and training solutions, adaptive messaging powered by machine learning, a new platform for the connected store, and companies using artificial intelligence (AI) for customer service, bot management, and image recognition.

Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals – across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.

With Amazon Machine Learning (Amazon ML), you can build and train predictive models and host your applications in a scalable cloud solution. In this project, you will use the visualization tools and wizards of Amazon ML to guide you through the process of creating a new machine learning (ML) model without having to learn complex ML algorithms and technology. To complete this project, you will download freely-available sample customer data and upload the data to an Amazon S3 bucket to create a datasource. You will then create an ML model from this datasource, from which you can then evaluate and adjust the ML model's performance, and then use it to generate predictions.

Holden Karau is transgender Canadian, and an active open source contributor. When not in San Francisco working as a software development engineer at IBM's Spark Technology Center, Holden talks internationally on Spark and holds office hours at coffee shops at home and abroad. She makes frequent contributions to Spark, specializing in PySpark and Machine Learning. Prior to IBM she worked on a variety of distributed, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelor of Mathematics in Computer Science.

Dr. John R. Talburt is Professor of Information Science at the University of Arkansas at Little Rock (UALR) where he is the Coordinator for the Information Quality Graduate Program and the Executive Director of the UALR Center for Advanced Research in Entity Resolution and Information Quality (ERIQ). He is also the Chief Scientist for Black Oak Partners, LLC, an information quality solutions company. Prior to his appointment at UALR he was the leader for research and development and product innovation at Acxiom Corporation, a global leader in information management and customer data integration. Professor Talburt holds several patents related to customer data integration and the author of numerous articles on information quality and entity resolution, and is the author of Entity Resolution and Information Quality (Morgan Kaufmann, 2011). He also holds the IAIDQ Information Quality Certified Professional (IQCP) credential.

And why is it useful for BigData? 29 Why are statistical distributions important? What is a training set, a validation set, a test set and a gold set in supervised and unsupervised learning? What is a cross-validation and what is an overfitting? Can you provide an example for Map and Reduce in Spark? What is a loss function, what are linear models, and what do we mean by regularization parameters in machine learning?