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Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation. With a dual focus on concepts and operations, this text comprises a complete howto and is an excellent resource for anyone considering the field. Case studies and handson activities incorporate realworld data sets and allow students the opportunity to exercise their new skills. Our Cloud Desktop integrates popular data mining tools, giving students a valuable familiarity with industrystandard applications. After defining the concepts of data mining and machine learning, Data Mining and Analytics delves into the types of databases, their respective relevance and popularity, and the trends that affect their use. The importance of data visualization for communication purposes is explored, as are the processes of data cleansing, clustering, and classification. Excel, SQL, NoSQL, Python, and R programming all receive indepth treatments, supplemented with handson exercises. Operations covered in earlier chapters are given realworld context through a practical application to the current issues of big data and of text and image data mining. The text concludes by describing an analysts steps from planning through execution, ensuring that readers gain the technical knowhow to launch, lead, or support a data project in the workplace.
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