European University Institute Library

Introduction to data mining and analytics with machine learning in R and Python, Kris Jamsa

eng

illustrations

index present

non fiction

Introduction to data mining and analytics with machine learning in R and Python

1128104662

Kris Jamsa

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 how-to and is an excellent resource for anyone considering the field. Case studies and hands-on activities incorporate real-world 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 industry-standard 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 in-depth treatments, supplemented with hands-on exercises. Operations covered in earlier chapters are given real-world 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 analyst’s steps from planning through execution, ensuring that readers gain the technical know-how to launch, lead, or support a data project in the workplace. --, Provided by publisher

Data mining and analytics -- Machine learning -- Databases and data warehouses -- Data visualization -- Keep Excel in your toolset -- Keep SQL in your toolset -- NoSQL data analytics -- Programming data mining and analytic solutions -- Data preprocessing and cleansing -- Data clustering -- Classification -- Predictive anlytics -- Data association -- Mining text and images -- Big data mining -- Planning and launching a data-mining and data-anaytics project