Data Mining and Business Analytics with R

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For 5 years, and until last week, the videos were only available to past and current students. However, the word spread and many colleagues, instructors, and students have asked me for access. Currently the videos cater only to those who understand English.

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Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. For additional information, see the Global Shipping Program terms and conditions - opens in a new window or tab No additional import charges on delivery Delivery: Estimated between Tue. Watch list is full. They are different from the many excellent machine learning videos and MOOCs in focus and in technical level — a basic statistics course that covers linear regression and some business experience should be sufficient for understanding the videos. Want to do business with KPMG?

I opened the option for community-contributed captions, in the hope that folks will contribute captions in different languages to help make the knowledge propagate further. They are all homemade — I tried to filter out barking noises and to time the recording when ceremonies were not held close to our home.

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Seller Inventory SKU Data Mining and Business Analytics with R. Johannes Ledolter. Publisher: Wiley , This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis About this title Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. From the Back Cover : Showcases R's critical role in the world of business Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible robust computational and analytical tools.

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New Quantity Available: 3. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

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Standard Linear Regression. Importance of Parsimony in Statistical Modeling. Logistic Regression. Classification Using a Nearest Neighbor Analysis.

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Multinomial Logistic Regression. More on Classification and a Discussion on Discriminant Analysis.

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Decision Trees. Network Data. Appendix A: Exercises.

Appendix B: References. A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools.

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