J. Gamper, Free University of Bolzano, DWDM 2012/13 Data Warehousing and Data Mining – Introduction – Acknowledgements: I am indebted to Michael Böhlen and Stefano Rizzi for providing me their slides, upon which these lecture notes are based.

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Students will be able to actively manage and participate in data mining projects executed by consultants or specialists in data mining. A useful takeaway from the course will be the ability to perform powerful data analysis in Excel. Lecture Notes. Lecture notes and homework assignments will be available at the class website in SloanSpace.

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The whole book and lecture slides are free and downloadable in PDF format. Lecture notes of data mining course by Cosma Shalizi at CMU R code examples are provided in some lecture notes, and also in solutions to home works.

Exploring Data: The data exploration chapter has been removed from the print edition of the book, but is available on the web. Appendices: All appendices are available on the web. A new appendix provides a brief discussion of scalability in the context of big data.

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is, although we might learn a model from training data, we ultimately care about how well it works on future test data. When a model fits training data well, but performs poorly on test data, we say that the model has overfit the training data; i.e., the model has fit properties

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Don't show me this again. Welcome! This is one of over 2,200 courses on OCW. Find materials for this course in the pages linked along the left. MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum.. No enrollment or registration.

Video Lectures Weka Data Mining Book Blog Twitter Welcome to the free online course Data Mining with Weka This 5 week MOOC will introduce data mining concepts through practical experience with the free Weka tool. The course features: • video lectures by Professor Ian H. Witten (available on YouTube and YouKu)

Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining by Tan, Steinbach, Kumar ... –Seminal book is Exploratory Data Analysis by Tukey –A nice online introduction can be found in Chapter 1 of the NIST Engineering Statistics Handbook

CS 412: Introduction to Data Mining Course Syllabus Course Description This course is an introductory course on data mining. It introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions: (1) pattern discovery and (2) cluster analysis.

Free data mining books. An Introduction to Statistical Learning: with Applications in R Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language. Modeling With Data This book focus some processes to solve analytical problems applied to data.

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.

Evaluation. Beyond Apriori (ppt, pdf) Chapter 6 from the book "Introduction to Data Mining" by Tan, Steinbach, Kumar. Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman. Lecture 5: Similarity and Distance. Metrics.

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