Data Mining for Business and Governance

The file contains cheatsheet materials for two M.Sc. DSS core courses, Data Mining for Business & Gov. (880022-M-6) and Machine Learning (880083-M-6). Both cheatsheets have been tested on multiple mock exams as well as used successfully in the actual exams. Includes python codes for Machine Learning. Some information overlaps due to it being covered in both courses.

Maximize your chances of acing the exam with this meticulously crafted cheat sheet. Designed specifically for exam success, it distills complex data mining concepts into easy-to-understand formulas and techniques, including the XAI lecture. Tailored to fit exam guidelines, this sheet is your allowed companion during the test, ensuring you have all the vital information at your fingertips. Whether you're grappling with decision trees, entropy calculations, or Naïve Bayes, this cheat sheet is you...

English summary of the Data Mining fro Business and Governance course from Master in Data Science and Society. Summary of lecture materials, readings, and notes.

All the needed information

Detailed summary of all lectures and additional notes, explanations and examples for the course "Data Mining for Business and Governance" at Tilburg University which is part of the Master Data Science and Society. Course was given by Ç. Güven, G.R. Nápoles during the second semester, block three of the academic year 2021 / 2022 (January to March 2022).

Cheat Sheet with all necessary information to pass the exam of "Data Mining for Business and Governance (-M-6)" at Tilburg University. The course is part of the Master Data Science and Society. It was taught in the academic year 2021/2022 in semester 2, block 3 (January to March 2022) by the lecturers Ç. Güven and G.R. Nápoles. The margins on the page are printer friendly and work well for the printers on Tilburg University campus.

This documents contains a summary of the final modules/weeks (4-7) for the course Data Mining for Business and Governance. The following topics are included in this summary: ⋅ Crisp (K-means) clustering ⋅ Fuzzy (c-means) clustering ⋅ Hierarchical clustering ⋅ Text mining ⋅ Preprocessing noisy text ⋅ Document similarity: Jaccard coefficient ⋅ Term frequency, inverse term frequency ⋅ Dimensionality reduction ⋅ Feature selection ⋅ Filtering strategy ⋅ Wrapper s...