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Databases and Data Mining��Course contents & “rules of the game”

(INTRODUCTORY) LECTURE

UP FAMNIT

2019/20

dr. Jamolbek Mattiev

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About the course

  • Course name:�Databases and Data Mining
  • Teacher: Jamolbek Mattiev
  • Course type: elective
  • Students:
    • Students of the study program Computer Linguistics (Master)�
    • Prerequisites: no prior knowledge needed�useful: basic statistics, basic programming (in JAVA)

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About 🡪 Theory 🡪 Practice 🡪 Rules of the game 🡪 Final grade 🡪 Literature & sources

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Course contents – theory

  • Knowledge discovery (searching for patterns) in data using the CRISP-DM methodology:
    • Problem understanding
    • Data understanding
    • Data preparation
    • Modeling – pattern discovery
    • Evaluation
    • Deployment

Source: https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

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About 🡪 Theory 🡪 Practice 🡪 Rules of the game 🡪 Final grade 🡪 Literature & sources

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Course contents – practice sessions

  • Using data mining techniques on real data:
    • “manually”
    • with aid of (computer) tools:
      • Advanced text editors (gedit, Notepad++, …)
      • Spreadsheet editors (Calc, Excel, …)
      • Open-source data mining toolboxes (WEKA, R, Tableau, …)

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About 🡪 Theory 🡪 Practice 🡪 Rules of the game 🡪 Final grade 🡪 Literature & sources

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Rules of the game

  • The final grade is “composed” of:
    • The written exam grade – or 2 mid-terms (80%)
      • Condition: written exam has to be “positive” (≥ 55/100 points),� mid-terms have to be both “positive”;
    • The oral exam grade (20%):
      • Condition: a “satisfactory” answer to each of the 3 questions;

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About 🡪 Theory 🡪 Practice 🡪 Rules of the game 🡪 Final grade 🡪 Literature & sources

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Literature and other sources

  1. Ian H. Witten,‎ Eibe Frank,‎ Mark A. Hall,‎ and Christopher J. Pal. Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition, Morgan Kaufmann, 2016.
  2. Mohammed J. Zaki, Wagner Meira, Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2014.
  3. David J. Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining, MIT Press, Fall 2000.
  4. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Verlag, 2001.
  5. Tom Mitchell: Machine Learning, McGraw Hill, 1997.
  6. UCI ML Repository: http://archive.ics.uci.edu/ml/
  7. Kaggle: https://www.kaggle.com/
  8. WEKA software: http://www.cs.waikato.ac.nz/ml/weka/

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About 🡪 Theory 🡪 Practice 🡪 Rules of the game 🡪 Final grade 🡪 Literature & sources