Sztuka czytania między wierszami - R i Data mining

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Slajdy stanowią ramy warsztatu z R i data miningu (poziom podstawowy).
Materiały przykładowe z komentarzami w języku polskim: https://gist.github.com/kmrowca/public

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  • Ćwiczenie na kartkach polegające na szukaniu zależności. Narysować na tablicy, podać przykład ze spłacalnością kredytów
  • Przykład z kodem pocztowym i numerem telefonu
  • Sztuka czytania między wierszami - R i Data mining

    1. 1. Sztuka czytania między wierszami czyli język R i Data Mining w akcji
    2. 2. <me> Katarzyna Mrowca </me>
    3. 3. The deal 
    4. 4. Agenda • Quick glance on theory - Data mining • Exercises on… paper • Quick glance on tool – R console • Exercises – became friend with R •…
    5. 5. Agenda • Quick glance on theory - Data mining • Exercises on… paper • Quick glance on tool – R console • Exercises – became friend with R •… Theory Exercise
    6. 6. Agenda • Quick glance on theory - Data preparation • Exercises • Decision trees • Cluser analysis • Text mining •… Theory Exercise
    7. 7. Agile is everywhere!
    8. 8. Agile is everywhere! • Retro after second break
    9. 9. Quick glance on theory!
    10. 10. What data mining is?
    11. 11. What „google” says?
    12. 12. What „google” says? Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), [1] an interdisciplinary subfield of computer science,
    13. 13. What „google” says? Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics.
    14. 14. What „google” says? Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics.
    15. 15. What „google” says? Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics.
    16. 16. What „google” says? Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics.
    17. 17. What „google” says? Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics.
    18. 18. What „google” says? The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
    19. 19. What „google” says? The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
    20. 20. What „google” says? The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
    21. 21. What „google” says? Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Source: wikipedia
    22. 22. Data mining – what is „inside” • Predictive • Regression • Classification • Collaborative Filtering • Descriptive • Clustering / similarity matching • Association rules and variants • Deviation detection
    23. 23. Data mining – what is „inside” • Predictive: • Regression • Classification • Collaborative Filtering • Descriptive: • Clustering / similarity matching • Association rules and variants • Deviation detection
    24. 24. Data mining – what is „inside” • Predictive: • Regression • Classification • Collaborative Filtering • Descriptive: • Clustering / similarity matching • Association rules and variants • Deviation detection
    25. 25. What data mining is not?
    26. 26. Why Data Mining is so popular?
    27. 27. What is a difference between statistics and data mining?
    28. 28. Exercise
    29. 29. Data preparation
    30. 30. Variables
    31. 31. Qualitative & Quantitative
    32. 32. Tame R console!
    33. 33. Take a break 
    34. 34. Regression
    35. 35. Time series
    36. 36. Decision trees
    37. 37. Regression trees
    38. 38. Classification trees
    39. 39. K means
    40. 40. Text mining
    41. 41. Thank you!

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