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

1. Sztuka czytania między
wierszami
czyli język R i Data Mining w akcji

2. <me>
Katarzyna Mrowca
</me>

4. The deal 

5. Agenda
• Quick glance on theory - Data mining
• Exercises on… paper
• Quick glance on tool – R console
• Exercises – became friend with R
•…

6. Agenda
• Quick glance on theory - Data mining
• Exercises on… paper
• Quick glance on tool – R console
• Exercises – became friend with R
•…
Theory
Exercise

7. Agenda
• Quick glance on theory - Data preparation
• Exercises
• Decision trees
• Cluser analysis
• Text mining
•…
Theory
Exercise

8. Agile is everywhere!

9. Agile is everywhere!
• Retro after second break

10. Quick glance on theory!

11. What data mining is?

12. What „google” says?

13. What „google” says?
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), [1] an interdisciplinary subfield of
computer science,

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. 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. 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. 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. 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.

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. 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. 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.

22. 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

23. Data mining – what is „inside”
• Predictive
• Regression
• Classification
• Collaborative Filtering
• Descriptive
• Clustering / similarity matching
• Association rules and variants
• Deviation detection

24. Data mining – what is „inside”
• Predictive:
• Regression
• Classification
• Collaborative Filtering
• Descriptive:
• Clustering / similarity matching
• Association rules and variants
• Deviation detection

25. Data mining – what is „inside”
• Predictive:
• Regression
• Classification
• Collaborative Filtering
• Descriptive:
• Clustering / similarity matching
• Association rules and variants
• Deviation detection

26. What data mining is not?

27. Why Data Mining is so
popular?

28. What is a difference between
statistics and data mining?

29. Exercise

30. Data preparation

31. Variables

32. Qualitative & Quantitative

33. Tame R console!

34. Take a break 

35. Regression

36. Time series

37. Decision trees

38. Regression trees

39. Classification trees

40. K means

41. Text mining

42. Thank you!