This slide deck gives an introduction on data science focusing on three most common tasks including regression, classification and clustering. Each task comes with a real world data science project to illustrate the concepts. This presentation was initially created for a one-hour guest lecture at Utah State University for teaching and education purposes.

1. Data Science in Industry
Applying Machine Learning to
Real-world Challenges

2. About me - Yuchen Zhao
principal data scientist at

3. obtained Ph.D. in
data mining and machine learning

4. worked in both academia and industry

6. Not just a researcher,
but a coder & hacker

7. What is data science?

8. data is everywhere...

9. data science helps
extract knowledge from data...

10. Data scientists investigate complex
data problems

11. find and interpret rich data sources

12. Visualize the data

13. get insights from data

14. from insights….

15. Questions?

18. Now is the fun part...

19. Data Science techniques!

20. Data science 101
● regression
● classification
● clustering
● ranking (not covered in this lecture)
● recommendation (not covered in this lecture)

21. Regression

22. What is regression?

23. A bit formal definition….
models a functional relationship
between
an input variable x
and
a response variable y

24. x
y

27. find the equation

28. What else can regression do?

29. Predicting who may change jobs!

31. x
y
Recap - regression

32. classification

33. identify to which of a set of categories
a new data point belongs

34. Spam or Not spam?

35. Credit approve or not?

36. Optical character recognition

37. Document classification

38. SVM

39. Decision tree

40. Use classification to...
find who you are
in social networks

42. classification

43. classification

44. Missing data
Outdated data
Non-standard data

45. Why we want to classify?

46. Understanding users’ social roles
is crucial to many
social network applications

47. including
advertising targeting,
marketing,
personalization,
recommendation, etc.

48. Finding out who you really are...

50. manually labeling is
time-consuming
and
error prone

51. Human learning
Machine learning

53. SVM

54. Decision tree

55. How accurate can we get?

57. Can we further improve?

58. Clustering

59. grouping a set of data points

60. data points in the same group ( cluster)
are more similar to each other
than to those
in other groups (clusters)

62. k-means clustering algorithm

63. k clusters

64. k = 3

65. step 1:
randomly select k points
as centroids

66. 3 random centroids

67. step 2:
assign every data point to
the nearest centroid

69. step 3:
calculate mean of each cluster
as the new centroid

71. repeat
assign clusters based on
the new centroids

73. How to use clustering to
solve big data problem?

74. Machine data is massive

76. 1 Tb/day is normal

77. no one has time to read all data...

78. Clustering comes to rescue!

80. clustering algorithm summarizes
big data to a few groups

81. each group represents
a number of similar data points

82. investigating data points
one by one

83. just investigating the clusters!

84. Things to consider
in practice...

85. scalability

86. velocity

87. variety

88. real-time

89. What’s next?

90. Recap
● regression
● classification
● clustering

91. This presentation was initially created for a guest lecture at Utah State University for teaching and education purposes.
Thanks!