Data Analysis - Making Big Data Work

1. Data Analysis Making Big Data Work
David Chiu
2014/11/24

2. About Me
Founder of LargitData
Ex-Trend Micro Engineer
ywchiu.com

3. Big Data & Data Science

4. US Election Prediction
4

5. World Cup Prediction

6. Hurricane Prediction

7. Data Science
http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

9. Being A Data Scientist, You Need to Know That Much? Seriously?

10. Statistic
Single Variable、Multi Variable、ANOVA
Data Munging
Data Extraction, Transformation, Loading
Data Visualization
Figure, Business Intelligence
Required Skills

11. What You Probably Need Is A Team
Business Analyst Knowing how to use different tools under different circumstance
Statistician How to process big data?
DBA How to deal with unstructured data
Software Engineer Knowing how to user statistics

12. Four Dimension
12
Single Machine Memory R Local File
Cloud Distributed Hadoop HDFS
Statistics Analysis Linear Algebra
Architect Management Standard
Concept MapReduce Linear Algebra Logistic Regression
Tool Hadoop PostgreSQL R
Analyst How to use these tools
Hackers R Python Java

13. “80% are doing summing and averaging”
Content
1.Data Munging
2.Data Analysis
3.Interpret Result
What Data Scientists Do?

14. Application of Data Analysis
Text Mining
Classify Spam Mail
Build Index
Data Search Engine
Social Network Analysis
Finding Opinion Leader
Recommendation System
What user likes?
Opinion Mining
Positive/Negative Opinion
Fraud Analysis
Credit Card Fraud

15. Feed data to computer
Make Computer to Do Analysis

16. Let Computer Predict For You

17. Predictive Analysis
Learn from experience (Data), to predict future behavior
What to Predict？
e.g. Who is likely to click on that ad？
For What？
e.g. According to the click possibility and revenue to decide which ad to show.
Predictive Analysis

18. Customer buying beer will also buy pampers?
People are surfing telephone fee rate are likely to switch its vendor
People belong to same group are tend to have same telecom vendor
Surprising Conclusion

19. According to personal behavior, predictive model can use personal characteristic to generate a probabilistic score, which the higher the score, the more likely the behavior.
Predictive Model

20. Linear Model
e.g. Based on a cosmetic ad. We can give 90% weight to female customers, give10% to male customer. Based on the click probability (15%), we can calculate the possibility score (or probability)
Female 13.5%，Male1.5%
Rule Model
e.g.
If the user is “She”
And Income is over 30k
And haven’t seen the ad yet
The click rate is 11%
Simple Predictive Model

21. Induction
From detail to general
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E
-- Tom Mitchell (1998)
Discover an effective model
Start from a simple model
Update the model based on feeding data
Keep on improving prediction power
Machine Learning

22. Statistic Analysis
Regression Analysis
Clustering
Classification
Recommendation
Text Mining
Application
22

23. Image recognition

24. Decision Tree
Rate > 1,299/Month
Probability to switch vendor 15%
Probability to switch vendor 3%
Yes
No

25. Decision Tree
Rate > 1,299/Month
Probability to switch vendor 3%
Yes
No
Probability to switch vendor 10%
Probability to switch vendor 22%
Income>22,000
Yes
No

26. Decision Tree
Rate > 1,299/Month
Yes
No
Probability to switch vendor 10%
Probability to switch vendor 22%
Income>22,000
Yes
No
Probability to switch vendor 1%
Probability to switch vendor 7%
Free for intranet
Yes
No

27. Supervised Learning
Regression
Classification
Unsupervised Learning
Dimension Reduction
Clustering
Machine Learning

28. Supervised Learning

29. Classification
e.g. Stock prediction on bull/bear market
Regression
e.g. Price prediction
Supervised Learning

30. Dimension Reduction
e.g. Making a new index
Clustering
e.g. Customer Segmentation
Unsupervised Learning

31. Lift
The better the lift, the greater the cost?
The more decision rule, the more campaign?
Design strategy for different persona?
The lift for 4 campaign?
The lift for 20 ampaign?
Lift

32. Can we use the production rate of butter to predict stock market?
Overfitting

33. Use noise as information
Over assumption
Over Interpretation
What overfitting learn is not truth
Like memorize all answers in a single test.
Overfitting

34. Testing Model
Use external data or partial data as testing dataset

35. Traditional Analysis Tool

36. Statistics On The Fly
Built-in Math and Graphic Function
Free and Open Source
http://cran.r-project.org/src/base/
R Language
36

37. Functional Programming
Use Function Definition To Retrieve Answer
Interpreted Language
Statistics On the Fly
Object Oriented Language
S3 and S4 Method
R Language

38. Most Used Analytic Language
Most popular languages are R, Python (39%), SQL (37%). SAS (20%).
By Gregory Piatetsky, Aug 27, 2013.

39. Kaggle
http://www.kaggle.com/
Most often used language in Kaggle competition

40. Data Scientist in Google and Apple Use R
What is your programming language of choice, R, Python or something else?
“I use R, and occasionally matlab, for data analysis. There is a large, active and extremely knowledgeable R community at Google.”
http://simplystatistics.org/2013/02/15/interview-with-nick-chamandy-statistician-at-google/
“Expert knowledge of SAS (With Enterprise Guide/Miner) required and candidates with strong knowledge of R will be preferred”
http://www.kdnuggets.com/jobs/13/03-29-apple-sr-data- scientist.html?utm_source=twitterfeed&utm_medium=facebook&utm_campaign=tfb&utm_content=FaceBook&utm_term=analytics#.UVXibgXOpfc.facebook

41. Discover which customer is likely to churn?
Customer Churn Analysis

42. Account Information
state
account length.
area code
phone number
User Behavior
international plan
voice mail plan, number vmail messages
total day minutes, total day calls, total day charge
total eve minutes, total eve calls, total eve charge
total night minutes, total night calls, total night charge
total intl minutes, total intl calls, total intl charge
number customer service calls
Target
Churn (Yes/No)
Data Description

43. > install.packages("C50") > library(C50) > data(churn) > str(churnTrain) > churnTrain = churnTrain[,! names(churnTrain) %in% c("state", "area_code", "account_length") ] > set.seed(2) > ind <- sample(2, nrow(churnTrain), replace = TRUE, prob=c(0.7, 0.3)) > trainset = churnTrain[ind == 1,] > testset = churnTrain[ind == 2,]
Split data into training and testing dataset
70% as training dataset
30% as testing dataset

44. churn.rp <- rpart(churn ~ ., data=trainset) plot(churn.rp, margin= 0.1) text(churn.rp, all=TRUE, use.n = TRUE)
Build Classifier
Classfication

45. > predictions <- predict(churn.rp, testset, type="class") > table(testset$churn, predictions)
Prediction Result
pred
no
yes
no
859
18
yes
41
100

46. > confusionMatrix(table(predictions, testset$churn)) Confusion Matrix and Statistics predictions yes no yes 100 18 no 41 859 Accuracy : 0.942 95% CI : (0.9259, 0.9556) No Information Rate : 0.8615 P-Value [Acc > NIR] : < 2.2e-16 Kappa : 0.7393 Mcnemar's Test P-Value : 0.004181 Sensitivity : 0.70922 Specificity : 0.97948 Pos Pred Value : 0.84746 Neg Pred Value : 0.95444 Prevalence : 0.13851 Detection Rate : 0.09823 Detection Prevalence : 0.11591 Balanced Accuracy : 0.84435 'Positive' Class : yes
Use Confusion Matrix

47. Use Testing Data to Validate Result
predictions <- predict(churn.rp, testset, type="prob") pred.to.roc <- predictions[, 1] pred.rocr <- prediction(pred.to.roc, as.factor(testset[,(dim(testset)[[2]])])) perf.rocr <- performance(pred.rocr, measure = "auc", x.measure = "cutoff") perf.tpr.rocr <- performance(pred.rocr, "tpr","fpr") plot(perf.tpr.rocr, colorize=T,main=paste("AUC:",(perf.rocr@y.values)))

48. Finding Most Important Variable model=fit(churn~.,trainset,model="svm") VariableImportance=Importance(model,trainset,method="sensv") L=list(runs=1,sen=t(VariableImportance$imp),sresponses=VariableImportance$ sresponses) mgraph(L,graph="IMP",leg=names(trainset),col="gray",Grid=10)

49. Dynamic Language
Execution at runtime
Dynamic Type
Interpreted Language
See the result after execution
OOP
Python Language
49

50. Cross Platform(Python VM)
Third-Party Resource
(Data Analysis、Graphics、Website Development)
Simple, and easy to learn
Benefit of Python

51. Data Analysis
Scipy
Numpy
Scikit-learn
Pandas
51

52. Company that use python
52

53. Use InfoLite Tool To Extract DOM

54. Use Python To Build Up Dashboard

55. Monitor Social Media and News
Monitor post on social media
Configure keyword and alert
Use line plot to show daily post statistics
55
蘋果, nownews, udn, 中央跟風傳媒 還有 其他財經媒體

56. Daily Statistics Report
56

57. Examine Associate Article
57

58. Configure Alert and Keyword
58

59. Configure Monitor Channel
59

60. Track Specific Article
60

61. Have You Learned Big Data?
61

63. The 3Vs of Big Data

65. Product Centric
Customer Centric
Product Centric v.s. Customer Centric

66. Customer Centric?
http://goo.gl/iuy4lY

67. Personal Recommendation

68. Knowing Who You Are?
Personal recommendation
Customer relation management
Knowing What Futures Likes?
From the history, we can see the future
Predictive analysis
Knowing What is Hidden Beneath?
Correlation, Correlation, Correlation
So… What is Big Data?

69. So… How To Analyze?

70. Apache Project – From Yahoo
Feature
Extensible
Cost Effective
Flexible
High Fault Tolerant
Hadoop

71. Hadoop Eco System
HDFS
MR
IMPALA
HBASE
PIG
HIVE
SQOOP FLUME
HUE, Oozie, Mahout

72. Tools for different scale
Size
Classification
Tools
Lines
Sample Data
Analysis and Visualisation
Whiteboard,
Bash, ...
KBs – low MBs
Prototype Data
Analysis and Visualisation
Matlab, Octave, R, Processing, Bash, ...
MBs – low GBs
Online Data
Storage
MySQL (DBs), ...
Analysis
NumPy, SciPy, Pandas, Weka..
Visualisation
Flare, AmCharts, Raphael
GBs
– TBs
– PBs
Big Data
Storage
HDFS, Hbase, Cassandra,...
Analysis
Hive, Giraph, Hama, Mahout

73. Amazon

74. Facebook

75. Recommendation System
Javascript
Flume
HDFS
HBase
Pig
Mahout

76. Item- Based

77. User - Based

78. Monitor User Rating

79. Send User Behavior to Backend

80. Use Flume To Collect Streaming Data
From /tmp/postlog.txt To /user/cloudera/flume

81. JSON sample data
{"food":"Tacos", "person":"Alice", "amount":3}
{"food":"Tomato Soup", "person":"Sarah", "amount":2}
{"food":"Grilled Cheese", "person":"Alex", "amount":5}
Demo Code
second_table = LOAD 'second_table.json'
USING JsonLoader('food:chararray, person:chararray, amount:int');
Use Pig To Load JSON

82. Build Recommendation Model

83. $ hbase shell
> create ‘mydata’, ‘mycf’
Build Table In HBase

84. Examine Data In HDFS

85. Use Pig To Transfer Data Into HBase

86. Examine Data In HBase

87. Build API

88. Recommendation System

89. Focus on algorithm
Divide and Conquer, Trie, Collaborative Filtering
Being an expert of single programming language
But knowing what tools and algorithm you can use to solve your problem
Define your role
Statistician
Software engineer
What You Should Do

90. Website:
largitdata.com
ywchiu.com
Email:
david@largitdata.com
tr.ywchiu@gmail.com
Contacts