Presentation on Modern Data Science Data scientists are in high demand. There is simply not enough talent to fill the jobs. Why? Because the sexiest job of 21th century requires a mixture of broad, multidisciplinary skills ranging from an intersection of mathematics, statistics, computer science, communication and business. Finding a data scientist is hard. Finding people who understand who a data scientist is, is equally hard. Check the video in spanish here :https://www.youtube.com/watch?v=R3jeBHLLiiM

1. Modern Data Science
Alejandro Correa Bahnsen
June 2016
@albahnsen
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2. Who am I?
Data Scientist
PhD in Machine Learning
Interested in Big Data Engineering
Passionate about open-source
Scikit-Learn contributor :)
Organizer of the Bogota Big Data Science Meetup
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3. Who I've worked with
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4. Where I work
Lead Data Scientist working on applying
Machine Learning for Security Informatics
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5. Aims of this talk
Discuss what a Modern Data Scientist is
(And what is not)
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6. 6

7. It's 2016 and there is still no
unique definition of Data
Science
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8. 8

9. “ A data scientist is a statistician
who lives in San Fransisco.
“ Data Science is statistics on a
Mac.
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10. Data Science is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it...
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11. Even worse, people use
several words interchangeable
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16. Lets focus only on modern
data science
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17. So what is Data
Science?
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18. Data Science
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19. Data Science is the intersection of
Hacking Skills, Math & Statistics
Knowledge and Substantive Expertise
Those are the pillars of data science: computing,
statistics, mathematics and quantitative disciplines
combined to analyze data for better decision making
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20. Hacking Skills
Ability to build things and find clever solutions to
problems.
Programming/Coding: Python and R (and others)
Databases: MySQL, PostgreSQL, Cassandra,
MongoDB and CouchDB.
Visualization: D3, Tableau, Qlikview and Markdown.
Big Data: Hadoop, MapReduce and Spark.
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21. Hacking Skills
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22. Hacking Skills
http://www.kdnuggets.com/2016/06/r-python-top-
analytics-data-mining-data-science-software.html
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23. Hacking Skills
http://www.kdnuggets.com/2016/06/r-python-top-
analytics-data-mining-data-science-software.html
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24. Math & Statistics
Being able understand the right solution to each
problem
Linear algebra: Matrix manipulation
Machine Learning: Random Forests, SVM, Boosting
Descriptive statistics: Describe, Cluster
Statistical inference: Generate new knowledge .
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25. Math & Statistics
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26. Substantive Expertise
Ability to ask good questions requires domain
understanding, that’s why a data scientist can’t create
data based solutions without a good industry knowledge
Is this A or B or C? (classification)
Is this weird? (anomaly detection).
How much/how many? (regression).
How is it organized? (clustering).
What should I do next? (reinforcement learning)
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27. How did we get here
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28. Data Science
Examples
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29. Netflix Price
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30. Goolge flu trends
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31. Creating a rembrandt
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32. Obama campaign
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33. Moneyball
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34. AlphaGo
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35. My recent
experience
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36. Phishing Detection
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37. Malware Identification
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38. Man-in-the-Browser Attacks
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39. Intrusion Detection
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40. Fraud Detection
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41. Fraud Detection
Estimate the probability of a transaction being fraud
based on customer patterns and recent fraudulent
behavior
Issues when constructing a fraud detection system:
Class Imbalance
Cost-sensitivity
Short time response of the system
Dimensionality of the search space
Feature preprocessing
Model selection
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42. Fraud Detection
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43. Class Imbalance
Fraudulent transactions represents between 0.01% to
0.5% of the transactions
Create a balanced dataset using:
Under sampling
Over sampling
TomekLinks sampling
Condensed Nearest Neighbor
NearMiss
Synthetic Majority Over Sampling
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44. Class Imbalance
Synthetic Majority Over Sampling Technique
SMOTE
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45. Cost-Sensitivity
Typical evaluation of a classification model:
Actual Fraud Actual Legitimate
Predicted Fraud True Positives (TP) False Positives (FP)
Predicted Legitimate False Negatives (FN) True Negatives (FN)
Accuracy = TP+FP+TN+FN
TP+TN
F Score =1 TP+FN+FP
TP
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46. Cost-Sensitivity
Assumes the same financial cost of false positives and
false negatives!
Not the case in fraud detection:
False positives: When predicting a transaction as
fraudulent, when in fact it is not a fraud, there is an
administrative cost
False negatives: Failing to detect a fraud, the amount
of that transaction is lost.
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47. Cost-Sensitivity
Cost Matrix
Actual Fraud Actual Legitimate
Predicted Fraud
Predicted Legitimate
Cost(f(S)) = y (1 − c )AMT + c C∑i=1
N
i i i i a
c = CTP a c = CFP a
c = AMTFN i c = 0TN
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48. Feature Engineering
Raw Features
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49. Feature Engineering
Transaction aggregated features
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50. Feature Engineering
Periodic Features
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51. Feature Engineering
Social Networks Analysis
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52. Finally - Some Models
Data
Large European Card Processing company
2012 & 2013 card present transactions
20 Million transactions
40,000 frauds
2 Million Euros in losses in the test set
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53. Finally - Some Models
Algorithms
Fuzzy Rules
Neural Networks
Naive Bayes
Random Forests
Random Forests with Cost-Proportonate Sampling
Cost-Sensitive Random Patches Decision Trees
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54. Finally - Some Models
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55. Takeaways
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56. How could you learn more?
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57. How could you learn more?
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58. How could you learn more?
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59. Embrace open-source
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60. Support open-source
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61. Modern
Data
Scientist
The sexiest job of
the 21th century
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62. Thank You!
@albahnsen
albahnsen.com
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