Modern Data Science
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The document presents a discussion on modern data science, highlighting that it encompasses skills in hacking, mathematics, statistics, and domain expertise to analyze data for informed decision-making. It emphasizes the lack of a standardized definition of data science and shares various applications, including fraud detection and anomaly identification. The talk also encourages embracing and supporting open-source tools within the data science community.
Modern Data Science
- 1. Modern Data Science Alejandro Correa Bahnsen June 2016 @albahnsen 1
- 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 2
- 3. Who I've worked with 3
- 4. Where I work Lead Data Scientist working on applying Machine Learning for Security Informatics 4
- 5. Aims of this talk Discuss what a Modern Data Scientist is (And what is not) 5
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- 7. It's 2016 and there is still no unique definition of Data Science 7
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- 9. “ A data scientist is a statistician who lives in San Fransisco. “ Data Science is statistics on a Mac. 9
- 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... 10
- 11. Even worse, people use several words interchangeable 11
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- 16. Lets focus only on modern data science 16
- 17. So what is Data Science? 17
- 18. Data Science 18
- 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 19
- 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. 20
- 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 . 24
- 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) 26
- 27. How did we get here 27
- 29. Netflix Price 29
- 33. Moneyball 33
- 34. AlphaGo 34
- 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 41
- 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 43
- 44. Class Imbalance Synthetic Majority Over Sampling Technique SMOTE 44
- 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 45
- 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. 46
- 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 47
- 49. Feature Engineering Transaction aggregated features 49
- 51. Feature Engineering Social Networks Analysis 51
- 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 52
- 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 53
- 54. Finally - Some Models 54
- 55. Takeaways 55
- 56. How could you learn more? 56
- 57. How could you learn more? 57
- 58. How could you learn more? 58
- 61. Modern Data Scientist The sexiest job of the 21th century 61