The Analytics Continuum

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Quick tour of data analytics and machine learning for the discerning business analyst and investment banker.

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The Analytics Continuum

  1. 1. The Analytics Continuum Rob Marano 7 May 2014 15/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
  2. 2. “What’s measured improves.” Peter F. Drucker © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 25/7/14
  3. 3. “Knowledge has to be improved, challenged, and increased constantly, or it vanishes.” Peter F. Drucker © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 35/7/14
  4. 4. “When you develop your opinions on the basis of weak evidence, you will have difficulty interpreting subsequent information that contradicts these opinions, even if this new information is obviously more accurate.” Nassim Nicholas Taleb The Black Swan: The Impact of the Highly Improbable © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 45/7/14
  5. 5. Agenda • Execution vs. search • Balancing the “knowns” & “unknowns” • Data here, there, everywhere … • Machine learning as foundation to analytics • Visualization as action to analytics • Imminent opportunities © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 55/7/14
  6. 6. History of Analytics Source: Economic Time of India What drives the progression? © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 65/7/14
  7. 7. Why Consider Such an Investment? • Like any innovation, right? • Enable the business to gain – Competitive advantage – Cost cutting via productivity or automation – Compliance • But what about all that tech we already have? Is change good to the bottom line? © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 75/7/14
  8. 8. Why Consider Such an Investment? • Machine learning is used in – Web search – Spam filters – Recommender systems – Ad placement – Credit scoring – Fraud detection – Stock trading – Drug design – and much more © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 85/7/14
  9. 9. Impact of “Startup Culture” • The most successful of businesses have perfected execution • They run operations with the highest level of efficiency and effectiveness for the business • Like any auto-assist or fully automated system, the operations are modeled perfectly Change is not considered a constant or asset © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 95/7/14
  10. 10. Impact of “Startup Culture” • The most successful of starts have perfected change as its advantage to search for its niche • Startups build solutions that anticipate change, especially on how to use data to pivot • Data & analytics form core to manage change Startups value change inherently © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 105/7/14
  11. 11. Impact of “Startup Culture” • The startup community continues to be the vendor of choice behind all modern analytics • Google, Yahoo, Facebook, Twitter, etc … the list goes on • Google started this “analytics age” – open source now dominates it Any business has access to modern analytics © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 115/7/14
  12. 12. Knowns & Unknowns • Knowledge & business strategy – “Known knowns” – “Known unknowns” – “Unknown unknowns” • Operations & strategy depend upon evidence • Timely get the right info to the right person © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 125/7/14
  13. 13. (Big) Data Here, There, Everywhere • Data operates every process but not collected • The more online, the more potential • Advantages – Competitive – Productivity/efficiency – Compliance Wisdom Knowledge Info Data © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 135/7/14
  14. 14. How Big is “Big Data?” 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 14 What’s big for your department? Company? Source: InfoChimps, “[Infographic] Taming Big Data from Wikibon”
  15. 15. Foundation of Analytics • Historically rigid data dictionaries provided advantages via SQL and RDBMS • As compute/storage reduced in cost & deployment complexities, more data processed • Cost of infrastructure kept rising; state-of-the- art not keeping pace Big Data enables commodity analytics © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 155/7/14
  16. 16. Analytics Core • Big Data – Commodity computation & storage – Modern computation framework – Open, loose-coupling of components • Machine learning – Commodity knowledge discovery • Delivered as a cost-effective service © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 165/7/14
  17. 17. IT Transition to Big Data Analytics • Startup advantages lead to cost-effective analysis of large quantities of data • Traditional data warehouse solutions do not effectively scale in cost nor productivity • Growth of open source delivers both New “open” vendors leading the way © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 175/7/14
  18. 18. Big Data as Enabler Source: VMware Blog, “4 Key Architecture Considerations for Big Data Analytics” © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 185/7/14
  19. 19. 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 19
  20. 20. Apache Hadoop as Epicenter 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 20 DataIntegration (Flume,Chukwa,Sqoop) Scripting (Pig) Distributed Storage (HDFS) SystemsManagement&Monitoring (Ambari,Zookeeper) Workflow&Scheduling (Oozie) Database (Hbase,Cassandra) Distributed Compute (MapReduce) Meta Data Services (HCatalog) Query (Hive) MachineLearning (Mahout) Source: Hortonworks, “About Hortonworks Data Platform”
  21. 21. The Hadoop Ecosystem • Ambari Deployment, configuration and monitoring • Flume Collection and import of log and event data • HBase Column-oriented database scaling to billions of rows • HCatalog Schema and data type sharing over Pig, Hive and MapReduce • HDFS Distributed redundant file system for Hadoop • Hive Data warehouse with SQL-like access • Mahout Library of machine learning and data mining algorithms • MapReduce Parallel computation on server clusters • Pig High-level programming language for Hadoop computations • Oozie Orchestration and workflow management • Sqoop Imports data from relational databases • Whirr Cloud-agnostic deployment of clusters • Zookeeper Configuration management and coordination 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 21 Source: Edd Dumbill, “What is Apache Hadoop?”
  22. 22. So, what is Machine Learning? • Non-trivial process of finding and communicating “valid, novel, potentially useful and understandable patterns in data.”1 • Delivers the engineering behind the science of automated classification, categorization, and recommendation without being explicitly programmed • Allows data to be transformed with relative ease into actionable knowledge ML powers today’s internet economies 1: Ciro Donalek, “Supervised & Unsupervised Learning” © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 225/7/14
  23. 23. Machine Learning as Enabler • Open source, cloud computing, & startup culture powered rise of analytics • Delivers powerful processing & results • Figures out how to perform a particularly manual task by generalizing from examples Tactics & strategy require evidence that learns © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 235/7/14
  24. 24. Learning – Human or Machine • Learning an iterative process to converge • The ML “space” is huge and growing, but get a handle on the intended mission objectives – Representation • Which group of classifiers will “it” learn; which features – Evaluation • Distinguish good from bad classifiers – Optimization • Which is the highest scoring classifier 1: Pedro Domingos, “A Few Useful Things to Know about Machine Learning” © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 245/7/14
  25. 25. Analytics Starts With Data 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 25 Ingestion Conversion Upload Image Source: Research Live, “Order from Chaos” websites + web svcs
  26. 26. and It Ends with Knowledge 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 26 Aggregation Analysis Visualization Image Source: Visualize This by Nahan Yau Wisdom Knowledge Info Data
  27. 27. Taxonomy of ML • ML converts data trends into logic to automate data processing • Based upon pattern recognition • Basic goal is generalization • Built upon two key techniques – Supervised learning – Unsupervised learning 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 27
  28. 28. Supervised Learning • ML technique which takes a training data set with specific features that result in a model • The model is used to assess whether an input is of a pre-defined class • Key to supervised learning remains feature set extraction • Popular examples include – Regression – Classification – Outliers detection 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 28
  29. 29. Unsupervised Learning • ML technique to group data according to similar features, or characteristics • Such technique does not require a model to be generated, rather similarity is calculated • Popular examples include – Clustering – Density estimation – Visualization by projection 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 29
  30. 30. Most Important Step in ML • “Know thine data like thyself” – Know features about your data in order to narrow the algorithm selection process – Are the features nominal or continuous? – Are there missing values in the features? – If missing values, where are they missing? – Are there outliers in the data? – Are you looking for something that occurs very infrequently? 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 30
  31. 31. Choosing the ML Algorithm • Know your data inside out & back again • Consider the goal • Use unsupervised unless need to predict certain target values, then use supervised • Choose a set of algos matched to goal/data • Try each algorithm, assess and compare • Adjust and combine optimization techniques • Choose, operate, and continually measure • Repeat 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 31
  32. 32. Generalized ML Application Steps • Collect data • Prepare the input data • Analyze input data & features • Train the algorithm (if supervised) • Test the algorithm with fresh data • Operate ML • Detect subtle changes to data (cycles,seasons) • Measure for performance • Repeat as frequently needed 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 32 Portions sourced: Machine Learning in Action by Peter Harrington, Manning Publications
  33. 33. Highlights of Supervised Algos • Generalized Linear Models – Bayesian Regression – Ordinary least squares (regression) • Support Vector Machines • K Nearest Neighbors • Naïve Bayes • Decision Trees • Neural Networks • Ensemble Methods 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 33 Portions sourced: “Supervised Learning” from scikit-learn.org
  34. 34. Highlights of Unsupervised Algos • Clustering • K-means • DBSCAN • Hidden Markov Models • Density Estimation • Neural Networks (restricted Boltzmann) 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 34 Portions sourced: “Supervised Learning” from scikit-learn.org
  35. 35. Learning -> Evaluation 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 35 • The Classifier Evaluation Framework 1 2 : Knowledge of 1 is necessary for 2 1 2 : Feedback from 1 should be used to adjust 2 Choice of Learning Algorithm(s) Datasets Selection Error-Estimation/ Sampling Method Performance Measure of Interest Statistical Test Perform Evaluation Source: “Performance Evaluation of Machine Learning Algorithms” by Mohak Shah & Nathalie Japkowicz
  36. 36. Overview of Performance Measures 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 36 All Measures Additional Info (Classifier Uncertainty Cost ratio Skew) Confusion Matrix Alternate Information Deterministic Classifiers Scoring Classifiers Continuous and Prob. Classifiers (Reliability metrics) Multi-class Focus Single-class Focus No Chance Correction Chance Correction Accuracy Error Rate Cohen’s Kappa Fielss Kappa TP/FP Rate Precision/Recall Sens./Spec. F-measure Geom. Mean Dice Graphical Measures Summary Statistic Roc Curves PR Curves DET Curves Lift Charts Cost Curves AUC H Measure Area under ROC- cost curve Distance/Error measures KL divergence K&B IR BIR RMSE Information Theoretic Measures Interestingness Comprehensibility Multi- criteria Source: “Performance Evaluation of Machine Learning Algorithms” by Mohak Shah & Nathalie Japkowicz
  37. 37. Confusion Matrix-Based Performance Measures 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 37 • Multi-Class Focus: – Accuracy = (TP+TN)/(P+N) • Single-Class Focus: – Precision = TP/(TP+FP) – Recall = TP/P – Fallout = FP/N – Sensitivity = TP/(TP+FN) – Specificity = TN/(FP+TN) True class  Hypothesized class Pos Neg Yes TP FP No FN TN P=TP+FN N=FP+TN Confusion Matrix Source: “Performance Evaluation of Machine Learning Algorithms” by Mohak Shah & Nathalie Japkowicz
  38. 38. Tying It All Together 5/7/14 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 38
  39. 39. Visualization as Action © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 395/7/14
  40. 40. Imminent Opportunities • Any business with high volume of data – Look at processes, human-machine interfaces – Sentiment; Customer Experience; Campaigns – Infosec; Network Services; Customer Churn • Sectors coming analytics-ready – Healthcare; Government; Retail – Manufacturing; Utilities • Imagine a world of Internet-of-Things? Can you imagine keeping all data? Analyze it? © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 405/7/14
  41. 41. Analytics • Big Data – Commodity compute & storage • Analytics – Commodity intelligence • Big Data Analytics – Store everything – Analyze everything – Do it everyday Cost effectively manage “unknown unknowns” © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 415/7/14
  42. 42. “Know the enemy and know yourself; in a hundred battles you will never be in peril.” Sun Tzu © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 425/7/14
  43. 43. “It’s no longer hard to find the answer to a given question; the hard part is finding the right question, and as questions evolve, we gain better insight into our own ecosystem and our business.” Kevin Weil Director of Product for Revenue Twitter © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 435/7/14
  44. 44. The Analytics Continuum Rob Marano rob@thehackerati.com 7 May 2014 © 2014 The Hackerati, Inc. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 445/7/14

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