Data Science: The Art of Foul Play by Serhiy Shelpuk
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Serhiy Shelpuk, Lead Data Scientist, Competence Manager at SoftServe, Inc., delivered an insightful presentation on Data Science and SoftServe`s Data Science Group Knowledge Model at the 2013 IT ...

Serhiy Shelpuk, Lead Data Scientist, Competence Manager at SoftServe, Inc., delivered an insightful presentation on Data Science and SoftServe`s Data Science Group Knowledge Model at the 2013 IT Weekend Ukraine conference that took place on September 14, 2013, in Kyiv, Ukraine. Here`s his presentation.

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Data Science: The Art of Foul Play by Serhiy Shelpuk Presentation Transcript

  • 1. Data Science the art of foul play Sergey Shelpuk SoftServe, Inc. sshel@softserveinc.com September, 2013
  • 2. “Your goal should not be to buy players, it should be to buy wins. In order to buy wins you should buy runs” (c)
  • 3. More data is available for companies Storage technologies allow storing and operating it Advanced analytics could be applied to this new data to achieve competitive advantage
  • 4. Data Scientist: The Sexiest Job of the 21st Century For Today’s Graduate, Just One Word: Statistics Hal Varian chief economist at Google “I keep saying that the sexy job in the next 10 years will be statisticians, and I’m not kidding.” Data Scientist: The Hottest Job You Haven't Heard Of
  • 5. Top 10 Strategic Technology Trends for 2013 • Mobile Device Battles • Mobile Applications and HTML5 • Personal Cloud • Enterprise App Stores • The Internet of Things • Hybrid IT and Cloud Computing • Strategic Big Data • Actionable Analytics • In Memory Computing • Integrated Ecosystems
  • 6. McKinsey Global Institute projects approximately 140,000 to 190,000 unfilled positions of data analytics experts in the U.S. by 2018 and a shortage of 1.5 million managers and analysts who have the ability to understand and make decisions using big data.
  • 7. Business Tasks • Define prospective customers • Define traffic jams in the city • Recommend restaurants and menus • Adjust UI to the particular user • Classify body part on X-Ray image • Define market niche • Define influencers in the social networks • Define similar customers or projects in portfolio • Define informal groups in the organization • Define fraud bank transaction • Define network intrusion attempts • Provide automatic aircraft engine testing • Provide automatic IT infrastructure monitoring • Provide clinical test analysis • Define the best price for the goods or services to maximize profits • Define best working schedule for the store • Define best amount of production • Define best business rules Model Family Classification Clustering Anomaly Detection Optimization • Naïve Bayes • Logistic regression • Support Vector Machines • Neural Networks • K-Means • K nearest neighbor • Self-organized maps • Mixture of Gaussians Algorithms • Mixture of Gaussians • Self-learning anomaly detection • • • • • Gradient descent Simplex method Newton’s method Normal equations Genetic algorithms
  • 8. Cross Industry Standard Process for Data Mining
  • 9. SoftServe Data Science Group Knowledge Model Business Logic Technology Level Level Level • Basics of Business Analysis • Basics of Economics • Basics of Product Management • Basics of Organizational Behavior • Statistics/Probability • Machine Learning • Data Mining • Artificial Intelligence • Matlab/Octave •R • SQL • Parallel Computing
  • 10. Deep Learning Neural Networks
  • 11. Feature extractor Task: recognize a motorcycle Learning algorithm
  • 12. The concept of Autoencoder
  • 13. The concept of Autoencoder … … … … … © Andrew Y. Ng
  • 14. Large scale deep learning networks See more: Building high-level features using large scale unsupervised learning
  • 15. Deep learning neural networks Pre-trained as Autoencoder Typical classification neural network
  • 16. Few results Images Video Text/NLP © Andrew Y. Ng
  • 17. Deep Learning in SoftServe Phase 1 results (old-fashion anomaly detection) Phase 2 prototype (deep learning approach)
  • 18. Useful Resources • Introduction to Statistics • Introduction to Artificial Intelligence • Machine Learning • Probabilistic Graphical Models • Statistics One
  • 19. Thank you!