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DATA SCIENCE DAN S REZNIK, DIRECTOR DATA SCIENCE CONSULTING LTD - PowerPoint PPT Presentation

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(c) 2019 Data Science Consutling Ltd. 4 MYTHS ABOUT DATA SCIENCE DAN S REZNIK, DIRECTOR DATA SCIENCE CONSULTING LTD (c) 2019 Data Science Consutling Ltd. Artificial Intelligence is Intelligence Machine Learning is Learning CONTENTS

  1. (c) 2019 Data Science Consutling Ltd. 4 MYTHS ABOUT DATA SCIENCE DAN S REZNIK, DIRECTOR DATA SCIENCE CONSULTING LTD

  2. (c) 2019 Data Science Consutling Ltd. • Artificial Intelligence is Intelligence • Machine Learning is Learning CONTENTS • Useful Analytics is Predictive • Data Science is Science

  3. (c) 2019 Data Science Consutling Ltd. MYTH 1: ARTIFICIAL THE CLOWN SHOW INTELLIGENCE IS INTELLIGENCE

  4. (c) 2019 Data Science Consutling Ltd.

  5. (c) 2019 Data Science Consutling Ltd. TYPES OF INTELLIGENCE

  6. (c) 2019 Data Science Consutling Ltd.

  7. (c) 2019 Data Science Consutling Ltd. INTELLIGENCE HUMAN ARTIFICIAL • Parse digital data • Understanding content • Achieve specific goals and tasks • Awareness of self, other, and context • Adapt • Learning, applying • Applications • Emotional intelligence / theory of mind • Games • Reasoning / problem-solving • OCR, voice-to-text • Planning, Creating • Object, face recognition • Critical thinking, rejecting • Autonomous cars • Joking / Loving / Giving

  8. (c) 2019 Data Science Consutling Ltd. ATLAS

  9. (c) 2019 Data Science Consutling Ltd. AUTONOMOUS CARS

  10. HOW ABOUT “DEEP LEARNING”? (c) 2019 Data Science Consutling Ltd.

  11. (c) 2019 Data Science Consutling Ltd. AI “WINTERS”

  12. (c) 2019 Data Science Consutling Ltd. GOL

  13. (c) 2019 Data Science Consutling Ltd. MYTH 2: MACHINE LEARNING IS LEARNING

  14. (c) 2019 Data Science Consutling Ltd. LEARNING HUMAN MACHINE • Estimate parameters • Cognitive: recall, calculate, discuss, analyze, problem solve, etc. • Hierarchically split • Psychomotor: dance, swim, play football, • Reinforce correct behavior dive, drive, ride, etc. • Regress network weights • Affective: To like something or someone, • Cluster love, appreciate, fear, hate, worship, etc.

  15. (c) 2019 Data Science Consutling Ltd. FACE SURVEILLANCE

  16. (c) 2019 Data Science Consutling Ltd. DEEP LEARNING

  17. (c) 2019 Data Science Consutling Ltd. MYTH 3: USEFUL ANALYTICS IS PREDICTIVE

  18. (c) 2019 Data Science Consutling Ltd. ANALYTICS PREDICTIVE DESCRIPTIVE • Describe Past • Model past’s parameters • Visualize Past • Project trends • Project distribution • Project multinomial fit • Stationarity

  19. (c) 2019 Data Science Consutling Ltd. DASHBOARDS

  20. (c) 2019 Data Science Consutling Ltd. CRIME PREDICTION

  21. (c) 2019 Data Science Consutling Ltd. TIME-SERIES FORECASTING

  22. (c) 2019 Data Science Consutling Ltd. TIME-SERIES FORECASTING

  23. T echs Strategic to BI (c) 2019 Dresner Advisory https://twitter.com/gp_pulipaka/status/1178379976514494470?s=20 (c) 2019 Data Science Consutling Ltd.

  24. (c) 2019 Data Science Consutling Ltd. MYTH 4: DATA SCIENCE IS SCIENCE

  25. (c) 2019 Data Science Consutling Ltd. WRANGLE FEATURES

  26. (c) 2019 Data Science Consutling Ltd. TAKE THE TRAIN

  27. (c) 2019 Data Science Consutling Ltd. MESTRE CALCETEIRO

  28. (c) 2019 Data Science Consutling Ltd. CABINET MAKING

  29. (c) 2019 Data Science Consutling Ltd. START WITH PROBLEM NOT TECH

  30. (c) 2019 Data Science Consutling Ltd. JOHN TUKEY (1915-2000) It is far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise

  31. (c) 2019 Data Science Consutling Ltd. WILL BARBERS OR DATA SCIENTISTS BE AUTOMATED? • Identify real-world problems, choose analysis plan • Design data collection, assess quality, correct labels, reject data • Incorporate domain knowledge, e.g., via feature engineering • Anticipate risks, manage them • Manage biases, ethical issues, impact of project in society • Analyze and critique performance • Explain insights to human stakeholders, convince them

  32. (c) 2019 Data Science Consutling Ltd. HOW ABOUT R?

  33. (c) 2019 Data Science Consutling Ltd. ECOSYSTEM

  34. (c) 2019 Data Science Consutling Ltd. 15k CRAN PACKAGES

  35. (c) 2019 Data Science Consutling Ltd. R VS PYTHON

  36. (c) 2019 Data Science Consutling Ltd. R VS PYTHON

  38. (c) 2019 Data Science Consutling Ltd.

  39. 39 (c) 2019 Dan S. Reznik -- FDC

  40. 40 IN TERMS OF GENERAL INTELLIGENCE, WE’RE NOT EVEN CLOSE TO A RAT. YANN LECUN FACEBOOK AI DIRECTOR (c) 2019 Dan S. Reznik -- FDC

  41. 41 (c) 2019 Dan S. Reznik -- FDC

  42. (c) 2019 Data Science Consutling Ltd. • Artificial Intelligence is a statistical modeling • Machine Learning is statistical modeling SUMMARY • Useful Analytics is still mostly descriptive • Data Science is carpentry

  43. (c) 2019 Data Science Consutling Ltd. THANK YOU DAN@DAT -SCI.COM

  44. (c) 2019 Data Science Consutling Ltd.

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