data science
play

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

Sep 22, 2023 •151 likes •611 views

(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.

Recommend

Data Science: Statistics or Computer Science? 9/15/2015 DATA SCIENCE: STATISTICS OR COMPUTER

Data Science: Statistics or Computer Science? 9/15/2015 DATA SCIENCE: STATISTICS OR COMPUTER

Data Science: Statistics or Computer Science? 9/15/2015 DATA SCIENCE: STATISTICS OR COMPUTER SCIENCE? DATA SCIE DA SCIENCE: ST STATIS ISTICS TICS OR OR CO COMPU MPUTER ER SCIEN SCIENCE? IMPLICATIONS FOR STATISTICS EDUCATION IMPLIC ICATION

608 views • 24 slides
DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data Set Overview

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data Set Overview

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data Set Overview Date,Block,Primary Type,Description, Location Description,Arrest,Domestic, District 05/23/2016 05:35:00 PM,024XX W DIVISION ST,ASSAULT,SIMPLE,

430 views • 13 slides
EMIS/DS 1300: A Practical Introduction to Data Science Slides by Michael Hahsler Data + Science

EMIS/DS 1300: A Practical Introduction to Data Science Slides by Michael Hahsler Data + Science

EMIS/DS 1300: A Practical Introduction to Data Science Slides by Michael Hahsler Data + Science = Results? Data Science ? Data Sources Results What is Data Science? Data science is a concept to unify statistics, data analysis, machine

635 views • 30 slides
DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type system sets the stage for the capabilities of the language Understanding data types empowers you as a data scientist DataCamp Data Types for Data

526 views • 23 slides
Causal Data Science Roman Kern Knowledge Discovery and Data Mining 2 (Version 1.0.4) Roman Kern,

Causal Data Science Roman Kern Knowledge Discovery and Data Mining 2 (Version 1.0.4) Roman Kern,

www.tugraz.at > Motivation : With purely observational data we are not able Causal Data Science to answer many questions that one would expect data science to deliver. Taking into the causal perspective , one may (with SCIENCE assumptions, or

464 views • 24 slides
Data science for business Sebastian Sauer

Data science for business Sebastian Sauer

10.5.2019 Data science for business Data science for business Sebastian Sauer file:///Users/sebastiansaueruser/Documents/Publikationen/blog_ses/data_se/public/slides/data-science-business/intro-data-science-talk-2019-05-14.html#31 1/27

302 views • 27 slides
Data science methods for online teaching Matthew Brett What is data science not? The

Data science methods for online teaching Matthew Brett What is data science not? The

Data science methods for online teaching Matthew Brett What is data science not? The now-contemplated field of Data Science amounts to a superset of the fields of statistics and machine learning which adds some technol- ogy for scaling

216 views • 3 slides
The Care and Feeding of Data Scientists: Concrete Tips for Retaining Your Data Science Team

The Care and Feeding of Data Scientists: Concrete Tips for Retaining Your Data Science Team

The Care and Feeding of Data Scientists: Concrete Tips for Retaining Your Data Science Team Michelangelo DAgostino Senior Director, Data Science @MichelangeloDA September 13, 2018 Data Science Retention is a Real Problem Data Science

756 views • 54 slides
ww www. w.big bigbang bang-datasc atascience.com ience.com Agenda BBDS Team Data Science

ww www. w.big bigbang bang-datasc atascience.com ience.com Agenda BBDS Team Data Science

ww www. w.big bigbang bang-datasc atascience.com ience.com Agenda BBDS Team Data Science Portfolio Data Science Process Data Explosion Why Data Science? Career in Data Science What is Data Science? Machine Learning Type of Analytics

1.26k views • 90 slides
GPU POWERED SOLUTIONS IN THE SECOND KAGGLE DATA SCIENCE BOWL SECOND ANNUAL DATA SCIENCE BOWL

GPU POWERED SOLUTIONS IN THE SECOND KAGGLE DATA SCIENCE BOWL SECOND ANNUAL DATA SCIENCE BOWL

April 4-7, 2016 | Silicon Valley GPU POWERED SOLUTIONS IN THE SECOND KAGGLE DATA SCIENCE BOWL SECOND ANNUAL DATA SCIENCE BOWL Massive online data science contest Mon 14 Dec 2015 Mon 14 Mar 2016 192 teams, 293 data scientists finished

805 views • 53 slides
Presentation of Data in Science Presentation of Data in Science Filesize: 5.8 MB Reviews

Presentation of Data in Science Presentation of Data in Science Filesize: 5.8 MB Reviews

5YZGXTDJWIUB Kindle / Presentation of Data in Science Presentation of Data in Science Presentation of Data in Science Filesize: 5.8 MB Reviews Reviews An exceptional book as well as the font applied was fascinating to learn. It is loaded

330 views • 4 slides
NIH Data Science Allen Dearry, PhD Office of Data Science, NIH OD Office of Scientific

NIH Data Science Allen Dearry, PhD Office of Data Science, NIH OD Office of Scientific

NIH Data Science Allen Dearry, PhD Office of Data Science, NIH OD Office of Scientific Information Management, NIEHS NSF BDPI Meeting u Arlington, VA u April 21, 2016 What Are the Big Problems to Solve? 1. Locating the data 2. Getting access

340 views • 5 slides
Extreme Data-Intensive Scientific Computing Alex Szalay JHU Big Data in Science Data

Extreme Data-Intensive Scientific Computing Alex Szalay JHU Big Data in Science Data

Extreme Data-Intensive Scientific Computing Alex Szalay JHU Big Data in Science Data growing exponentially, in all science All science is becoming data-driven This is happening very rapidly Data becoming increasingly

347 views • 23 slides
logoslides Master degree in Data Science Why a new Master in Data Science? High volumes of data

logoslides Master degree in Data Science Why a new Master in Data Science? High volumes of data

logoslides Master degree in Data Science Why a new Master in Data Science? High volumes of data emerging in many different context led to the development of new methodologies to: Explore and organize the structure of available data. Identify

367 views • 15 slides
Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science dense complex static/non-interactive often not accompanied by an easy-to-follow narrative Data art beautiful abstract

647 views • 5 slides
Presentation of Data in Science Presentation of Data in Science Filesize: 7.64 MB Reviews

Presentation of Data in Science Presentation of Data in Science Filesize: 7.64 MB Reviews

P2TNGHDPWLPR # Doc // Presentation of Data in Science Presentation of Data in Science Presentation of Data in Science Filesize: 7.64 MB Reviews Reviews It is simple in read easier to understand. I am quite late in start reading this one, but

368 views • 4 slides
Introduction to Data Science January 11, 2016 About this course DATA 5000: Introduction to Data

Introduction to Data Science January 11, 2016 About this course DATA 5000: Introduction to Data

Introduction to Data Science January 11, 2016 About this course DATA 5000: Introduction to Data Science Some highlights: Topics for data scientists R IBM Cognos Workspace, IBM SPSS Modeler, Watson Analytics VCL cloud Course

369 views • 25 slides
Deconstructing Data Science David Bamman, UC Berkeley Info 290 Lecture 7: Data and

Deconstructing Data Science David Bamman, UC Berkeley Info 290 Lecture 7: Data and

Deconstructing Data Science David Bamman, UC Berkeley Info 290 Lecture 7: Data and representation Feb 7, 2016 Data Science knowledge raw data algorithm Data data category example web logs, cell phone activity,

834 views • 56 slides
Data Science in the Wild Lecture 12: Memory-Based Data Warehouses Eran Toch Data Science in the

Data Science in the Wild Lecture 12: Memory-Based Data Warehouses Eran Toch Data Science in the

Data Science in the Wild Lecture 12: Memory-Based Data Warehouses Eran Toch Data Science in the Wild, Spring 2019 1 Data Engineering Extract Transform Load & Clean Sources Data Warehouse Data Science in the Wild, Spring 2019 2

1.12k views • 54 slides
DataCamp Data Types for Data Science DataCamp Data Types for Data Science Creating and looping

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Creating and looping

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Creating and looping through dictionaries Hold data in key/value pairs Nestable (use a dictionary as the value of a key within a dictionary) Iterable Created by dict()

827 views • 19 slides
Big Data for housing providers Jim Vine 3Vs of Big Data Data science Source:

Big Data for housing providers Jim Vine 3Vs of Big Data Data science Source:

Big Data for housing providers Jim Vine 3Vs of Big Data Data science Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram Data mining Source: http://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

399 views • 27 slides
Data & Science A m mandate f for d data d driven c corporate i innovation By Igor

Data & Science A m mandate f for d data d driven c corporate i innovation By Igor

Data & Science A m mandate f for d data d driven c corporate i innovation By Igor Stojkovi Enterprise Analytics & Data Phillip Morris International Contents Mathematics for data science in commercial environment To prove

312 views • 15 slides
Building a Data Science Capability from Scratch Victor Hu Head of Data Science QCon - March2017

Building a Data Science Capability from Scratch Victor Hu Head of Data Science QCon - March2017

Building a Data Science Capability from Scratch Victor Hu Head of Data Science QCon - March2017 Contents Background QBE Context The five challenges: 1. Buy-in -- creating a corporate ambition for data science 2. Team

363 views • 24 slides
WELCOME DATA SCIENCE STRATEGY Are we ready for it? Asure / DOMO 3 DATA SCIENCE STRATEGY

WELCOME DATA SCIENCE STRATEGY Are we ready for it? Asure / DOMO 3 DATA SCIENCE STRATEGY

WELCOME DATA SCIENCE STRATEGY Are we ready for it? Asure / DOMO 3 DATA SCIENCE STRATEGY ASURES EXPERIENCE Bruce Harris Ulises Gonzalez-Guerra Director IT and Enterprise Business Applications, Pricing Specialist / AWS Cost

477 views • 34 slides

More recommend