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The Practice of Data Science - Demystifying Data Science Conference

In my talk, “The Practice of Data Science,” I provided a high-level overview of what it means to practice data science by taking a look at the people, processes and tools that underlie the field of data science. You can view this talk (and many others) by registering for the free online conference from Metis here:

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The Practice of Data Science - Demystifying Data Science Conference

  1. 1. The Practice of Data Science: People, Processes and Tools Bob. E. Hayes, PhD @bobehayes Presented at Metis’ Demystifying Data Science: A FREE Online Conference for Aspiring Data Scientists – Sept 27, 2017
  2. 2. Bob E. Hayes, PhD Email: Web: Twitter: @bobehayes • Author of three books on customer experience management and analytics • PhD in industrial-organizational psychology • #6 blogger overall on CustomerThink ( • #3 blogger on the topic of customer analytics ( • Top expert in Big Data and Data Science • experts/ • influencers-brands.html
  3. 3. 3 Outline • Why now? • Definition of Data Science • The People: Data Science Skills • The Process: From Data to Insight • The Tools • Education Requirements • Gender Diversity
  4. 4. 4 Data and Our Ability to Process it
  5. 5. Analytics Skills Gap is Huge* * From PwC: Investing in America’s Data Science and Analytics Talent
  6. 6. 6 Data Science Defined Data science is way of extracting insights from data using the powers of computer science and statistics applied to data from a specific field of study.
  7. 7. 7 Data Science Defined The People
  8. 8. 8 JobRolesinDataScience *Researcher (e.g., researcher, scientist, statistician); Business Management (e.g., leader, business person, entrepreneur); Creative (e.g., jack of all trades, artist, hacker); Developer (e.g., developer, engineer)
  9. 9. 9 Three Skill Domains of Data Science Domain Knowledge Math / Statistics Technology / Programming
  10. 10. 10 25 Data Science Skills Top 10 Data Science Skills 1. Communication 2. Managing structured data 3. Data mining and visualization tools 4. Science / Scientific method 5. Math 6. Project management 7. Data management 8. Statistics and statistical modeling 9. Product design and development 10. Business developmentData are based on responses to AnalyticsWeek and Business Over Broadway Data Science Survey. From September 2015.
  11. 11. 11 Skill Proficiency Varies by Data Science Role 0 10 20 30 40 50 60 70 80 Buisness development Budgeting Goverance and Compliance Optimization Math Graphical Models Algorithms Bayesian Statistics Machine Learning Data Mining and Viz Tools Statistics and statistical modeling Science/Scientific Method CommunicationUnstructured data Structured data NLP and text mining Data Management Big and distributed data Systems Administration Database Administration Cloud Management Back-end Programming Front-end Programming Product Design Project management Domain Expert Developer Researcher Proficiency Standard Math / Statistics Tech / Programming Domain Knowledge Data are based on responses to AnalyticsWeek and Business Over Broadway Data Science Survey. From September 2015.
  12. 12. 12 In Search of the Data Science Unicorn I wish I knew some Python. Data are based on responses to AnalyticsWeek and Business Over Broadway Data Science Survey. From 2015.
  13. 13. 14 Analytics, Data Mining and Data Science Methods S = Start with Strategy M = Measure Metrics and Data A = Apply Analytics R = Report Results T = Transform your Business From “CRISP-DM, still the top methodology for analytics, data mining, or data science projects“
  14. 14. 15 Cross Industry Standard Process for Data Mining (CRISP-DM) (IBM, Teradata, Daimler AG, NCR Corporation and OHRA) From Data to Insight For more information on these methods, see:;; Knowledge Discovery in Databases (KDD) SEMMA (SAS)
  15. 15. 16 Getting Insight from Data: The Scientific Method 1. Formulate Questions 2. Generate hypothesis/ hunch 3. Gather / Generate data 4. Analyze data / Test hypothesis 5. Take action / Communicate results • Start with a problem statement. • What are your hunches / hypotheses? • Be sure your hypotheses are testable. • You can use experimental or observational approach to analyzing data. • Integrate your data silos to ask bigger questions; connect the dots and get a 360 degree view of the phenomenon you’re studying. • Employ Predictive analytics / Inferential statistics to test hypotheses. • Employ machine learning to quickly surface insights. • Implement your findings; inform decision-makers; optimize algorithms • Use Prescriptive analytics to guide course of action.
  16. 16. 17 Iterative Process of Discovery Image from Netflix Tech Blog: with-experimentation-and-data-5b0ae9295bdf
  17. 17. 18 Scientific Method and Data Science Skills
  18. 18. 19 The Tools
  19. 19. 20 Top Data Science Tools Rexer Analytics Data Science Survey 2015 For a comprehensive overview of different data science tools, please see:
  20. 20. 21 Data Science Ecosystem Gartner Magic Quadrant (2017) Forrester Wave Leaders IBM SAS RapidMiner KNIME For a good review of data science platforms, please see:
  21. 21. 22 Extra Important Skills, Role of Formal Education, Gender Diversity
  22. 22. 23 Importance of Data Science Skills by Job Role
  23. 23. 24 What skills are linked to project success?
  24. 24. 25 Highest Level of Education Attained
  25. 25. 26 Education and Data Science Skills Data are based on responses to AnalyticsWeek and Business Over Broadway Data Science Survey. From 2015.
  26. 26. 27 Lack of Gender Diversity
  27. 27. 28 Job Roles in Data Science by Gender
  28. 28. 29 Gender Diversity – Other Science Roles
  29. 29. 30 Gender Comparison of Proficiency across Skills
  30. 30. 31 Advice for Data Scientists • Be specific when talking about “data scientists” • There are different types – defined by what they do and the skills they possess • Work with other data professionals who have complementary skills. Teamwork is key to successful data science projects. • Learn to use data mining and visualization tools • R, Python, SPSS, SAS, graphics, mapping, web-based data visualization • Be an advocate for women in the field of data science