Fundamentals of Data Science: What is Data Science?

Data science understood through

  • vocabulary
  • industry leaders
  • myths
  • data products

Vocabulary

An interdisciplinary science and supported by data (digital representation of information), Data Science combines formal science (Math, Logic) and applied science (Sociology, Stats, Computer Science).

It provides actionable intelligence via testable explanations, predictions, interactive intelligence, and intelligent machines.

Industry Leaders

“… a hybrid skill set that combines analytical, statistical, development and engineering skills that enable a team to provide value insights, and direction to people.”

Ann-Jinette Hess, Data Scientist/Manager @ Rackspace

“… equal parts hacker, stats geek, and entrepreneur.”

Chris Chapo, Data Scientist @ Analytical-Solution

“…detecting patterns that can then be used to help people make better decisions.”

Alice Zhen, Data Scientist/Manager @ Amazon

Myths

  1. Data Science == Statistics
    • Used in data science but it’s only a small part of it
  2. Data Science == Business Analyst
    • light on decision science and heavy on KPI reporting
  3. Data Science == Data Science
    • no common understanding between hiring managers, recruiters, and applicants
  4. Data Science curriculum is consistent across educators
    • different curricula
  5. If I want to be a data scientist, I just need to learn how to use R or Python.
    • autoCAD does not make an architect

Products

  • Recommenders – YouTube, Netflix, Social Media, Pinterest, Amazon
  • Optimization – UPS No Left Turn Project
  • Advertising – how to make people click ads
  • Social Services – The Crisis Text Line
  • Cyber Security – account takeovers, fraud detection

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