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Data Science in Real Life No ratings yet.




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Data Science in Real Life

Created/Published/Taught by:
Coursera
Johns Hopkins University
Brian Caffo
Jeff Leek
Roger D. Peng

Content Found Via:
Coursera

Free? Partially: Some Free Content, Some Paid

Cost Range:
$0.00 - $36.00

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This course is #4 of 5 in the Executive Data Science Specialization from Johns Hopkins University.

From Coursera:

“Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We’ve left the technical information aside so that you can focus on managing your team and moving it forward.

After completing this course you will know how to:

  1. Describe the “perfect” data science experience
  2. Identify strengths and weaknesses in experimental designs
  3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls.
  4. Challenge statistical modeling assumptions and drive feedback to data analysts
  5. Describe common pitfalls in communicating data analyses
  6. Get a glimpse into a day in the life of a data analysis manager.

The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include:

  1. Experimental design, randomization, A/B testing
  2. Causal inference, counterfactuals,
  3. Strategies for managing data quality.
  4. Bias and confounding
  5. Contrasting machine learning versus classical statistical inference”

Course promo on Youtube.

 

Recommended Prerequisites: Requires "a basic understanding of how data can be used in an industry, academic, or government environment."

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