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Data Science in Real Life
  • Level Foundation
  • المدة 7 ساعات hours
  • الطبع بواسطة Johns Hopkins University
  • Offered by

عن

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

الوحدات

What you've gotten yourself into

    • 6 Videos
    • 3 Readings
Show info about module content

6 Videos

  • Just for fun, course promotional video
  • Data science in the ideal versus real life Part 1
  • Data science in the ideal versus real life Part 2
  • Examples
  • Machine Learning vs. Traditional Statistics Part 1
  • Machine Learning vs. Traditional Statistics Part 2

3 Readings

  • Pre-Course Survey
  • Course structure
  • Grading

The data pull is clean

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • The Data Pull is Clean

1 Videos

  • Managing the Data Pull

1 Readings

  • The data pull is clean

The experiment is carefully designed, principles

    • 4 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • The experiment is carefully designed principles

4 Videos

  • Experimental design and observational analysis
  • Causality part 1
  • Causality Part 2
  • What Can Go Wrong?: Confounding

1 Readings

  • The experiment is carefully designed

The experiment is carefully designed, things to do

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • The experiment is carefully designed, things to do

3 Videos

  • A/B Testing
  • Sampling bias and random sampling
  • Blocking and adjustment

1 Readings

  • The experiment is carefully designed, things to do

Results of analyses are clear

    • 4 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Results of analyses are clear

4 Videos

  • Multiplicity
  • Effect size, significance, & modeling
  • Comparison with benchmark effects
  • Negative controls

1 Readings

  • Results of analyses are clear

The Decision is obvious

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • The Decision is Obvious

2 Videos

  • Non-significance
  • Estimation Target is Relevant

1 Readings

  • The decision is obvious

The analysis product is awesome

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • The analysis product is awesome

2 Videos

  • Report writing
  • Version control

1 Readings

  • The analysis product is awesome

Post-Course Survey

    • 1 Readings
Show info about module content

1 Readings

  • Post-Course Survey

Auto Summary

"Data Science in Real Life" is a one-week foundational course designed for managers in the Data Science & AI domain. Taught by Coursera, it focuses on bridging the gap between ideal data science scenarios and real-world challenges. Learners will explore experimental design, data quality management, and effective communication of analyses. The course emphasizes practical, conceptual learning without heavy technical details. Subscription options include Starter, Professional, and Paid, making it accessible for varied needs. Ideal for active managers of data scientists and statisticians, this course ensures actionable insights and improved team management in data-driven environments.

Brian Caffo, PhD
Instructors

Brian Caffo, PhD

Jeff Leek, PhD
Instructors

Jeff Leek, PhD

Roger D. Peng, PhD
Instructors

Roger D. Peng, PhD