Data Science in Real Life

About this course: 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: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb

Created by:  Johns Hopkins University

• Taught by:  Brian Caffo, PhD, Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

Basic Info Course 4 of 5 in the Executive Data Science Specialization Commitment 1 week of study, 4-6 hours Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.3 stars Average User Rating 4.3See what learners said 课程作业

每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。

来自同学的帮助

与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。

证书

获得正式认证的作业，并与朋友、同事和雇主分享您的成功。

Johns Hopkins University The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus


WEEK 1


Introduction, the perfect data science experience



This course is one module, intended to be taken in one week. Please do the course roughly in the order presented. Each lecture has reading and videos. Except for the introductory lecture, every lecture has a 5 question quiz; get 4 out of 5 or better on the quiz.


22 videos, 10 readings expand

1. Video: Just for fun, course promotional video
2. 阅读: Pre-Course Survey
3. 阅读: Course structure
4. 阅读: Grading
5. Video: Data science in the ideal versus real life Part 1
6. Video: Data science in the ideal versus real life Part 2
7. Video: Examples
8. Video: Machine Learning vs. Traditional Statistics Part 1
9. Video: Machine Learning vs. Traditional Statistics Part 2
10. 阅读: The data pull is clean
11. Video: Managing the Data Pull
12. 阅读: The experiment is carefully designed
13. Video: Experimental design and observational analysis
14. Video: Causality part 1
15. Video: Causality Part 2
16. Video: What Can Go Wrong?: Confounding
17. 阅读: The experiment is carefully designed, things to do
18. Video: A/B Testing
19. Video: Sampling bias and random sampling
20. Video: Blocking and adjustment
21. 阅读: Results of analyses are clear
22. Video: Multiplicity
23. Video: Effect size, significance, & modeling
24. Video: Comparison with benchmark effects
25. Video: Negative controls
26. 阅读: The decision is obvious
27. Video: Non-significance
28. Video: Estimation Target is Relevant
29. 阅读: The analysis product is awesome
30. Video: Report writing
31. Video: Version control
32. 阅读: Post-Course Survey

Graded: The Data Pull is Clean
Graded: The experiment is carefully designed principles
Graded: The experiment is carefully designed, things to do
Graded: Results of analyses are clear
Graded: The Decision is Obvious
Graded: The analysis product is awesome