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
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 |
- Video: Just for fun, course promotional video
- Reading: Pre-Course Survey
- Reading: Course structure
- Reading: Grading
- Video: Data science in the ideal versus real life Part 1
- Video: Data science in the ideal versus real life Part 2
- Video: Examples
- Video: Machine Learning vs. Traditional Statistics Part 1
- Video: Machine Learning vs. Traditional Statistics Part 2
- Reading: The data pull is clean
- Video: Managing the Data Pull
- Reading: The experiment is carefully designed
- Video: Experimental design and observational analysis
- Video: Causality part 1
- Video: Causality Part 2
- Video: What Can Go Wrong?: Confounding
- Reading: The experiment is carefully designed, things to do
- Video: A/B Testing
- Video: Sampling bias and random sampling
- Video: Blocking and adjustment
- Reading: Results of analyses are clear
- Video: Multiplicity
- Video: Effect size, significance, & modeling
- Video: Comparison with benchmark effects
- Video: Negative controls
- Reading: The decision is obvious
- Video: Non-significance
- Video: Estimation Target is Relevant
- Reading: The analysis product is awesome
- Video: Report writing
- Video: Version control
- Reading: Post-Course Survey
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
A bit extense but very well simplified for non experts in Statistics.
Good content
Smartly selected topics for an executive course. Well balanced between theory and examples.
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Esta serie de cursos, es recomendable para iniciar en la carrera de Ciencia de Datos, conceptos claros, expuestos por catedráticos de primer nivel