John Hopkins University - Data Science in Real Life
- Offered byCoursera
Data Science in Real Life at Coursera Overview
Duration | 7 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Science in Real Life at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Data Science in Real Life at Coursera Course details
- 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
Data Science in Real Life at Coursera Curriculum
Introduction, the perfect data science experience
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
Managing the Data Pull
Experimental design and observational analysis
Causality part 1
Causality Part 2
What Can Go Wrong?: Confounding
A/B Testing
Sampling bias and random sampling
Blocking and adjustment
Multiplicity
Effect size, significance, & modeling
Comparison with benchmark effects
Negative controls
Non-significance
Estimation Target is Relevant
Report writing
Version control
Pre-Course Survey
Course structure
Grading
The data pull is clean
The experiment is carefully designed
The experiment is carefully designed, things to do
Results of analyses are clear
The decision is obvious
The analysis product is awesome
Post-Course Survey
The Data Pull is Clean
The experiment is carefully designed principles
The experiment is carefully designed, things to do
Results of analyses are clear
The Decision is Obvious
The analysis product is awesome
Data Science in Real Life at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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