生活中的数据科学

Part of a 5-course series, the Executive Data Science Specialization

约翰霍普金斯大学

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生活中的数据科学

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

生活中的数据科学 is course 4 of 5 in the Executive Data Science Specialization.

In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.

授课教师

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Roger Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Syllabus

Week 1 Introduction, the perfect data science experience

What you’ve gotten yourself into
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

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

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