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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

Estimated Learning Time:  7 hours

SKILLS YOU WILL GAIN:

Probability & Statistics

Experiment

General Statistics

Communication

Data Analysis

Software Engineering

Writing

INSTRUCTORS

Course Instructor PhotoBrian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

Course Instructor PhotoJeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Course Instructor PhotoRoger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

 

  • Data Science in Real Life
  • Fee: $59.00
 

If you are thinking of taking more than one course, the subscription option may be more beneficial.
After payment, you will receive a separate email, usually within one business day, that will include instructions on how to access the course.


- Single course access (generally 30 days with option to extend if needed)
- 3-month subscription to online learning catalog - $199.00
- 12-month subscription to online learning catalog - $499.00

3-Month Subscription 1-Year Subscription