Data Science in Real Life (Coursera)

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.

Course 4 of 5 in the Executive Data Science Specialization.

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

What You Will Learn

- Identify strengths and weaknesses in experimental designs

- Learn novel solutions for managing data pulls

- Describe common pitfalls in communicating data analyses

- Understand a typical day in the life of a data analysis manager

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.