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 cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb
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.
Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be easy.
Data Science in Real Life is the fourth and final course in the “Executive Data Science” specialization offered by John Hopkins University on Coursera. The one-week course examines various steps in the data analysis process and contrasts ideal outcomes...
Data Science in Real Life is the fourth and final course in the “Executive Data Science” specialization offered by John Hopkins University on Coursera. The one-week course examines various steps in the data analysis process and contrasts ideal outcomes against the outcomes you are likely to experience in reality. Grading is based upon a few short multiple-choice quizzes.
The lecture videos are crisp and the professor does a good job explaining the topics without being overly technical. It does discuss some topics that you won’t fully appreciate without having hand-on experience doing data science projects, but it will help prepare you for some of the problems you might encounter. Like other courses in the Executive Data Science track, there’s not too much to dislike about this course other than its brevity and the limited depth at which topics can be covered in a one-week course.
Data Science in Real Life is nice, succinct overview of many of the challenges you are likely to face in data projects and suggestions for overcoming them. It is raises considerations that could be useful for both data analysts and managers.
I give Data Science in Real Life 4 out of 5 stars: Very Good.
I enjoyed taking this course and I think that it delivers on what it promises on the title. I recommend taking this course after the first three recommended titles in the Coursera Specialization where it belongs.