Data Science in Real Life

1,149 ratings
144 reviews

Course 4 of 5 in the Executive Data Science Specialization

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: Course cover image by Jonathan Gross. Creative Commons BY-ND

100% 在线课程


完成时间大约为5 小时

建议:1 week of study, 4-6 hours
Comment Dots




  • Check
    Describe common pitfalls in communicating data analyses
  • Check
    Identify strengths and weaknesses in experimental designs
  • Check
    Learn novel solutions for managing data pulls
  • Check
    Understand a typical day in the life of a data analysis manager


Data ScienceData AnalysisData ManagementStatistics

100% 在线课程


完成时间大约为5 小时

建议:1 week of study, 4-6 hours
Comment Dots



Syllabus - What you will learn from this course


5 hours to complete

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....
22 videos (Total 160 min), 10 readings, 6 quizzes
Video22 videos
Data science in the ideal versus real life Part 14m
Data science in the ideal versus real life Part 23m
Machine Learning vs. Traditional Statistics Part 114m
Machine Learning vs. Traditional Statistics Part 23m
Managing the Data Pull11m
Experimental design and observational analysis10m
Causality part 18m
Causality Part 29m
What Can Go Wrong?: Confounding5m
A/B Testing9m
Sampling bias and random sampling5m
Blocking and adjustment11m
Effect size, significance, & modeling7m
Comparison with benchmark effects4m
Negative controls5m
Estimation Target is Relevant10m
Report writing8m
Version control4m
Reading10 readings
Pre-Course Survey10m
Course structure10m
The data pull is clean10m
The experiment is carefully designed10m
The experiment is carefully designed, things to do10m
Results of analyses are clear10m
The decision is obvious10m
The analysis product is awesome10m
Post-Course Survey10m
Quiz6 practice exercises
The Data Pull is Clean10m
The experiment is carefully designed principles10m
The experiment is carefully designed, things to do10m
Results of analyses are clear8m
The Decision is Obvious10m
The analysis product is awesome10m
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Top Reviews

Statistics review
By SMAug 20th 2017

A very good and concise course that helps to understand the basics of the Data Science and its applications. The examples are very relevant and helps to understand the topic easily.

By ESNov 12th 2017

Highly educational course on the realities of data analysis. Many good tips for your own analyses as well as for managing others responsible for coherent and accurate analyses.



Brian Caffo, PhD

Professor, Biostatistics

Jeff Leek, PhD

Associate Professor, Biostatistics

Roger D. Peng, PhD

Associate Professor, Biostatistics

About Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

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