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 course is part of the Executive Data Science Specialization
Offered By
Executive Data Science SpecializationJohns Hopkins UniversityAbout this Course
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Learner Career Outcomes
33%
started a new career after completing these courses
14%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 5 hours to complete
English
Subtitles: English
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
Skills you will gain
StatisticsData ScienceData AnalysisData Management
Learner Career Outcomes
33%
started a new career after completing these courses
14%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 5 hours to complete
English
Subtitles: English
Offered by
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.
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.
5 hours to complete
22 videos (Total 160 min), 10 readings, 6 quizzes
22 videos
Data science in the ideal versus real life Part 14m
Data science in the ideal versus real life Part 23m
Examples7m
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
Multiplicity6m
Effect size, significance, & modeling7m
Comparison with benchmark effects4m
Negative controls5m
Non-significance5m
Estimation Target is Relevant10m
Report writing8m
Version control4m
10 readings
Pre-Course Survey10m
Course structure10m
Grading10m
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
6 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
About the Executive Data Science Specialization
Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects.
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.
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