Ce cours fait partie de la Spécialisation Executive Data Science
Data Science in Real Life
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À propos de ce cours
4.4
1,236 ratings
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
Ce que vous allez apprendre
- Describe common pitfalls in communicating data analyses
- Identify strengths and weaknesses in experimental designs
- Learn novel solutions for managing data pulls
- Understand a typical day in the life of a data analysis manager
Compétences que vous acquerrez
Data ScienceData AnalysisData ManagementStatistics
Programme du cours : ce que vous apprendrez dans ce cours
1
Section
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 vidéos (Total 160 min), 10 lectures, 6 quiz
22 vidéos
Data science in the ideal versus real life Part 14 min
Data science in the ideal versus real life Part 23 min
Examples7 min
Machine Learning vs. Traditional Statistics Part 114 min
Machine Learning vs. Traditional Statistics Part 23 min
Managing the Data Pull11 min
Experimental design and observational analysis10 min
Causality part 18 min
Causality Part 29 min
What Can Go Wrong?: Confounding5 min
A/B Testing9 min
Sampling bias and random sampling5 min
Blocking and adjustment11 min
Multiplicity6 min
Effect size, significance, & modeling7 min
Comparison with benchmark effects4 min
Negative controls5 min
Non-significance5 min
Estimation Target is Relevant10 min
Report writing8 min
Version control4 min
10 lectures
Pre-Course Survey10 min
Course structure10 min
Grading10 min
The data pull is clean10 min
The experiment is carefully designed10 min
The experiment is carefully designed, things to do10 min
Results of analyses are clear10 min
The decision is obvious10 min
The analysis product is awesome10 min
Post-Course Survey10 min
6 exercices pour s'entraîner
The Data Pull is Clean10 min
The experiment is carefully designed principles10 min
The experiment is carefully designed, things to do10 min
Results of analyses are clear8 min
The Decision is Obvious10 min
The analysis product is awesome10 min
4.4
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Statistics review
(44)par SM•Aug 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.
par ES•Nov 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.
Enseignants
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
À propos de 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.
À propos de la Spécialisation Executive Data Science
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.
Foire Aux 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?
Is financial aid available?
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