Этот курс входит в специализацию ''Специализация Executive Data Science'
Data Science in Real Life
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Об этом курсе
4.4
1,269 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
Чему вы научитесь
- 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
Приобретаемые навыки
Data ScienceData AnalysisStatisticsData Management
Программа курса: что вы изучите
1
Раздел
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 видео (всего 160 мин.), 10 материалов для самостоятельного изучения, 6 тестов
22 видео
Data science in the ideal versus real life Part 14мин
Data science in the ideal versus real life Part 23мин
Examples7мин
Machine Learning vs. Traditional Statistics Part 114мин
Machine Learning vs. Traditional Statistics Part 23мин
Managing the Data Pull11мин
Experimental design and observational analysis10мин
Causality part 18мин
Causality Part 29мин
What Can Go Wrong?: Confounding5мин
A/B Testing9мин
Sampling bias and random sampling5мин
Blocking and adjustment11мин
Multiplicity6мин
Effect size, significance, & modeling7мин
Comparison with benchmark effects4мин
Negative controls5мин
Non-significance5мин
Estimation Target is Relevant10мин
Report writing8мин
Version control4мин
10 материала для самостоятельного изучения
Pre-Course Survey10мин
Course structure10мин
Grading10мин
The data pull is clean10мин
The experiment is carefully designed10мин
The experiment is carefully designed, things to do10мин
Results of analyses are clear10мин
The decision is obvious10мин
The analysis product is awesome10мин
Post-Course Survey10мин
6 практического упражнения
The Data Pull is Clean10мин
The experiment is carefully designed principles10мин
The experiment is carefully designed, things to do10мин
Results of analyses are clear8мин
The Decision is Obvious10мин
The analysis product is awesome10мин
4.4
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Основные моменты
Statistics review
(44)автор: 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.
автор: 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.
Преподавателя
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
О 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.
О специализации ''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.
Часто задаваемые вопросы
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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