课程信息
4.4
1,267 ratings
151 reviews
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...
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100% 在线课程

立即开始,按照自己的计划学习。
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可灵活调整截止日期

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建议:1 week of study, 4-6 hours

完成时间大约为5 小时
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English

字幕:English

您将学到的内容有

  • 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

您将获得的技能

Data ScienceData AnalysisData ManagementStatistics
Stacks
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Clock

建议:1 week of study, 4-6 hours

完成时间大约为5 小时
Comment Dots

English

字幕:English

教学大纲 - 您将从这门课程中学到什么

1

章节
Clock
完成时间为 5 小时

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....
Reading
22 个视频(共 160 分钟), 10 个阅读材料, 6 个测验
Video22 个视频
Data science in the ideal versus real life Part 14分钟
Data science in the ideal versus real life Part 23分钟
Examples7分钟
Machine Learning vs. Traditional Statistics Part 114分钟
Machine Learning vs. Traditional Statistics Part 23分钟
Managing the Data Pull11分钟
Experimental design and observational analysis10分钟
Causality part 18分钟
Causality Part 29分钟
What Can Go Wrong?: Confounding5分钟
A/B Testing9分钟
Sampling bias and random sampling5分钟
Blocking and adjustment11分钟
Multiplicity6分钟
Effect size, significance, & modeling7分钟
Comparison with benchmark effects4分钟
Negative controls5分钟
Non-significance5分钟
Estimation Target is Relevant10分钟
Report writing8分钟
Version control4分钟
Reading10 个阅读材料
Pre-Course Survey10分钟
Course structure10分钟
Grading10分钟
The data pull is clean10分钟
The experiment is carefully designed10分钟
The experiment is carefully designed, things to do10分钟
Results of analyses are clear10分钟
The decision is obvious10分钟
The analysis product is awesome10分钟
Post-Course Survey10分钟
Quiz6 个练习
The Data Pull is Clean10分钟
The experiment is carefully designed principles10分钟
The experiment is carefully designed, things to do10分钟
Results of analyses are clear8分钟
The Decision is Obvious10分钟
The analysis product is awesome10分钟
4.4
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50%

完成这些课程后已开始新的职业生涯
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83%

通过此课程获得实实在在的工作福利

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Statistics review
(44)
创建者 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.

创建者 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.

讲师

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

关于 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....

关于 Executive Data Science 专项课程

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....
Executive Data Science

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  • 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.

  • 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.

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