Data Science in Real Life
Beschreibung
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
About this course: 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 r…
Frequently asked questions
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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
About this course: 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
Created by: Johns Hopkins University-
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health -
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health -
Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
每门课程都像是一本互动的教科书,具有预先录制的视频、测验和项目。
来自同学的帮助与其他成千上万的学生相联系,对想法进行辩论,讨论课程材料,并寻求帮助来掌握概念。
证书获得正式认证的作业,并与朋友、同事和雇主分享您的成功。
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
WEEK 1
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, 10 readings expand
- Video: Just for fun, course promotional video
- 阅读: Pre-Course Survey
- 阅读: Course structure
- 阅读: Grading
- Video: Data science in the ideal versus real life Part 1
- Video: Data science in the ideal versus real life Part 2
- Video: Examples
- Video: Machine Learning vs. Traditional Statistics Part 1
- Video: Machine Learning vs. Traditional Statistics Part 2
- 阅读: The data pull is clean
- Video: Managing the Data Pull
- 阅读: The experiment is carefully designed
- Video: Experimental design and observational analysis
- Video: Causality part 1
- Video: Causality Part 2
- Video: What Can Go Wrong?: Confounding
- 阅读: The experiment is carefully designed, things to do
- Video: A/B Testing
- Video: Sampling bias and random sampling
- Video: Blocking and adjustment
- 阅读: Results of analyses are clear
- Video: Multiplicity
- Video: Effect size, significance, & modeling
- Video: Comparison with benchmark effects
- Video: Negative controls
- 阅读: The decision is obvious
- Video: Non-significance
- Video: Estimation Target is Relevant
- 阅读: The analysis product is awesome
- Video: Report writing
- Video: Version control
- 阅读: Post-Course Survey
Graded: The Data Pull is Clean
Graded: The experiment is carefully designed principles
Graded: The experiment is carefully designed, things to do
Graded: Results of analyses are clear
Graded: The Decision is Obvious
Graded: The analysis product is awesome
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