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学生对 约翰霍普金斯大学 提供的 生活中的数据科学 的评价和反馈

2,134 个评分
252 条评论


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: Course cover image by Jonathan Gross. Creative Commons BY-ND
Statistics review
(44 条评论)


Aug 19, 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.

Nov 11, 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.


126 - 生活中的数据科学 的 150 个评论(共 251 个)

创建者 David G

Dec 14, 2016

Nice course thx

创建者 Manish K

Jul 4, 2020

Nice course :)

创建者 Kayal V

Jun 8, 2020

Really Awesome

创建者 Gerwin N B

Aug 18, 2020

Great course!

创建者 Ng T C

Jan 27, 2019

Good learning

创建者 Mario L

May 6, 2018


创建者 ellen w

Aug 6, 2017


创建者 Pablo A L

Feb 8, 2016


创建者 Flt L G R

Jul 22, 2020


创建者 DR. S T C

Jul 14, 2020


创建者 Mohammad S H S

Jun 19, 2020

Thank you

创建者 Wladimir R

Sep 30, 2018


创建者 Ahmed T

Apr 24, 2017


创建者 Reiner P

May 30, 2020


创建者 David C

May 7, 2020


创建者 Chander W

Nov 10, 2019


创建者 Hector R C C

Mar 17, 2019


创建者 Bauyrzhan S

Jun 13, 2018


创建者 Mathew G

Aug 16, 2020


创建者 DR. M E

Apr 27, 2020


创建者 ALAA A A

Jan 11, 2018


创建者 Dr V K S G

Jul 21, 2020


创建者 Augustina R

Dec 29, 2016

Some of the material here was repeated from other courses but overall I felt this was my favorite course in the series. I particularly appreciated the real life examples of what can go wrong with data collection and suggestions/best practices for how to handle that. It gave me a lot of ideas for how to deal with some uncertainties I was facing in some of my own research.

创建者 Clifton d L

Dec 6, 2017

Great that the messy reality is acknowledged and not only the perfect theoretical data science is explained, but also the things that usually go wrong (and how to mitigate these issues).

Some of the quiz with "check multiple answers" didn't seem clear to me / I found opinionated.

创建者 Suman C

Mar 4, 2018

Expected few more real life examples and hope to see some basics of Formal modelling. Found myself lacking in understanding the formal modelling concepts and how to arrive at the formulas.

Other than that the course helped me to get started in Data Science.