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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

2,314 个评分
278 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.


201 - 生活中的数据科学 的 225 个评论(共 280 个)


Oct 19, 2016

good course, but focus more on inferential analysis than predictive analysis

创建者 Gustavo V

Apr 13, 2019

Help me understand what can I expect from a real data science project.

创建者 Deepak G

Jun 28, 2016

Quality of this course is better than the rest of the specialization.

创建者 Sangeeta N

Feb 21, 2021

This gives the basics of Data Science that one needs to lead a team

创建者 Chris C

Nov 22, 2017

A little difficult overall but had some key points to take away.

创建者 Jomo C

Jan 28, 2018

Good course, Longer than expected. Very satisfying at the end

创建者 Rorie D

Apr 20, 2016

great approach, thanks. A few typos, but otherwise great.

创建者 Navneet W

Sep 10, 2020

On of the best courses of Data science on Coursera.

创建者 Brian N

Apr 10, 2018

Good for introduction in Data Science Process

创建者 Paul C

Nov 4, 2016

A solid course with lots of practical advice.

创建者 Paulose B

Oct 31, 2016

Short session need more handson excercise


Jan 22, 2020

Good course with vibrant instructors.


Jan 7, 2017

More real world examples are required

创建者 Hubertus H

Jan 27, 2017

Good summary on experimental design.

创建者 Nachum S

Jul 13, 2018

Good, a bit long for the material.

创建者 Setia B

Dec 6, 2017

I really enjoyed the course :)

创建者 Jeffery T

Nov 30, 2017

Good course for managers

创建者 Angel S

Jan 17, 2016

Pretty useful course

创建者 Venuprasad R

Jan 5, 2016

Very practical views

创建者 Rui R

Jun 18, 2017

Too much theory ...


Jun 20, 2020

Nice course 👍

创建者 Deepa F P

Sep 5, 2017

Good content


Aug 5, 2020

Nice course

创建者 R.K.Suriyakumar

Jun 7, 2020

its good

创建者 ECE- R G

Jul 13, 2020