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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

4.4
1,655 个评分
189 条评论

课程概述

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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Statistics review
(44 条评论)

热门审阅

SM

Aug 20, 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.

ES

Nov 12, 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.

筛选依据:

151 - 生活中的数据科学 的 175 个评论(共 188 个)

创建者 Rajeev R

Dec 07, 2015

Lectures themselves were OK, but presentation needs work. Intro session was very repetitive. Lot of jargon introduced without explanation. Pop-ups w text showed up but disappeared before I was able to finish reading them. Best part of course was actually the text notes at the beginning of each sesssion. A minor nitpick: course description suggests that there are 3 instructors presenting, but I only saw one.

创建者 Gonzalo G A

Dec 16, 2016

It's sometimes difficult to follow professors beacuse they take for granted information about the examples they use that is not evident for the learners. They should take a minute to explain a little bit more what the examples consist of and what are the charts they show. As it happens when Brian Caffo explains the blocking adjustments part.

创建者 Cauri J

Jul 04, 2017

I found this course used a lot of jargon without explanation. It seems like the instructor understands the content so well that he assumes a level of knowledge from students that do not match the expectations of the rest of the content in this track. At the same time I found the content well presented.

创建者 Michail C

Jul 17, 2019

This course is an excellent effort to document the issues faced in real-life data science. However, the flow of the videos seems to be a bit confusing and some of the content is explained in a weird manner.

创建者 Daniel C d F

Dec 06, 2016

I missed several concepts to better understand some of the discussions and explanations. It was valid, but I think the statistics background should be better explored.

创建者 Peter L

Aug 14, 2018

The course is valuable but highly focussed on scientific applications (inference) and less on business application (i.e. prediction). I hoped for a more even mix.

创建者 Sean H

Nov 24, 2015

The video quality and content were good. Unfortunately, there were a lot of spelling errors and grammatical mistakes in the written portions.

创建者 Chong K M

Mar 18, 2018

Very difficult and time consuming course which contains a lot of technical words and jargon. Not recommended for the average beginner.

创建者 Jean-Michel M

Feb 22, 2019

I would drop some of the cartoons. They are funny but they seem to distract Bryan and overall it's distracting for us students too.

创建者 Rong-Rong C

Dec 14, 2017

There is a lot of technical jargon covered which made the course more challenging than the other courses in the series.

创建者 Alberto M B

Mar 20, 2019

It wasn't as focus on Managing Data Scientists as I was expecting, but rather focus on tips for Data Scientist.

创建者 Marco A P

Jan 03, 2017

Much theorical with few examples. Could incorporate examples outside the health world as well.

创建者 Gilson F

Aug 02, 2019

Não gostei muito da didatica do instrutor e os slides não ajudam no entendimento

创建者 emilio z

Jun 06, 2017

Explanations in videos qere not very clear nor very well connecetd with the Quiz

创建者 Christopher L

May 03, 2018

Would have liked a bit more examples and math in some cases. Others were fine.

创建者 Ioannis L

Apr 09, 2017

A bit less engaging than the other parts of the Executive Data Science course.

创建者 Patricia S

Jan 02, 2020

good content but could be simplified and presented in a more focused man

创建者 Amal L C

Mar 16, 2017

It was quite hard with all the statistical jargon. Too much theory.

创建者 Poon F

Jan 30, 2018

This class has more useful materials than previous ones.

创建者 Manas B

May 11, 2016

Relevant materials, but lecture delivery is rather dry,

创建者 Matej K

May 01, 2018

Sometimes it was hard to understand what's going on.

创建者 Angelina

Apr 02, 2019

The material is too long and boring.

创建者 Weihua W

Jan 19, 2016

Too short, too expensive.

创建者 Yuvaraj B

Dec 26, 2017

Very Good Content

创建者 Jason C

Nov 06, 2018

I found this course to be notably worse than all of the others in the series. There is very very little practical content provided within the lectures. Way too many summaries or over-views of what's to come next without really getting into the nuances of what is discussed as a course topic. Way too much repetition of the exact same content, there is even repetition of content in this course that was presented in another one of the courses in the series. Many of the examples are purely meant as a comedic aside rather than actually functioning to discuss the topic with depth. E.g. - talking about statistical modeling and putting up a picture of Ben Stiller from Zoolander - then keeping the picture up there for the entire explanation. There's literally a Nic Cage example provided for the confounding factor lecture only for the instructor to say directly after "This isn't actually the best example" - then proceeds to not explain why it was brought up aside from mentioning there's a spurious correlation. Way too much repetition of similar examples - showing photos of a muscular v. skinny Christian Bale. This pop-culturey reference isn't needed in the first place and doesn't need to be shown in triplicate. I don't mind repetition if there is additional nuance or content provided through them, but that isn't the case in this course. I find there is too much focus on side tangents, where the instructor seems to change thoughts mid-sentence but forgets to come back to the original idea. I think that every single video could be cut down by 25%, purely by being more concise, and should include more nuanced descriptions. I found it particularly odd that instrumental variables were noted as a rather clever technique, yet an explanation was intentionally avoided, however an example was still provided. Bringing up a topic, intentionally refusing to define it, then providing an example directly after just doesn't make sense. I think that more time needs to be spent refining the lectures so that they're designed to teach content. It has the feel of someone who's talking about a field to get people interested in it rather than a practical training course. Many key terms are very poorly defined with examples (on many cases the audience is referred to wikipedia for explanations) in which the basics are repetitively explained while the nuances are glossed over. There seems to be an odd theme where summaries and over-generalizations are far too frequent and yet the key terms and how they relate to examples are an afterthought. I don't think the summaries are necessary given the fact that users can literally re-watch every single video and there isn't enough total content to justify a summary in the first place. Additionally, this course also seems to deviate from the others in that there is an assumption that the student has a heavy amount of programming experience already built in (or that's my assumption since many of the term explanations aren't discussed too heavily). Prior lectures break down the basics more and indicate that potential managers should pursue the data specialization courses.