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Comentários e feedback de alunos de Ciência de Dados na Vida Real da instituição Universidade Johns Hopkins

4.4
estrelas
1,672 classificações
190 avaliações

Sobre o curso

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...
Destaques
Statistics review
(44 avaliações)

Melhores avaliações

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.

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176 — 189 de {totalReviews} Avaliações para o Ciência de Dados na Vida Real

por 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.

por Aline N O

Jul 17, 2019

This course for me was the most difficult to understand. Using as example situations with health area was hard to understand how I can apply in my case. But in general, the other courses were very nice for me.

por Jean-Gabriel P

Aug 10, 2017

OK content but delivery could be better. Also poor value for money (you pay 49$ for a course you can finish in a few days) versus other Coursera courses that get you much more bang for your buck.

por Karun T

Feb 28, 2017

The content was redundant at times, at other the dots that were trying to be connected were to wide apart on the spectrum

por Marcelo H G

Jul 29, 2017

It is good but demands statistics and some knowledge in research area.

por Julià D A

Jun 13, 2017

Too qualitative, I would had liked some hands-on examples.

por Shafeeq S

Jan 08, 2019

Not that engaging content.Too much theoretical approach.

por Peter P

Jun 20, 2016

Too much focus on technicalities - not management based.

por Hiteshwar G

Jan 05, 2018

The content and examples seem irrelevant.

por Varun M

Sep 19, 2016

very boring videos.

por GIacomo V

Feb 28, 2016

The course tests are at times partially unrelated to the content of the lessons. In the test of Lesson 7 we are asked if removing jargon from an analysis makes the analysis clearer. This is never mentioned in the course.

The question does not have a unique yes/no solution. It depends on the context, in particular on the audience of the analysis and report. If I'm talking to technical people who knows a lot about the topic jargon can be useful, on the other hand if jargon is not documented it can be confusing.

How are we supposed to know this?

This is just one example, but all the courses of the EDS specialisation had these issues. I don't know if it is a language barrier or what but I feel that I didn't have a chance to study more to get a better score. You either happen to have the same idea of the teacher or you don't, and this is not professional.

por Deleted A

Aug 10, 2016

Was expecting soo much more from the entire courses. Not a single practical part, soo much talk and write.

Sorry would not share the course with friends, 190€ is too much for what I have just learned.

por yassine a

Nov 03, 2015

very bad and not organised

por Seyyed M A D

Apr 19, 2018

Thanks