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Voltar para Ciência de Dados na Vida Real

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

4.4
1,494 classificações
175 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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1 — 25 de {totalReviews} Avaliações para o Ciência de Dados na Vida Real

por Liping L

Jan 29, 2019

This is a recommended course where you understand what are the requirements for a data engineer, data manager and data scientis

por Mauricio L

Jun 22, 2019

Great course. It delivers a fantastic framework to assess the process of successful Data Science.

por jose c

Jun 11, 2019

Claridad del contenido entregado del curso

por Yonathan M P

Jun 08, 2019

Great course!!!!! Tons of useful insights!

por Omid F

May 10, 2019

Thank you very much for your excellent course.

Best Regards

Omid Faseli

por Georgios P

May 06, 2019

Very good introductory topics!

por Priyanka F P

Apr 14, 2019

Excellent technical information!

por Gustavo V

Apr 14, 2019

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

por pietbartolo

Apr 12, 2019

Very useful course! I really enjoyed the technical not so much the statistical part of the course.

por Angelina

Apr 02, 2019

The material is too long and boring.

por 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.

por Jason G

Mar 18, 2019

Very informative and a good introduction into the aspects faced while doing Data Science!

por Hector R C C

Mar 17, 2019

thanks!

por BAHAR A

Feb 24, 2019

It is a helpful course about a statistical area. I recommend it.

por 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.

por Neil N

Feb 17, 2019

Good overview of the reality of the challenges in data science. A glaring miss from my perspective was any real focus on the challenges of ML/AI based analysis. This module was really focused on traditional statistical modeling

por Ng T C

Jan 27, 2019

Good learning

por Alzum S M

Jan 20, 2019

It's an excellent course. I have learnt a lot.

por Shafeeq S

Jan 08, 2019

Not that engaging content.Too much theoretical approach.

por Kim K R

Dec 23, 2018

GREAT COURSE!!!

por Elton K

Dec 14, 2018

Interesting for a Non-Data Science Executive despite some minor spelling errors in video transcripts.

por ELINGUI P U

Nov 08, 2018

Great practice example, great team at Zillow, and that to DataCamp

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 Humna A

Oct 30, 2018

Awesome course! the only negative thing is that all the examples are related to biostatistics. Examples related to other fields like economics, social science, psychology etc should have been included. Besides that it was a great experience

por Emmanuelle M

Oct 10, 2018

Great course, although, if you are not already working or have knowledge in this particular filed/topic, it is challenging.