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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
estrelas
2,313 classificações
278 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

19 de ago de 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

11 de nov de 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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151 — 175 de 280 Avaliações para o Ciência de Dados na Vida Real

por DR. S T C

14 de jul de 2020

Excellent

por Mohammad S H S

19 de jun de 2020

Thank you

por Wladimir R

30 de set de 2018

Excellent

por Ahmed T

24 de abr de 2017

Excellent

por Reiner P

30 de mai de 2020

Perfect

por David C

7 de mai de 2020

awesome

por Chander W

10 de nov de 2019

Amazing

por Hector R C C

17 de mar de 2019

thanks!

por Bauyrzhan S

13 de jun de 2018

Perfect

por HOSSAM E D M S A

2 de dez de 2020

Thanks

por Mathew G

16 de ago de 2020

great

por DR. M E

27 de abr de 2020

good

por ALAA A A

11 de jan de 2018

good

por Dr V G

21 de jul de 2020

OK

por Augustina R

29 de dez de 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.

por Clifton d L

6 de dez de 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.

por suman c

4 de mar de 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.

por Keuntae K

25 de mar de 2018

This is a good course, overall. Maybe providing more general examples related to the topics of the course makes this course much more useful and helpful for people who do not have any backgrounds of brain or neural systems in medical science like me.

por Humna A

30 de out de 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 Juan F D T

10 de mai de 2020

Brian makes a terrific job trying to explain in simple terms what a real life data science effort takes. Sometimes it was a little hard to understand because of how the instructor spoke but nothign hat a rewind and replay wouldn't fix.

por Neil N

17 de fev de 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 Scott K

10 de out de 2015

I really enjoyed the comparison of what is ideal vs. what actually happens when it comes to data science. This was a very practical course and gave insight into what to expect from data science and analysis.

por Sam L

3 de set de 2020

One more thing to add. The link to Study.com works. But Study.com wouldn't let me watch the whole video anyway; it requires registering and subscribing (after trial period is over). Not useful.

por Akshay k

19 de abr de 2020

It was kind of hard to understand as I did not have any professional experience in data science. But, I am sure I can work in a professional environment now with the teachings of the professor.

por Manish J

19 de jan de 2021

Gives directions on how to deal with a situation where a clear conclusion may not be forthcoming from the analysis--- a situation that more often than not is likely to hold true in real world