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Avis et commentaires pour d'étudiants pour Data Science in Real Life par Université Johns-Hopkins

4.4
étoiles
2,300 évaluations
278 avis

À propos du cours

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...
Points forts
Statistics review
(44 avis)

Meilleurs avis

SM
19 août 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 nov. 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 sur 280 Avis pour Data Science in Real Life

par DR. S T C

14 juil. 2020

Excellent

par Mohammad S H S

19 juin 2020

Thank you

par Wladimir R

30 sept. 2018

Excellent

par Ahmed T

24 avr. 2017

Excellent

par Reiner P

30 mai 2020

Perfect

par David C

7 mai 2020

awesome

par Chander W

10 nov. 2019

Amazing

par Hector R C C

17 mars 2019

thanks!

par Bauyrzhan S

13 juin 2018

Perfect

par HOSSAM E D M S A

2 déc. 2020

Thanks

par Mathew G

16 août 2020

great

par DR. M E

27 avr. 2020

good

par ALAA A A

11 janv. 2018

good

par Dr V G

21 juil. 2020

OK

par Augustina R

29 déc. 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.

par Clifton d L

6 déc. 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.

par suman c

4 mars 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.

par Keuntae K

25 mars 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.

par Humna A

30 oct. 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

par Juan F D T

10 mai 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.

par Neil N

17 févr. 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

par Scott K

10 oct. 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.

par Sam L

3 sept. 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.

par Akshay k

19 avr. 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.

par Manish J

19 janv. 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