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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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226 - 250 sur 280 Avis pour Data Science in Real Life

par SATISH R

7 juin 2017

Great

par Luis A S E

15 mars 2021

Good

par Parag

7 févr. 2021

.

par David T

14 nov. 2016

Some good tips, nothing terribly new for those who have had a course in statistics. Materials made easy to digest. The variety from the 3 instructors was nice. Missed opportunity: to combine the best aspects from each. The course notes were either excerpts from R.Peng's books /blogs (good) or automated transcripts (complete with typical AI typos... "wait" instead of "weight"). Some lectures were repetitive from one course to another. Slides with examples were useful, slides with clip-art and comic stips less so. Tries to be something for everyone. Would be better to aim either at former DS analysts aspiring to be managers or seasoned managers trying to better understand DS.

par Ruben S

17 août 2016

Brian tries to achieve too much in too little time. It addresses important issues and it gives a good overview, including some hidden gems (Machine Learning vs Stats, for example), but it feels mostly too rushed and superficial for my taste/expectations, and it fails to connect to my previous knowledge (and I have a PhD in Maths, although no strong Stats background), hence little added value for me when I cannot relate to what is being discussed.

par Rajeev R

7 déc. 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.

par Gonzalo G A

16 déc. 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.

par Cauri J

4 juil. 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.

par Michail C

17 juil. 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.

par Daniel C d F

5 déc. 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.

par Peter L

14 août 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.

par Astolfo

5 juil. 2020

It was good, but the content is harder to understand in this course.

I would prefer a similar format and emphasis as the other two last courses.

par Sean H

24 nov. 2015

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

par Chong K M

18 mars 2018

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

par Jean-Michel M

22 févr. 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.

par PAVITHRA.T

28 juil. 2020

First of all it's too tough to understand but day by day I understood something I got it ..tq.it is very helpful for my studies

par Rong-Rong C

14 déc. 2017

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

par Alberto M B

20 mars 2019

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

par Marco A P

2 janv. 2017

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

par Giovany G

15 juil. 2020

I would prefer that the examples be expressed with statistical and mathematical calculations

par Gilson F

2 août 2019

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

par emilio z

6 juin 2017

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

par Christopher L

3 mai 2018

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

par Ioannis L

9 avr. 2017

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

par Patricia S

2 janv. 2020

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