Chevron Left
Zurück zu Data Science in Real Life

Kursteilnehmer-Bewertung und -Feedback für Data Science in Real Life von Johns Hopkins University

4.4
Sterne
2,061 Bewertungen
248 Bewertungen

Über den Kurs

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...
Highlights
Statistics review
(44 Bewertungen)

Top-Bewertungen

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.

Filtern nach:

201 - 225 von 247 Bewertungen für Data Science in Real Life

von Cauri J

Jul 04, 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.

von Michail C

Jul 17, 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.

von Daniel C d F

Dec 06, 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.

von Peter L

Aug 14, 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.

von Astolfo

Jul 05, 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.

von Sean H

Nov 24, 2015

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

von Chong K M

Mar 18, 2018

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

von 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.

von PAVITHRA.T

Jul 28, 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

von Rong-Rong C

Dec 14, 2017

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

von 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.

von Marco A P

Jan 03, 2017

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

von Giovany G J

Jul 15, 2020

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

von Gilson F

Aug 02, 2019

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

von emilio z

Jun 06, 2017

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

von Christopher L

May 03, 2018

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

von Ioannis L

Apr 09, 2017

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

von Patricia S

Jan 02, 2020

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

von Gowtham V

May 03, 2020

Would like to have simpler examples to understand some of the concepts.

von Amal L C

Mar 16, 2017

It was quite hard with all the statistical jargon. Too much theory.

von Poon F

Jan 30, 2018

This class has more useful materials than previous ones.

von Manas B

May 11, 2016

Relevant materials, but lecture delivery is rather dry,

von Matej K

May 01, 2018

Sometimes it was hard to understand what's going on.

von Angelina

Apr 02, 2019

The material is too long and boring.

von Weihua W

Jan 19, 2016

Too short, too expensive.