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Opiniones y comentarios de aprendices correspondientes a Managing Data Analysis por parte de Universidad Johns Hopkins

4.6
estrellas
2,808 calificaciones
388 reseña

Acerca del Curso

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. 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 basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD...
Aspectos destacados
Helpful quizzes
(3 reseñas)
Well-organized content
(24 reseñas)

Principales reseñas

EL

Mar 01, 2017

A long course compared to others in the specialization, but a lot of great material. Very well presented, the instructors know how to present this material and make it easy to grasp and understand.

ST

Nov 23, 2016

The course is full of the cases and the real life examples coupled with the theory background. Its very simple to understand and the course will definitely be of an value for people looking for

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251 - 275 de 384 revisiones para Managing Data Analysis

por Bauyrzhan S

Jun 12, 2018

Good!

por Mona A A

Jul 24, 2020

good

por Dhiraj K

Aug 20, 2019

g

o

o

d

por ALAA A A

Jan 11, 2018

good

por Manas K K

Dec 31, 2017

V

por Dristy C

Oct 07, 2017

C

por Kevin M

Mar 25, 2020

Solid process overview of managing a data analysis project.

Overall a straightforward course and the length/depth is appropriate for the course objectives.

Some of the material is entry level management and experienced managers can judge how best to consume the course

The course does not directly cover supervised / unsupervised learning but refers to association and prediction. There is no mention of cross-validation data sets, F1, precision, or recall as "measures" for evaluating the formal models.

The EDA section could be bolstered by mentioning feature scaling as part of the exploratory data analysis. There is no direct mention of cluster analysis, k-means, PCA, or similar tools that may be applicable to EDA.

por Neil I

Jun 09, 2020

Good course if you have some knowledge of data analysis and an interest in the area. On completing I felt more confident about my abilities, in my ability to work with data scientists, as well as critiquing some past projects and realising how I might have improved them. (I also now realise and can explain why recommendation algorithms are so annoying, which is perhaps more important.)

por Christopher L

May 01, 2018

Pretty good, but I would have liked more math. I understand that others would not, but many times one equation can cut through 3-4 paragraphs and be more clear than the text. It can be frustrating knowing that if you just had the equation things would be 100% clear, but with just a bunch of text, you just get a vague idea, for more work, ie reading time.

por Abhishek S

Sep 18, 2017

The course was good but as a suggestion, walkthrough of an example for the modelling would have helped. I was little confused when the equation was used during the course to explain the confounder, predictor and outcome. Instead of using X, Y, Z - may be use an actual example and show they all relate would have made the course

por Christos G

Aug 29, 2017

Very interesting insights and ideas about how to manage Data Analysis, especially the part with the communication. I think there could be some more emphasis on the troubleshooting side, as it overall appeared to be a finite, engineering process which can always end successfully if the instructions are followed closely.

por Triste R S

Feb 08, 2017

It was very informative. The instructor needs to slow down just a little though, I could tell he's a little nervous speaking to "large groups". Otherwise, it was great. I'm from BAWA and I am familiar with and love JHU, so I support any great course coming from there.

por Jens P

Apr 11, 2016

Excellent focus on what makes managing Data Analysis teams different from managing managing other teams. This course has the most impact if combined with general management background/classes. Speech fillers, like "um," "ah," "like," etc. prove distracting at times.

por Carsten K

Jun 04, 2020

Great course and overview of the processes involved. But it would be great if the readings were also put into videos (I'm doing an online course - if I wanted a book, I would buy it). And please (!!) get rid of the music in some of the videos, as it just distracts.

por JOSEPH A

Apr 09, 2018

Brilliant course - fantastic overview. What's lacking for 5* is the EDA exercise in R should've been within an R IDE to enable total beginners to get more hands on. After all EDA is mostly about DOING. Hope course designers fix this for the next iteration.

por Cristian F

Nov 12, 2017

The topics in the course shows that there is a set of steps to counduct a data science project since the definition of the question to solve to the apropiate way to communicate the results. The content of some videos could be considered technical.

por serge a

Dec 21, 2017

Very valuable, however, in particular the section on inference vs prediction included material not explained before and hard to follow. Also examples with t-values and interpretation of values when adding confounders was difficult to grasp.

por Rorie D

Apr 18, 2016

Liked how a lot of content was covered in a small amount of time. Thought the instructor was effective in presenting the important elements of the lectures. Only issue is some typos in the quizzes that were a bit distracting.

por Sandro A

Nov 08, 2017

Great course with short digestible lessons, great lecturer with an ability to communicate technical details in a very engaging manner, and my appreciation of data analysis is better. Am glad I took this course.

por Janusz Z

Oct 04, 2015

It was a great experience a big amount of knowledge in a short presentation. Overall the summary papers are great, however I missed a more interactive videos with more bullets points and etc.

Thank you!

por Benjamin T C

Dec 31, 2019

Like other reviewers said, this course is larger than the two previous courses. The content is excellent but I am giving 4/5 stars because I found many misspelled words throughout the course lectures.

por RANJITH D

Apr 07, 2019

This course is not for a person without any idea of Data analysis or Statistics. This course isn't for beginners. The content could have been presented even better with lucid examples.

por Vipul G

Jul 26, 2017

Good overview of the process. Helped me in bridging data analysis processes with things that I already do as part of project management or business analytics/decision support projects.

por jose a z r

Nov 11, 2015

Critical thinking is essential at the moment of working over data. This was a nice course with a very good theory about all the process,which a data analyst has to perform.

por Kevin C

Oct 31, 2017

Well defined strategies for getting a handle on the data analysis process. Short and concise class that hit on relevant points required to be successful in this area.