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Вернуться к Managing Data Analysis

Отзывы учащихся о курсе Managing Data Analysis от партнера Университет Джонса Хопкинса

Оценки: 2,872
Рецензии: 402

О курсе

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
Основные моменты
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Лучшие рецензии


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.


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 из 397 отзывов о курсе Managing Data Analysis

автор: Wallace O

Mar 27, 2017

I liked it

автор: NAVIN B

Oct 21, 2016


автор: Katarzyna P

Dec 08, 2015


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Aug 24, 2020


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Oct 30, 2018


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Jul 07, 2018


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Dec 01, 2017


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May 07, 2017


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Jul 24, 2020


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Aug 20, 2019





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Jan 11, 2018


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Dec 31, 2017


автор: Dristy C

Oct 07, 2017


автор: 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.

автор: 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.)

автор: 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.

автор: 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

автор: 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.

автор: 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.