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Avis et commentaires pour d'étudiants pour Information Visualization: Foundations par New York University

4.5
étoiles
129 évaluations
34 avis

À propos du cours

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on information visualization and to design and develop advanced applications for visual data analysis. This course aims at introducing fundamental knowledge for information visualization. The main goal is to provide the students with the necessary “vocabulary” to describe visualizations in a way that helps them reason about what designs are appropriate for a given problem. This module also gives a broad overview of the field of visualization, introducing its goals, methods and applications. A learner with some or no previous knowledge in Information Visualization will get a sense of what visualization is, what it is for and in how many different situations it can be applied; will practice to describe data in a way that is useful for visualization design; will familiarize with fundamental charts to talk about the concept of visual encoding and decoding....

Meilleurs avis

EM

Jun 24, 2019

Great intro to basics about information visualization. Would be a bit better if after doing the assessments the correct answers were provided as a guide to the peer review.

RH

Oct 29, 2018

Great course! Not too complicated and covers fundamental topics about visual encoding and decoding.\n\nThe explanation is clear and concise.\n\nThank you Professor Enrico!

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26 - 34 sur 34 Avis pour Information Visualization: Foundations

par Halian V

Dec 31, 2019

Great lectures, Enrico is a fantastic teacher and the depth of information covered is very well measured! Some of the in-video exercises are, however, very obvious. We could use better crafted questions that summarize the content instead of some simple "word" games in which you must remember exact terms. Peer review quizes are nice, but the "distribution" dynamics could be better, I felt that my exercises would never be reviewed haven't I asked for it in the course forums. Despite that, very nice course!

par Vishnu S

Jun 24, 2019

The course is well taught at a good pace. The assignments are fun and tough in a sense, it makes you work. The only bad thing is the PEER REVIEW system. You'll have to spend some time waiting for peer reviews to happen.

par Swaminathan J

Jan 22, 2020

Some of the quizzes had wrong answers/ different answers to what was taught in the course. They have been mentioned multiple times by many in Course discussions but still seems to have been not fixed.

par Luisa B L

May 23, 2020

This is a course that presents the basic concepts needed to start working with information visualization.

I found it excellent, orderly, clear and concise.

par Stavros D S

Aug 17, 2020

i think it describes in a comprehensive way the foundations of data vis

par Carl C

Apr 17, 2019

Very good foundation into data visualization.

par Jonathan D

May 11, 2020

The course materials are generally useful. the videos are reasonably well explained so that is positive.

The main concern I have about the course is the peer review in weeks 3 and 4. My concern is as follows: Students are relying on other students interpretation of the course materials to provide an assessment of other students work. For example, a student who has little understanding of the course materials can provide poor feedback to other students out of ignorance.

Furthermore, a student may submit a poor assignment and may receive a passing grade. Is there a lack of oversight on the side of the course administrators.

If successive peer-reviewed submissions are required it can take many days for a students work to be graded depending on the number of course attendees.

The course should be graded by an expert rather than peer-reviewed for those two reasons.

par Joseph L P

Jul 18, 2020

The course needs to include topics about data validation, the use of independent (x) and dependent variables (y) for preparing graphs, and limiting the combination of data attributed to avoid too much granularization of data visualizations (to ensure effectiveness). Thank you very much!!!

par Anirudh

May 10, 2020

Discussion Forums talk about quiz questions being broken and delayed peer feedback