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Отзывы учащихся о курсе Graph Analytics for Big Data от партнера Калифорнийский университет в Сан-Диего

4.3
звезд
Оценки: 1,120
Рецензии: 214

О курсе

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects....

Лучшие рецензии

KM
16 дек. 2017 г.

Got an amazing introduction to Graph Analytics in Big Data. Technical issues with Neo4J made this course a little more challenging than necessary. But the introduction to Spark GraphX was invaluable.

JT
25 окт. 2016 г.

This course was excellent as an introduction to Graph Analytics and using Neo4j. Not only did I learn a lot, I've been given tasks related to what I've learned in this course after finishing it.

Фильтр по:

126–150 из 213 отзывов о курсе Graph Analytics for Big Data

автор: Anthony W U

22 мар. 2017 г.

Displaying handson queries graphically would make it more appealing as it was in Neo4j

автор: Gabriel T

9 февр. 2018 г.

Very sound course with lots of information to digest. Just enough for "lift-off".

автор: Petch C

21 апр. 2020 г.

hand on is quite hard and need a lot of installation such as cloudera.

автор: Kun Z

10 мая 2017 г.

Very good project! Hopefully it will be helpful in my future career!

автор: Efendi

22 мая 2017 г.

Suggest to remove the peer-grade assessment as it could be bias :)

автор: Anant K

7 февр. 2019 г.

Can be improved further by including rigorous Hands-on exercises

автор: Adam G

21 дек. 2018 г.

Within the usual pedagogical standards, it is a very good course

автор: Prospero-Martin R

31 авг. 2018 г.

I really enjoyed learning graph analytics, great course!

автор: Juan J R M

26 авг. 2017 г.

It's really long and we need more practical examples

автор: Mihai-Bogdan Z

2 сент. 2020 г.

Things made a bit too complicated sometimes.

автор: Marwa K E

6 окт. 2020 г.

Week 5 materials are not well prepared.

автор: Miguel A R S

5 дек. 2017 г.

This is a great introductory course.

автор: Rüdiger S

1 нояб. 2020 г.

Liked the hands-on neo4j part most.

автор: Amir A

9 февр. 2017 г.

Thanks so much

you are great people

автор: Fernando M

30 июня 2016 г.

interesting practices with neo4j

автор: Mehul P

30 дек. 2017 г.

Nice overview to get into it.

автор: Seth D

9 сент. 2016 г.

best course of the series

автор: Congcong Z

5 дек. 2017 г.

well explained

автор: Liliana d C C M

11 нояб. 2019 г.

buen curso

автор: Qian H

31 июля 2017 г.

Not bad

автор: Bahaa E A E

27 июня 2018 г.

Thanks

автор: Rohit K S

13 окт. 2020 г.

Good!

автор: AGARAOLI A

10 февр. 2017 г.

-

автор: Brittany

9 июля 2018 г.

The course theory was illustrated and demonstrated very well. Examples were shown and the lectures were short but concise. I appreciated this greatly. The professor also spoke very slowly and deliberately, so the viewers could understand and have time to let the information sink in. In contrast, although the guest lecturer for Neo4j was great, the material was not up-to-date and caused several issues in completing the assignment. Other students were able to lead the class in the right direction in order to even start the assignment. The GraphX on Cloudera virtual machine was almost impossible to replicate as well due to the material being so outdated. Week 5 of the course peaked my interest but lacked the resources to completely follow the instructions and understand the material that was being presented.

автор: Stephane T

7 мар. 2017 г.

I would have liked to put 5 stars. This topic is so important and relevant to big data. After week 4 hands on part, it became obvious we will not see how to implement or interpret the more abstract graph concepts presented in week 3. That was very disappointing.

Moreover, the structure of the course is not as good as the other module. I don't understand the lack of balance between theory and hands on (Not enough hands on to reflect the theory) part.

On a constructive note, I would replace some of the theoretical concepts of week 3 with additional information on how to link a graph database to machine learning OR I would add more hands on exercise to help using those more complex concepts and learn how to interpret them.