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Big Data Modeling and Management Systems に戻る

カリフォルニア大学サンディエゴ校 による Big Data Modeling and Management Systems の受講者のレビューおよびフィードバック

4.4
2,590件の評価
427件のレビュー

コースについて

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. Through guided hands-on tutorials, you will become familiar with techniques using real-time and semi-structured data examples. Systems and tools discussed include: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. This course provides techniques to extract value from existing untapped data sources and discovering new data sources. At the end of this course, you will be able to: * Recognize different data elements in your own work and in everyday life problems * Explain why your team needs to design a Big Data Infrastructure Plan and Information System Design * Identify the frequent data operations required for various types of data * Select a data model to suit the characteristics of your data * Apply techniques to handle streaming data * Differentiate between a traditional Database Management System and a Big Data Management System * Appreciate why there are so many data management systems * Design a big data information system for an online game company This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+....

人気のレビュー

MP

Oct 17, 2017

Good Explanations of Concepts and Nice Tests. I got a trilling experience in completing the peer Assignments with keen observation and Analyzing of Concepts learned.Thanq for your course very much.

VG

Mar 28, 2017

Nice course to describe the traditional data modeling (RDBMS) as well as various semi-structured and un-structured data modeling and management of the systems (Batch and Streaming data processing)

フィルター:

Big Data Modeling and Management Systems: 351 - 375 / 416 レビュー

by Rahul P

Jul 14, 2019

The practical contents were too less. Too much of theory does tend to make the course a little bit boring...

by Yuzhen

Oct 11, 2016

The content of the course is very good, but the definitions and explanations are not straight forward.

by Alexander H

Dec 27, 2018

could be a bit more specific, especially for students with a classical DBMS background

by Radu G

Dec 21, 2016

The final test has little to do with the course. You must do extra study to respond.

by Jorge F d l V G

Oct 23, 2016

The level of the exercises and quizzes don't reflect the content of the course.

by Congcong Z

Nov 07, 2017

it would be better if there is person to person explanation of homework

by Moses B R H

Jun 19, 2017

Week 6 Peer graded assignment instruction should be more detailed.

by Manik S

Aug 11, 2019

LOW QUALITY VIDEOS, OBSOLETE RESOURCES, VIRTUALLY NO TECH SUPPORT

by Tze M H

Sep 07, 2017

Some concepts are too abstract, find it hard to understand.

by Waldo U

Dec 31, 2017

The last assignment is really vague and poorly written.

by Abu R

Aug 01, 2017

great course

Hands on practice needs to do a better job

by Tushar S

Oct 04, 2016

Just about okay. Wouldn't recommend to pay for this.

by Diwen

Aug 19, 2019

Peer-graded assignment is very badly described.

by Gustavo S

Aug 08, 2016

More practical exercises should be recommended.

by Amit D

Jul 13, 2020

Good Course. More practice sessions are needed

by Gregory S

Sep 29, 2017

Assignments were vague and hard to understand.

by Abhishek V

Jul 31, 2017

Final assignment needed to be more clear

by rkouatly

Aug 06, 2018

The course requires more practical part

by Christine G

Aug 09, 2017

Capstone assignment was disappointing.

by Marco F

Apr 24, 2018

Final assignemet is too hard.

by JUAN A R R

May 11, 2017

poco contenido práctico

by Ra'ed A

May 07, 2019

Fix the mistakes!

by Ruowang Z

Apr 24, 2020

too simplistic

by Barend M

Jul 13, 2017

.

by Hendrik B

Dec 17, 2017

Sorry, but I don't think this is a very good course. Here are some reasons why: The time said to be needed for the course is artificially increased, because there are ten minutes appraised for each set of lecture slides. At the end, there is almost no reading material, which is not obvious when looking at the course at the start. I think this is almost fraud. Ultimately, there are basically only lectures, no other media to learn, except for some multiple-choice quizzes. There are Coursera courses which are way more diverse. Additionally, the lectures are not particularly good (not speaking of the horrible design and colouring). Especially, when talking about some examples for BDMS, it is difficult to follow because some of the concepts have not been explained properly prior to that. The quizzes are not very good, and it is very obvious that there is not much thought behind the answer options. Also, for the quizzes you almost exclusively need to memorize learned stuff, but not to transfer knowledge or to apply knowledge). The final exam was a joke, because there was NO attempt by the supervisors to give students some intuition about the right answers after they submitted. Still, they were expected to rate others’ submissions. Meaning, when you didn't know how to answer a question, you were still expected to rate others submissions. Seriously? In general, so far it feels like the lecturers attempted to make a shallow course for a big topic, meaning big data, in order to get some money (after all: it is expensive to earn a course certificate). There are courses on other topics (e.g. “Game Theory” by Stanford University, where the quizzes are relatively hard but you get a feeling that you learn something, “Improving your statistical inferences” by Eindhoven University, which has many different media to learn, not just lectures, and has exceptionally good quizzes, as well as “Bayesian Statistics: From Concept to Data Analysis” by Santa Cruz University, which has very modern style of presenting the lecture). Sorry, but I think this course needs improvement, especially since the topic is so important.