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Learner Reviews & Feedback for Big Data Modeling and Management Systems by University of California San Diego

4.4
stars
2,671 ratings
442 reviews

About the Course

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+....

Top reviews

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)

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401 - 425 of 434 Reviews for Big Data Modeling and Management Systems

By Erik P

Oct 03, 2017

I think some real polish needs to be applied to the final assignment in this course. Some of the questions are not formatted well for the coursera web site.. and even on my macbook pro retina I am seeing text overrunning the text box. Really want to see UCSD represented better

By Nimal J K

Jan 10, 2017

The course content was not very appealing. Explanations were not that engaging. What I really missed was the actual practical aspect where you don't work with command prompt but an much more user friendly interface that is more up to date with current standards.

By Stanislav D

Jan 24, 2019

Final task has numerous problems ranging from Coursera site formatting limitations that have not been accounted for to lack of master-answer to peers (e.g. peers unfamiliar with industry can't grade what they don' know)

By Robert H

Sep 09, 2017

Tedious exercises through VM where instructions oftentimes do not work out of the box. It is a hassle to download the slides in small sets and their design awful. Definitely one of the worse courses I have taken.

By Izabelle A

Apr 11, 2019

Cannot finish Twitter activities as commands shown in the video apparently don´t work with the most recent pyhton version. I am very disappointed about it, since I am mostly focused on Twitter-related data.

By David S

Feb 03, 2017

I don't feel this course is always very clear. I Feel that I usually am missing the big picture. I follow it in details, but the pace does not match in terms of how you can view the big picture of it.

By Francisco J

Aug 06, 2017

Lectures are not really useful for real examples. Indeed final task related to the graphs is not explained in the lectures about how to declare properties for nodes/edges in a graph.

By Floyd C

Dec 31, 2018

Compared to the first course this one is not as good, especially Week 5. You can see lots of people complaining about week 5 which does not make sense to the students at all.

By Abdulrahman A T

Jul 20, 2020

The course does not add much knowledge if you came from computer science field. It touches the very basics. The exercises are simple, and the final project is very vague.

By akhil r k

Oct 24, 2016

I wish the courses are more project oriented. This is a very good introduction but, atleast you could provide some optional projects or some tasks.

By JOHN G

May 16, 2020

Slides were minimally helpful, lectures did not track well with quizzes, assignments were poorly designed and difficult to imput into the tool.

By Guillaume V

Jun 16, 2017

Disappointing course. Poor language level, many mistakes (grammar, words, in examples shown). Poorly explained and confusing final assignment

By Andrew C

Dec 11, 2018

Level of content between week four and five is vastly gapped. Too big of a jump. No explanation of BDMS and DBMS in between

By Kaddoum R

Nov 16, 2016

Too high level. Last assignment too ambiguous, peer assessment was completely random and not reliable to pass the course.

By Enric P C

Feb 08, 2019

I have found this course less attractive to follow than other Big Data courses

By Riccardo P

Apr 20, 2018

Barely an introduction, it could be somehow merged with the course #1

By VAIBHAV

May 25, 2020

some concepts are too concisely explained to get on clearly

By Lucas A H

Jan 19, 2019

Too simple for a persona that's already in the BI industry

By James K

Feb 11, 2017

Too simple, no programming, just theory.

By Irfan S

Sep 26, 2017

Very basic and lack real time

By Joaquim P

Jun 11, 2019

Too simple.

By Leslie X

Jun 23, 2016

hard to follow not because it is difficult, but the lecture is only slides, texts, reading slides, very boring and not so many hand-on instruction. only thing i remember is the instructor's face after finish this class. Dont know why you add this into such a good specialist.

By Robert P

Sep 25, 2016

Poorly designed assignment on data modeling did little to expand my knowledge on the topic. Which is a shame since the individual lectures were well done and very interesting. The "Pink Flamingo" peer-peer-reveiwed exercise needs to go.

By Kjell L

Sep 12, 2016

The last peer review is really hard to do. Hard is because the wording is very ambiguous and not all understand how to review. There was a guy who answered with SQL query. This is hardcore since we have not learned that yet...

By John F

Aug 11, 2020

Maybe this course would have made more sense at the end of the specialization. But here it just seems like an unnecessary spike in difficulty to understand (not necessarily difficulty to pass) due to the poor lecturing style.