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學生對 加州大学圣地亚哥分校 提供的 Big Data Modeling and Management Systems 的評價和反饋

4.4
2,207 個評分
364 個審閱

課程概述

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+....

熱門審閱

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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126 - Big Data Modeling and Management Systems 的 150 個評論(共 353 個)

創建者 Mohammad M

Sep 30, 2019

Big Data modeling is important consideration while designing big data solution. Most of other online courses lacks this module. Thanks to coursera for bringing this course. Five star.

創建者 Novan Z

Aug 26, 2019

Easy and understandable

創建者 Ali S

Sep 21, 2019

The course was great I learned basics of different big data visualizations. Sources of big data and different tools introduced which can be very helpful. The quizes and assignments helped me to better understand the course.

創建者 Luis R G

Dec 05, 2019

Great course to learn and also practice how data can be visualized and how to model data, formats and how important is to choose appropriate data format and model

創建者 Chenyang A

Feb 17, 2018

Wonderful course

創建者 Md S F

Aug 20, 2019

best big data course

創建者 Vikas K S

Sep 28, 2018

I liked the course. Everything was good at its part.

創建者 Hector d J G C

Oct 23, 2016

it was a little too slow but is ok, specially with beginners as me.it can be a little bit more interactive or didactical.

創建者 Francisco S H

Jun 18, 2019

Very good course, specially the last homework. Thanks.

創建者 Jose L R

Oct 13, 2018

Great course! Please update the hands on!

創建者 Duc D

Dec 10, 2018

Good content!

創建者 Juan H B S

May 26, 2018

Difficult topic, but interesting and the explanations were clear.

創建者 Saurabh K

May 26, 2019

good

創建者 Vikas G

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)

創建者 Ferry W

Jul 02, 2018

easy to understand and helpful

創建者 Misty S

Aug 28, 2019

While the course content is very good, I feel this is not for beginners as one needs a basic knowledge of traditional relational databases for starters. The assignment helps clear the concepts and makes it understandable enough to work on it.

創建者 Andrés F

Oct 10, 2016

Good to understand Big Data.

創建者 Panagiotis T

Jan 16, 2019

Very useful, although some concepts (especially week 5) are a bit condensed and hard to remember without further practice.

創建者 Samir G

Nov 21, 2016

Very helpful

創建者 Santiago E L

Jan 04, 2018

You guys are rock stars

創建者 Sita R P G

Oct 24, 2016

Modeling is the start and gives an insight how to choose the correct model for a big data project. The correct choice of the model spells success for the project. Many data models were introduced and the hands-on exercises were helpful in better understanding of the concepts.

創建者 Aldo F B

Apr 03, 2017

I've learned a lot of things about data models and new ways to process the information. I want to learn new knowledge of big data because I want to apply in my professional life

創建者 JAMSHAID

Dec 28, 2017

Great course to get you started in the world of Big Data

創建者 Srinivas B

Sep 26, 2016

Excellent

創建者 Routhana L

Apr 10, 2019

As a undergraduate data analytics student, this course was an enlightening experience that complemented my more theoretical, less-applicational on campus course very well.