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+.
MM
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.
BR
It was a difficult module, although trainer tried to convey but seems it is more complex it took time for me to understand the concept and apply the same while doing my assignment.
VC
The course provided me a good understanding of the tools and insights on how data could be modeled and managed. I feel confident that I can use the knowledge at work.
DN
Pretty good overall, although some exercises are a bit difficult to understand from the descriptions and instructions given, some graphs and initial reference documentation for exercises might help
YG
Overall relevant and clear presentations. Course material quite general, but I guess this is expected from an introductory-level course.Peer-reviewed assignment's instructions can be clearer.
RM
Interesting. Sometimes a little bit overwhelmed by a lot of information within a single video but it gives you an overview of what is big data modeling and management systems.
SM
Gives us a very good understanding on Big data modeling and various data models like Graph Data model, Vector Space model (TF-IDF), Vertica, AsterixDB, Aerospike etc.
AG
Lot of new information, excellent delivery. Given 4 as I feel real-use case flavor is inadequate -exercises could be more intensive, real case studies can be added.
CJ
Great overview of a few databases and what they are good at. Would have liked actual hands-on like installing a database and demonstrating the key feature the database is good at.
LR
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
VG
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)
MP
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.
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There is no enough practice, for final exam it is impossible to understand what is right and what is wrong even when making peer-review
Just a basic overview. Not much hands on
Material is very high level with very little practice examples. The attached documentation doesn't really add anything to the course and the quizzes are quick and simple.
No extra readings are provided to deepen the knowledge. Hands on are not maintained as outdate info is presented in the Hands On labs. Twitter HandsOn is not correctly explained.
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.
Sorry to say, but the course's topics are superficially explained. Teacher provided an overwhelming quantity of concept at the speed of light with very few practical examples. The entire course is not very explicative for someone that is not already a subject matter expert (that any case would define this course no more than a quick review). The assignment requirements are unclear and, in my opinion, teacher has not sufficiently explained the concepts required in order to straightforwardly perform it. More over, the assignment requires to use tables and graph, but the learning platform embedded editor does not allow to design this kind of graphical elements.
It is the One of the best courses available for BigData Modelling . Even if the learner is beginner he/she can easily grab the things. I enjoyed this course a lot and got a lot of skills..
informative, descriptive with hands-on experience on updated tools.
The setup.sh file for chapter 1 is not updated. spent a lot of time to google and go thru discussion forum to resolve the setup issues before I am able to continue the course. This wasted a lot of time.
Too much talk and less practice.
It is like you are at university class. If I needed to get bunch of unnecessary information in big data,I could have go to get proper diploma in some university. The purpose of doing online course is to have practical points about big data, not to get all those information which I am going to forget after few days.
I would suggest to change the structure of course, reduce number of examples and talks in videos, and increase more practical things on Big Data field (ex, doing some exercises in cloudera etc.)
The quizzes tested superficial knowledge, and basically just required you to memorize bullet points from the slides. The final project had incredibly confusing instructions, and the discussion forums didn't really clear anything up. The course materials seem to be about 5 years old, and I'm sure a lot has changed in terms of tools and technologies since then.
I've learned some useful information from this series of classes, but it doesn't seem like much effort was put into the content beyond the lecture videos.
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.
Course is interesting but the support is almost null. The image file provided for the VM is not complete, I had to spend several hours trying to install and fix the errors I was getting; thanbkfully some of the issues were fixed by other users in the comments but you need to have some skills with unix environments.
The course is not structured well. I hope if the course provides the skills needed to build noSQL data models such as Columnar - KV - Graphical etc. but the course keeps jumping from notion to notion with no clear and smooth structure.
The lecturer presentation style is terrible.
Coure appearst to be abandoned by instructors (as of 02/2022). Practice material is extremely outdated, relying on a VM with CentOS 6 that can no longer correctly install packages. The exercises appear to have been last updated in 2018 and despite multiple questions on the forums I haven't seen any recent feedback from moderators or instructors.
I feel as though the assessment questions could have been more specific and the assessment criteria when marking could have been more precise. But other than that it was a great course.
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
The course was great, but the virtual machine is obsolete and some operations were performed in my native machine. Some python code had to be adapted from version 2 to version 3 (in my case the version was 3.12.3), and some additional changes where done to file "big-data-2\sensor\plot-data.py", because "matplotlib.dates.epoch2num(x)" is no more supported, so it was replaced with a change in the structure of "x" with "x.append(datetime.datetime.fromtimestamp(timestamp))". The data stream service at rtd.hpwren.ucsd.edu in port 12020 was not active, but the video for the hands-on was useful to look at the real-time plot of the stream.
Good course overall. Fundamentals covered well. Perhaps a bit too easy. A lot of python scripts are broken with the latest version of python. CentOS is also no longer supported. Should have used a 100% self-contained VM and not getting users to re-download python etc..
Was a very hands-on experience with the Hadoop ecosystem streaming and analysis of live tweets from twitter gave a general outlook on how to perform simple operations with the HDFS