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Learner Reviews & Feedback for Data Manipulation at Scale: Systems and Algorithms by University of Washington

4.3
stars
767 ratings

About the Course

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams...

Top reviews

HA

Jan 11, 2016

Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice.The lessons are well designed and clearly conveyed.

DK

Jan 24, 2016

Good! I like the final (optional) project on running on a large dataset through EC2. The lectures aren't as polished and compact as they could be but certainly a very valuable course.

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101 - 125 of 168 Reviews for Data Manipulation at Scale: Systems and Algorithms

By Fermin Q

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Nov 3, 2016

It gives good information, but frankly covers way too many tools at the end, and the explanations are good but somewhat rushed. Some parts were a little boring, as no immediate practical use seemed on the horizon.

By MICHEL S

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Jan 5, 2016

Very broad and instructive course with a good level of theory, many practical examples. Good teaching.

Some nice assignments but a lake of assignement for the 4th week

I recommand this course

By Anne-Marie D

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Jul 20, 2020

Well structured and nice overview of data manipulation. But the assignments should really be updated in order to use python 3.x instead of 2.7, which is not maintained anymore...

By Yu-Heng H

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Nov 25, 2018

It's pretty tough in assignments especially when there are mistakes in the given description, but I do learn the basic concepts of relational algorithm and MapReduce from them.

By Wesley E

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Oct 4, 2016

Definitely need some background in R or Python and the lectures are a bit old. Seem to be from around 2013 when this first came out but most of the info is still relevant.

By sajit m k

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Jan 12, 2016

Its pretty decent. I liked the assignments. However there were some typos in the lecture slides and also the grader output is not very friendly.

By Alari

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Dec 3, 2015

Very good course, but lectures could be more tuned onto the home assignments. A lot of independent work for me at least. Teacher is very good.

By Mandar B

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Mar 29, 2017

Course gives you good overview on different important data science technologies. Hands on labs are important to get the grip on concepts.

By Dmitry G

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Jan 2, 2016

Last week of the course is too much information and without any assignments it kind of doesn't make much sense and it doesn't stick.

By Tony G

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May 13, 2016

covers a lot of ground quickly, but you still get a good understanding of the underlying theory or technologies

By Timothy R

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Jun 22, 2017

Very good introduction to relational algebra and map reduce. Also helped scratch up on some python and SQL.

By Chuck C

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Jun 26, 2017

Great content. The questions are academic and sometimes hard to understand the desired outcome

By Damien L

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Nov 16, 2017

Excellent course. I just sad about the absence of any assignment or even quiz in Week 4..

By SIU C M

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Sep 29, 2015

It is a comprehensive course for learning quite up-to-date technology and concept.

By Abhijit S

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Oct 21, 2015

Good Course for beginner in Data Scientist field. I recommend this course

By Gregory C

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Nov 25, 2017

Very good class - the assignments were pretty uninteresting, though.

By Dan C

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Jun 9, 2016

I enjoyed this course and found it challenging. Good job!

By Jiancheng Y

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Dec 6, 2015

Great assignment and course design! Not easy for me.

By Jeffrey L

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Jan 9, 2016

Very good course! Interesting problem sets.

By Gregory T

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Nov 29, 2015

Interesting intro to some powerful ideas

By Jack X

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Feb 12, 2017

recommend to improve assignment details

By Krzysztof L

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Jul 27, 2016

Good introduction to Big Data systems.

By Sophia J

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Oct 27, 2015

it is very useful but easy enough

By Dario P C

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Mar 25, 2016

Very usefull course. Great!

By Vijayasenthilkumar K

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Mar 28, 2017

Excellent course!