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Kursteilnehmer-Bewertung und -Feedback für Data Manipulation at Scale: Systems and Algorithms von University of Washington

687 Bewertungen
148 Bewertungen

Über den Kurs

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...



Jan 11, 2016

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


May 28, 2016

I like the breadth of coverage of this class. Each of the exercise is a gem in that I get to learn something new also. I would highly recommend this even to experience practitioner also.

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51 - 75 von 144 Bewertungen für Data Manipulation at Scale: Systems and Algorithms

von Daniel A

Nov 21, 2015

This was a great course - well planned out and really informative. Thanks!

von Vijai K S

Jan 20, 2016

Going through the content really scares someone like me. At the same time, i feel that the challenge in doing the assignments will only help me improve well. I would suggest beginners to stay away and get a hold of the basics before jumping into the course.

von Shibaji M

Sep 17, 2015

This is a great course

von Maria P

Oct 28, 2015

4.5 because it was very difficult to access the optional assignments and there was effort expended on reformatting them since the last offering of the course. Otherwise it's an excellent course and I've already been recommending it.

von Miao J

Dec 25, 2015

Great course. Very helpful!

von francisco y

Jan 19, 2016

Great course!

von Usman

Dec 27, 2016

A great course. I would just like more assignments and more information about spark.

von Dan S R

May 25, 2017

Great work, very satisfied!!

von Jakub B

Jan 04, 2016

Really good teaching: instead of cramming tons of information lecturer differentiates main ideas from technical stuff (that will surely change in several years). Also, exercises are good.

Disclaimer: I have browsed several courses that touch these topics. I think this one is the best, at least on coursera.

von Nayan J

Dec 14, 2015

Coding assignments help shed the resistance :)

von Matthew M

Jan 21, 2016

excellent treatment of the material

von Raheel H

Jul 01, 2019

A great way to start, and become familiar with the nature, requirements & analytics of today's data.

von Killdary A d S

Jul 04, 2019

Excelente curso, conteúdo fácil de entender e realmente desafiador. Recomendo para quem quer entender como é realizado a extração e análise de dados não estruturados.

von Desiree D

Jul 31, 2019

Hard but awesome

von Bingcheng L

Aug 04, 2019

Very very very tough for me. took me 3 months to finish.

But I learned so much from this course.

von Yu-Heng H

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.

von Gregory C

Nov 25, 2017

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

von Christine

Jul 09, 2016

This is a great introduction. I would give it a higher scoring but was frustrated with one of the labs have issues in the grader and even after completion of the course its not clear what was wrong: the instructions, the solution data set, the input data set. Other then that enjoyed the content.

von Dmitry G

Jan 02, 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.

von Sajit K

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.

von Xuefei J

Oct 27, 2015

it is very useful but easy enough

von Hao-en S

Nov 14, 2015

Excellent course content and lecture, however, the homework design is not so friendly for students.

First, instructions are not clear. I spent a lot of time in figuring out the real meaning behind each problem. Second, the judge system is not so rigorous. Though trying best on homework and not playing tricks are students' responsibility, the judge system is just too weak to survive those hackings.

Thank you all for your efforts.

von Vijaya S K K

Mar 28, 2017

Excellent course!

von Annavajjala S P A S

Mar 13, 2017

The contents of the course were good enough. The assignments, though simple required some work in terms of understanding the kind of data that you are dealing with, which is important. Although, a lot of content has been covered, it was arranged in a logical manner.

von Dario P C

Mar 25, 2016

Very usefull course. Great!