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

740 Bewertungen
161 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 157 Bewertungen für Data Manipulation at Scale: Systems and Algorithms

von Guruswamy S

May 29, 2018

Very wide and fundamentally robust introduction.

von Nayan J

Dec 14, 2015

Coding assignments help shed the resistance :)

von Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

von Artur S

Nov 08, 2015

Brilliant course with amazing test tasks!

von Kevin R

Nov 12, 2015

Great exercises one can learn alot from.

von Cesar O

Aug 16, 2020

Nice explanation of mapReduce, love it

von Matthew M

Jan 21, 2016

excellent treatment of the material

von Felipe G

Mar 07, 2016

great course! ... congratulations.

von Roland P

Jul 27, 2017

Great intro into wider aspects

von Dan S R

May 25, 2017

Great work, very satisfied!!

von Miao J

Dec 25, 2015

Great course. Very helpful!

von Shibaji M

Sep 17, 2015

This is a great course

von Minh T

Aug 24, 2019

Great for students.

von Menghe L

Jun 08, 2017

great for learner

von Shambhu R

Jul 27, 2016

Very nice course!

von Desiree D

Jul 31, 2019

Hard but awesome

von Vaibhav G

Jun 16, 2017

Awesome content.

von Sebastian O M

Nov 21, 2015

100% Recomendado

von devang

Oct 04, 2015

Amazing Course!

von Jeffery L T

Jan 27, 2017

Great course!

von francisco y

Jan 19, 2016

Great course!

von Muhammad Z H

Sep 19, 2019

learnt a lot

von Ivan S

May 11, 2017

Nice !

von Jan Z

Nov 21, 2016

The course was very good - especially the map-reduce part I found very well explained and inspirational. The problem sets were thought-provoking and really taught me a lot.

Two things that could be improved:

1) The problem sets are really nice (again, map-reduce is the best one), but there are quite a few errors in the description, a lot of information is dated (e.g. in ps.1 the twitter link is old), and working with the grader can be very clumsy. See Machine Learning by Andrew Ng to see how to design perfect, easy to operate and submit problem sets. Perhaps work with PyCharm creators?

2) The second to last part was a bit lacking - it was basically skimming though all different types of databases, which didn't make me feel like I really acquired any skill. Because of how little time was spent on each database type and there were so many, I don't really remember much of it now (hardly anything to be honest).

von Dylan T

May 06, 2017

The course is interesting and well made. Compared the the other two, I found the first assignment quite difficult and required quite a bit of time to complete. Introducing SQL through relational algebra seemed relevant to me, and made the formulation of SQL queries very natural. The section about map reduce may appear difficult to process first but as the student has to go through (and beyond in one case) the examples presented in the course. In the end, I found the assignment very useful in putting thing in place. I received full grade but still have to go through week 4, maybe a small quiz in the end to test our understanding of the different concept would have been handy.