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

4.3
697 Bewertungen
152 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...

Top-Bewertungen

HA

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.

SL

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 148 Bewertungen für Data Manipulation at Scale: Systems and Algorithms

von Jun Q

Aug 08, 2016

This is a quite wonderful course for large-scale data science. I believe I will have learned a lot via completing the courses.

von DAUTREY

Mar 14, 2016

very interesting materials about RDBMS and nosql systems

von valery n

Sep 02, 2017

Excelente curso, contenidos muy completos; sin embargo, deberían actualizar las instrucciones de cada Assignment con las correcciones ya descritas en los foros, para algunos es díficil encontrar estas correcciones fuera del enunciado. Por lo demás, gracias por esta oportunidad, por abrir las puertas de una universidad tan importante a otros estudiantes que jamás podrían asistir a su campus.

von Mangesh J

Sep 27, 2015

Awesome course. I would love more courses like this.The only part I feel rather discomforting is that the course does not offer non verified certificates to those who cannot afford the 59 dollar fee (PS from India and 59 dollar for a course is huge deal for me) :)

von Christopher A

Sep 29, 2015

It gave a nice, challenging and very engaging introduction to different data preparation techniques. The course surveyed Twitter data stream analysis, SQL, MapReduce jobs and a host of NoSQL and Graph tools. While it could use assignments for the latter topics, the course was structured in an easy to follow manner and was sufficiently challenging to keep engagement. In addition, the way the lessons were broken down into digestable chunks greatly aided in keeping engagement and keeping my interest. I look forward to future courses offered by UWashington and the same professor.

von Achal K

Feb 05, 2018

A very good introduction to skills needed for applying data science ideas on large scale data problems.

von Artur S

Nov 08, 2015

Brilliant course with amazing test tasks!

von Hernan A

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.

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 Muhammad A I

Sep 11, 2019

Love the the concept of "learning abstraction rather than tool".

von Huangtastic

Sep 10, 2019

The lecture covers a broad range of materials, from complexity of algorithm to map reduced formulation. The assignments are challenging and up to date. However, I would prefer the lecture to be more technical and coherent.

von Minh T

Aug 24, 2019

Great for students.

von Muhammad Z H

Sep 19, 2019

learnt a lot

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 Dan C

Jun 09, 2016

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

von Maxime B

Mar 01, 2016

The power-point used has a lot of mistakes corrected live by the speaker.

The speaker speech is sometime slow and not precise, it probably has been recorded only once.

Apart from that the content covers the subject and the assignments are relevant and fun.

von Fermin Q

Nov 03, 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.

von SIU C M

Sep 29, 2015

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

von Kay S

Feb 06, 2016

There are some inconsistencies in the course or the arrangement of the videos, maybe due to technical problems.

For improvement I would really wish to have some substantial results, especially in the last week of the course. That is, I would prefer to discuss at least something in depth rather than everything broadly.