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

4.3
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...

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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26 - 50 von 145 Bewertungen für Data Manipulation at Scale: Systems and Algorithms

von Jeffery L T

Jan 27, 2017

Great course!

von Do H L

Jun 30, 2016

I had a lot, really, a lot of fun in this course.

The first week was really awesome! Although the debugging was very tedious and time-consuming, I felt a great deal of achievement first hand dealing with Twitter data and coded up text analytics algorithms from scratch.

Really a great introductory course to data science! Highly recommend because it's really fun. However, a great amount of comfort with coding and patience for working through ambiguous bug messages will be essential to completing this course :)

von Kenneth P

Dec 06, 2015

Course is well structured, moving on with the lessons is a build up of techniques and concepts. Delivery of the course material is well paced and gives all the required information to grasp the concepts.

von Korbinian K

Nov 07, 2016

Really useful course when you want to learn about big data management and need a starter. It is however definitely recommended to have some programming experience and knowledge about bash/command line. The course met all my expectations, but to make it perfect I would have wished for an extra exercise using Pig or Spark.

von Wonjun L

Mar 06, 2016

If you are interested in data science then this course is the right one.

von Ivan S

May 11, 2017

Nice !

von Roberto S

Jun 13, 2017

Very good introduction to the topic; requires quite an effort to complete the assignments, but the outcome is worth it.

von Shambhu R

Jul 27, 2016

Very nice course!

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 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 Sofia C

Nov 15, 2016

The contents were very relevant and more geared to those with some experience already. The assignments are worth doing. The only problem is that some of the assignments have errors which are only listed in pinned posts in the forum (with a link to a ticket but nothing's been done about it). Still, learned a lot so the on the whole would recommend it.

von Dimitrios K

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.

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 francisco y

Jan 19, 2016

Great course!

von Dan S R

May 25, 2017

Great work, very satisfied!!

von Matthew M

Jan 21, 2016

excellent treatment of the material

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 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.