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Data Manipulation at Scale: Systems and Algorithms, University of Washington

4.3
Оценки: 663
Рецензии: 142

Об этом курсе

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...

Лучшие рецензии

автор: 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.

автор: 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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Рецензии: 138

автор: Yu-Heng Hung

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.

автор: Max Ettelson

Nov 12, 2018

Assignments need to be updated, but the material is solid!

автор: Guruswamy Srikanth

May 29, 2018

Very wide and fundamentally robust introduction.

автор: Batt Jimmy

Apr 14, 2018

Very good course for understanding the underlying logic behind emerging big data technologies

автор: Dwayne Benefield

Apr 13, 2018

Good information but lectures were poorly produced and unedited and exercise instructions were blatantly incorrect several times.

автор: Achal Kathuria

Feb 05, 2018

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

автор: Anish Chandran

Jan 17, 2018

Thanks for this course.True Parallel computing example would have made it even more awesome .

автор: James Sheldon

Jan 07, 2018

The material is good. If you can get past the instructor's mumbling and rapid speaking then you'll be okay.

автор: Jana Endemann

Dec 07, 2017

Quite interesting subjects, but video material is not of high quality and many mistakes are not changed in later sessions but altered via a text in the screen of a note on the next sheet.

автор: Gregory Crysler

Nov 25, 2017

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