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Learner Reviews & Feedback for Data Manipulation at Scale: Systems and Algorithms by University of Washington

697 ratings
152 reviews

About the Course

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 reviews


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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1 - 25 of 148 Reviews for Data Manipulation at Scale: Systems and Algorithms

By Max E

Nov 12, 2018

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

By Anish C

Jan 17, 2018

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

By Daniella B

Apr 21, 2016

Lectures are great and well structured. Programming assignments are just amazing and interesting. Great course!

By Robert H S J

Feb 15, 2016

I learned so much from this course. In particular, I've got a much more solid grasp of SQL (even though I've been using it for 30 years), and much more clarity about "map/reduce". The lectures are clear, delivery is excellent, and the assignments are interesting.

By Benjamin T

Feb 25, 2016

- great and very useful overview of concepts important in big data that does not get bogged down in random details

- interesting and sufficiently challenging assignments

By Daniel W

Apr 26, 2017

For me, a really nice combination of

1. a theoretical overview of database and data processing concepts, MapReduce and the most important implementations of the various concepts (SQL and NoSQL databases),

2. practical application of these concepts in real-world programming exercises.

I like the way Bill explains, and I like the exercises - however, to complete those, you need to be ready to learn the technology on your own, the lectures are NOT about learning the technology (Python programming etc.) to do the exercises. For me, that's fine, but for people who have little or no programming experience it might be frustrating.

So, if you like the combined approach of this course, I can really recommend it!

By Ahmed M E E

Apr 14, 2017

Very good and informative course for data scientists and data engineers

By devang

Oct 04, 2015

Amazing Course!

By Achal K

Feb 05, 2018

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

By Asier

Nov 21, 2015

Excellent overview of the Big Data field and its relation to eScience.


Mar 14, 2016

very interesting materials about RDBMS and nosql systems

By 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.

By 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) :)

By 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.

By Paulo S S S

Feb 06, 2016

Very relevant if you want to understand the theories behind data systems and algorithms. I consider it a bit time consuming but completely worth taking into consideration the amount of topics it covers.

By Sebastian O M

Nov 21, 2015

100% Recomendado

By Guruswamy S

May 29, 2018

Very wide and fundamentally robust introduction.

By Mahmoud M

Jan 18, 2016

The course is very coherent and comprehensive. It covers only important aspects of the fields. Also, the exercises are very well prepared.

By Vaibhav G

Jun 16, 2017

Awesome content.

By 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.

By 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.


Jan 21, 2016

Interesting course, good hands-on exercises. very useful course to practice python

By Zahid P

Nov 14, 2015

While I haven't been able to keep up and submit most assignments, the material seems highly relevant and good to know. The videos are helpful and assignments provide good practice.

Note: I am currently a software engineer and have an undergrad degree in Industrial Engineering (so I have some exposure to the concepts in the course).

By Francisco A J

Mar 06, 2017

Overall, this was an excellent introductory course. The instructor presented the material in a very clear manner and introduced all topics using applied examples. The weekly assignments were aligned with the course content as well, allowing me to apply the knowledge learned in each lesson.

By Felipe G

Mar 07, 2016

great course! ... congratulations.