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

4.3
stars
748 ratings
162 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

HA
Jan 10, 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.

WL
May 27, 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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151 - 158 of 158 Reviews for Data Manipulation at Scale: Systems and Algorithms

By SHERRY W

Mar 27, 2017

This course totally reminds me of some courses back in college: unorganized material and the assignments are unrelated to the tutorial. The assignments themselves seem to be very helpful but the tutorials did no help of achieving these assignments.

I had a hard time following the instructor despite that I've completed all the certificate for python from University of Michigan. I'm aware of my background of python is still not strong enough so I thought it's probably just me not able to learn it fast enough.

But then I watched the tutorial about SQL. As a data architect / ETL developer, SQL is something I'm familiar with and use it everyday and then I realized that the instructor couldn't explain a nested query well. The reason I was able to understand about the SQL part is because I already know.

By tuzunkan

Dec 6, 2015

Lost in details. Professionals(btw I hold a MSc degree in Computer Engineering) cannot get anything from this. What is the point of writing frequency.pl where there is a hist() function in R? If the instructor is trying to teach us how to program in any language, then I can assure you the data science class is not the right place. I recommend the instructor check ESSEC Business School for analytics subject to better comprehend the Coursera and its goals.

By Lloney M

Nov 2, 2017

The course info makes no mention of Python as a prerequisite. Yet the first assignment demands Python knowledge and skills. Without which you can't pass the assignment. Yet the week's lecture is not about Python.

By Malina R

Oct 1, 2020

The instructors do not respond and they provide computer programs that do not work. They are aware that there are issues in the programs but have done nothing to remedy the issue.

By Andreea D L

Feb 6, 2016

Th first three classes are very 'thin' in content and the assignments are easy. The fourth class is basically optional and it has TONS of content. What's the point?

By Aitor G R

Feb 20, 2017

Outdated, unintelligibly exercises, terrible lectures.

By Catherine Z

Feb 19, 2016

Poorly designed videos, too long and confused

By FilippoV

Sep 19, 2017

very poor!