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

Data Manipulation at Scale: Systems and Algorithms

Bill Howe

Instructor: Bill Howe

61,422 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.3

(766 reviews)

20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
89%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.3

(766 reviews)

20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
89%
Most learners liked this course

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Taught in English

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This course is part of the Data Science at Scale Specialization
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There are 5 modules in this course

Understand the terminology and recurring principles associated with data science, and understand the structure of data science projects and emerging methodologies to approach them. Why does this emerging field exist? How does it relate to other fields? How does this course distinguish itself? What do data science projects look like, and how should they be approached? What are some examples of data science projects?

What's included

22 videos4 readings1 programming assignment

Relational Databases are the workhouse of large-scale data management. Although originally motivated by problems in enterprise operations, they have proven remarkably capable for analytics as well. But most importantly, the principles underlying relational databases are universal in managing, manipulating, and analyzing data at scale. Even as the landscape of large-scale data systems has expanded dramatically in the last decade, relational models and languages have remained a unifying concept. For working with large-scale data, there is no more important programming model to learn.

What's included

24 videos1 programming assignment

The MapReduce programming model (as distinct from its implementations) was proposed as a simplifying abstraction for parallel manipulation of massive datasets, and remains an important concept to know when using and evaluating modern big data platforms.

What's included

26 videos1 programming assignment

NoSQL systems are purely about scale rather than analytics, and are arguably less relevant for the practicing data scientist. However, they occupy an important place in many practical big data platform architectures, and data scientists need to understand their limitations and strengths to use them effectively.

What's included

36 videos

Graph-structured data are increasingly common in data science contexts due to their ubiquity in modeling the communication between entities: people (social networks), computers (Internet communication), cities and countries (transportation networks), or corporations (financial transactions). Learn the common algorithms for extracting information from graph data and how to scale them up.

What's included

21 videos

Instructor

Instructor ratings
4.1 (15 ratings)
Bill Howe
University of Washington
4 Courses89,222 learners

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Recommended if you're interested in Data Analysis

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4.3

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