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.
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이 강좌에 대하여
귀하가 습득할 기술
- Relational Algebra
- Python Programming
- Mapreduce
- SQL
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워싱턴 대학교
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.
강의 계획표 - 이 강좌에서 배울 내용
Data Science Context and Concepts
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?
Relational Databases and the Relational Algebra
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.
MapReduce and Parallel Dataflow Programming
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.
NoSQL: Systems and Concepts
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.
Graph Analytics
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.
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- 5 stars57.25%
- 4 stars25.46%
- 3 stars9.10%
- 2 stars4.61%
- 1 star3.56%
DATA MANIPULATION AT SCALE: SYSTEMS AND ALGORITHMS의 최상위 리뷰
A great way to start, and become familiar with the nature, requirements & analytics of today's data.
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.
Last week of the course is too much information and without any assignments it kind of doesn't make much sense and it doesn't stick.
Engaging problemset makes sure that you will get your hands dirty with data. And that is great! Definitely worth your time.
대규모 데이터 과학 특화 과정 정보
Learn scalable data management, evaluate big data technologies, and design effective visualizations.

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