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.
Data Manipulation at Scale: Systems and Algorithms
Data Manipulation at Scale: Systems and Algorithms
This course is part of Data Science at Scale Specialization
Instructor: Bill Howe
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4.3
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20 hours to complete
3 weeks at 6 hours a week
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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)Offered by
Recommended if you're interested in Data Analysis
Universidad Nacional Autónoma de México
DeepLearning.AI
École Polytechnique Fédérale de Lausanne
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4.3
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Showing 3 of 766
HA
5
Reviewed on Jan 10, 2016
Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice.
DG
4
Reviewed on Jan 1, 2016
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.
WE
4
Reviewed on Oct 3, 2016
Definitely need some background in R or Python and the lectures are a bit old. Seem to be from around 2013 when this first came out but most of the info is still relevant.
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