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Relational AlgebraPython ProgrammingMapreduceSQL

次における4の1コース

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英語

字幕:英語

シラバス - 本コースの学習内容

1
6時間で修了

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?

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22件のビデオ (合計125分), 4 readings, 1 quiz
22件のビデオ
Appetite Whetting: Extreme Weather2 分
Appetite Whetting: Digital Humanities8 分
Appetite Whetting: Bibliometrics4 分
Appetite Whetting: Food, Music, Public Health5 分
Appetite Whetting: Public Health cont'd, Earthquakes, Legal4 分
Characterizing Data Science5 分
Characterizing Data Science, cont'd5 分
Distinguishing Data Science from Related Topics4 分
Four Dimensions of Data Science6 分
Tools vs. Abstractions7 分
Desktop Scale vs. Cloud Scale5 分
Hackers vs. Analysts2 分
Structs vs. Stats5 分
Structs vs. Stats cont'd5 分
A Fourth Paradigm of Science3 分
Data-Intensive Science Examples6 分
Big Data and the 3 Vs5 分
Big Data Definitions4 分
Big Data Sources6 分
Course Logistics7 分
Twitter Assignment: Getting Started14 分
4件の学習用教材
Supplementary: Three-Course Reading List10 分
Supplementary: Resources for Learning Python10 分
Supplementary: Class Virtual Machine10 分
Supplementary: Github Instructions10 分
2
5時間で修了

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.

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24件のビデオ (合計122分), 1 quiz
24件のビデオ
From Data Models to Databases4 分
Pre-Relational Databases5 分
Motivating Relational Databases3 分
Relational Databases: Key Ideas4 分
Algebraic Optimization Overview6 分
Relational Algebra Overview4 分
Relational Algebra Operators: Union, Difference, Selection6 分
Relational Algebra Operators: Projection, Cross Product4 分
Relational Algebra Operators: Cross Product cont'd, Join6 分
Relational Algebra Operators: Outer Join4 分
Relational Algebra Operators: Theta-Join4 分
From SQL to RA6 分
Thinking in RA: Logical Query Plans4 分
Practical SQL: Binning Timeseries5 分
Practical SQL: Genomic Intervals6 分
User-Defined Functions3 分
Support for User-Defined Functions4 分
Optimization: Physical Query Plans5 分
Optimization: Choosing Physical Plans4 分
Declarative Languages5 分
Declarative Languages: More Examples4 分
Views: Logical Data Independence5 分
Indexes6 分
3
5時間で修了

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.

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26件のビデオ (合計122分), 1 quiz
26件のビデオ
A Sketch of Algorithmic Complexity5 分
A Sketch of Data-Parallel Algorithms5 分
"Pleasingly Parallel" Algorithms4 分
More General Distributed Algorithms4 分
MapReduce Abstraction4 分
MapReduce Data Model3 分
Map and Reduce Functions2 分
MapReduce Simple Example3 分
MapReduce Simple Example cont'd3 分
MapReduce Example: Word Length Histogram2 分
MapReduce Examples: Inverted Index, Join6 分
Relational Join: Map Phase4 分
Relational Join: Reduce Phase4 分
Simple Social Network Analysis: Counting Friends3 分
Matrix Multiply Overview5 分
Matrix Multiply Illustrated4 分
Shared Nothing Computing4 分
MapReduce Implementation5 分
MapReduce Phases6 分
A Design Space for Large-Scale Data Systems4 分
Parallel and Distributed Query Processing5 分
Teradata Example, MR Extensions5 分
RDBMS vs. MapReduce: Features6 分
RDBMS vs. Hadoop: Grep5 分
RDBMS vs. Hadoop: Select, Aggregate, Join3 分
4
3時間で修了

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.

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36件のビデオ (合計166分)
36件のビデオ
NoSQL Roundup4 分
Relaxing Consistency Guarantees3 分
Two-Phase Commit and Consensus Protocols5 分
Eventual Consistency4 分
CAP Theorem4 分
Types of NoSQL Systems4 分
ACID, Major Impact Systems4 分
Memcached: Consistent Hashing2 分
Consistent Hashing, cont'd4 分
DynamoDB: Vector Clocks5 分
Vector Clocks, cont'd5 分
CouchDB Overview4 分
CouchB Views3 分
BigTable Overview5 分
BigTable Implementation5 分
HBase, Megastore3 分
Spanner5 分
Spanner cont'd, Google Systems6 分
MapReduce-based Systems5 分
Bringing Back Joins4 分
NoSQL Rebuttal4 分
Almost SQL: Pig4 分
Pig Architecture and Performance3 分
Data Model3 分
Load, Filter, Group5 分
Group, Distinct, Foreach, Flatten5 分
CoGroup, Join3 分
Join Algorithms3 分
Skew5 分
Other Commands3 分
Evaluation Walkthrough3 分
Review6 分
Context3 分
Spark Examples5 分
RDDs, Benefits6 分
2時間で修了

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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21件のビデオ (合計91分)
21件のビデオ
Structural Analysis4 分
Degree Histograms, Structure of the Web4 分
Connectivity and Centrality4 分
PageRank3 分
PageRank in more Detail3 分
Traversal Tasks: Spanning Trees and Circuits5 分
Traversal Tasks: Maximum Flow1 分
Pattern Matching6 分
Querying Edge Tables4 分
Relational Algebra and Datalog for Graphs4 分
Querying Hybrid Graph/Relational Data3 分
Graph Query Example: NSA6 分
Graph Query Example: Recursion4 分
Evaluation of Recursive Programs3 分
Recursive Queries in MapReduce4 分
The End-Game Problem3 分
Representation: Edge Table, Adjacency List4 分
Representation: Adjacency Matrix2 分
PageRank in MapReduce5 分
PageRank in Pregel5 分
4.3
143件のレビューChevron Right

Data Manipulation at Scale: Systems and Algorithms からの人気レビュー

by HAJan 11th 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.

by SLMay 28th 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.

講師

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Bill Howe

Director of Research
Scalable Data Analytics

ワシントン大学(University of Washington)について

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....

Data Science at Scaleの専門講座について

Learn scalable data management, evaluate big data technologies, and design effective visualizations. This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project....
Data Science at Scale

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