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.
Programa Especializado - Data Science at ScaleUniversidad de WashingtonAcerca de este Curso
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Certificado para compartir
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Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 4 en
Fechas límite flexibles
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Aprox. 20 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Habilidades que obtendrás
Relational AlgebraPython ProgrammingMapreduceSQL
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 20 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Universidad de Washington
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.
Programa - Qué aprenderás en este curso
6 horas para completar
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?
6 horas para completar
22 videos (Total 125 minutos), 4 lecturas, 1 cuestionario
22 videos
Appetite Whetting: Extreme Weather2m
Appetite Whetting: Digital Humanities8m
Appetite Whetting: Bibliometrics4m
Appetite Whetting: Food, Music, Public Health5m
Appetite Whetting: Public Health cont'd, Earthquakes, Legal4m
Characterizing Data Science5m
Characterizing Data Science, cont'd5m
Distinguishing Data Science from Related Topics4m
Four Dimensions of Data Science6m
Tools vs. Abstractions7m
Desktop Scale vs. Cloud Scale5m
Hackers vs. Analysts2m
Structs vs. Stats5m
Structs vs. Stats cont'd5m
A Fourth Paradigm of Science3m
Data-Intensive Science Examples6m
Big Data and the 3 Vs5m
Big Data Definitions4m
Big Data Sources6m
Course Logistics7m
Twitter Assignment: Getting Started14m
4 lecturas
Supplementary: Three-Course Reading List10m
Supplementary: Resources for Learning Python10m
Supplementary: Class Virtual Machine10m
Supplementary: Github Instructions10m
5 horas para completar
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.
5 horas para completar
24 videos (Total 122 minutos)
24 videos
From Data Models to Databases4m
Pre-Relational Databases5m
Motivating Relational Databases3m
Relational Databases: Key Ideas4m
Algebraic Optimization Overview6m
Relational Algebra Overview4m
Relational Algebra Operators: Union, Difference, Selection6m
Relational Algebra Operators: Projection, Cross Product4m
Relational Algebra Operators: Cross Product cont'd, Join6m
Relational Algebra Operators: Outer Join4m
Relational Algebra Operators: Theta-Join4m
From SQL to RA6m
Thinking in RA: Logical Query Plans4m
Practical SQL: Binning Timeseries5m
Practical SQL: Genomic Intervals6m
User-Defined Functions3m
Support for User-Defined Functions4m
Optimization: Physical Query Plans5m
Optimization: Choosing Physical Plans4m
Declarative Languages5m
Declarative Languages: More Examples4m
Views: Logical Data Independence5m
Indexes6m
5 horas para completar
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.
5 horas para completar
26 videos (Total 122 minutos)
26 videos
A Sketch of Algorithmic Complexity5m
A Sketch of Data-Parallel Algorithms5m
"Pleasingly Parallel" Algorithms4m
More General Distributed Algorithms4m
MapReduce Abstraction4m
MapReduce Data Model3m
Map and Reduce Functions2m
MapReduce Simple Example3m
MapReduce Simple Example cont'd3m
MapReduce Example: Word Length Histogram2m
MapReduce Examples: Inverted Index, Join6m
Relational Join: Map Phase4m
Relational Join: Reduce Phase4m
Simple Social Network Analysis: Counting Friends3m
Matrix Multiply Overview5m
Matrix Multiply Illustrated4m
Shared Nothing Computing4m
MapReduce Implementation5m
MapReduce Phases6m
A Design Space for Large-Scale Data Systems4m
Parallel and Distributed Query Processing5m
Teradata Example, MR Extensions5m
RDBMS vs. MapReduce: Features6m
RDBMS vs. Hadoop: Grep5m
RDBMS vs. Hadoop: Select, Aggregate, Join3m
3 horas para completar
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.
3 horas para completar
36 videos (Total 166 minutos)
36 videos
NoSQL Roundup4m
Relaxing Consistency Guarantees3m
Two-Phase Commit and Consensus Protocols5m
Eventual Consistency4m
CAP Theorem4m
Types of NoSQL Systems4m
ACID, Major Impact Systems4m
Memcached: Consistent Hashing2m
Consistent Hashing, cont'd4m
DynamoDB: Vector Clocks5m
Vector Clocks, cont'd5m
CouchDB Overview4m
CouchB Views3m
BigTable Overview5m
BigTable Implementation5m
HBase, Megastore3m
Spanner5m
Spanner cont'd, Google Systems6m
MapReduce-based Systems5m
Bringing Back Joins4m
NoSQL Rebuttal4m
Almost SQL: Pig4m
Pig Architecture and Performance3m
Data Model3m
Load, Filter, Group5m
Group, Distinct, Foreach, Flatten5m
CoGroup, Join3m
Join Algorithms3m
Skew5m
Other Commands3m
Evaluation Walkthrough3m
Review6m
Context3m
Spark Examples5m
RDDs, Benefits6m
2 horas para completar
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.
2 horas para completar
21 videos (Total 91 minutos)
21 videos
Structural Analysis4m
Degree Histograms, Structure of the Web4m
Connectivity and Centrality4m
PageRank3m
PageRank in more Detail3m
Traversal Tasks: Spanning Trees and Circuits5m
Traversal Tasks: Maximum Flow1m
Pattern Matching6m
Querying Edge Tables4m
Relational Algebra and Datalog for Graphs4m
Querying Hybrid Graph/Relational Data3m
Graph Query Example: NSA6m
Graph Query Example: Recursion4m
Evaluation of Recursive Programs3m
Recursive Queries in MapReduce4m
The End-Game Problem3m
Representation: Edge Table, Adjacency List4m
Representation: Adjacency Matrix2m
PageRank in MapReduce5m
PageRank in Pregel5m
Revisiones
Principales revisiones sobre DATA MANIPULATION AT SCALE: SYSTEMS AND ALGORITHMS
por HA10 de ene. de 2016
Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice. The lessons are well designed and clearly conveyed.
por SL27 de may. de 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.
por DK23 de ene. de 2016
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.
por AD19 de jul. de 2020
Well structured and nice overview of data manipulation. But the assignments should really be updated in order to use python 3.x instead of 2.7, which is not maintained anymore...
Acerca de Programa especializado: Data Science at Scale
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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
- es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
- Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Hay ayuda económica disponible?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.
¿Recibiré crédito universitario por completar el Curso?
Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.
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