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Voltar para Manipulação de dados em escala: sistemas e algoritimos

Comentários e feedback de alunos de Manipulação de dados em escala: sistemas e algoritimos da instituição Universidade de Washington

752 classificações
164 avaliações

Sobre o curso

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. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams...

Melhores avaliações

10 de Jan de 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.

27 de Mai 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.

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51 — 75 de 160 Avaliações para o Manipulação de dados em escala: sistemas e algoritimos

por suyang z

15 de Out de 2015

good for people who have some experience in python and SQL

por Anish M

24 de Set de 2015

great exercises and assignments. The course is involving.


14 de Mar de 2016

very interesting materials about RDBMS and nosql systems

por Srikanth G

29 de Mai de 2018

Very wide and fundamentally robust introduction.

por Nayan J

14 de Dez de 2015

Coding assignments help shed the resistance :)

por Shivanand R K

18 de Jun de 2016

Excellent thoughts and concepts presented.

por Artur S

8 de Nov de 2015

Brilliant course with amazing test tasks!

por Kevin R

12 de Nov de 2015

Great exercises one can learn alot from.

por Cesar O

16 de Ago de 2020

Nice explanation of mapReduce, love it

por Matthew

21 de Jan de 2016

excellent treatment of the material

por Felipe G

7 de Mar de 2016

great course! ... congratulations.

por Roland P

27 de Jul de 2017

Great intro into wider aspects

por Dan S R

25 de Mai de 2017

Great work, very satisfied!!

por Miao J

24 de Dez de 2015

Great course. Very helpful!

por Shibaji M

17 de Set de 2015

This is a great course

por Minh T

24 de Ago de 2019

Great for students.

por Menghe L

8 de Jun de 2017

great for learner

por Shambhu R

27 de Jul de 2016

Very nice course!

por Desiree D

31 de Jul de 2019

Hard but awesome

por Vaibhav G

16 de Jun de 2017

Awesome content.

por Sebastian O M

21 de Nov de 2015

100% Recomendado

por devang

4 de Out de 2015

Amazing Course!

por Jeffery L T

27 de Jan de 2017

Great course!

por francisco y

18 de Jan de 2016

Great course!

por Muhammad Z H

19 de Set de 2019

learnt a lot