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.
Informações sobre o curso
Aprox. 21 horas para completar
Aprox. 21 horas para completar
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Principais avaliações do MANIPULAÇÃO DE DADOS EM ESCALA: SISTEMAS E ALGORITIMOS
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.
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.
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.
It's pretty tough in assignments especially when there are mistakes in the given description, but I do learn the basic concepts of relational algorithm and MapReduce from them.
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.
Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.\n\nHighly recommended.
Its pretty decent. I liked the assignments. However there were some typos in the lecture slides and also the grader output is not very friendly.
Very good course, but lectures could be more tuned onto the home assignments. A lot of independent work for me at least. Teacher is very good.
The course is very coherent and comprehensive. It covers only important aspects of the fields. Also, the exercises are very well prepared.
Course gives you good overview on different important data science technologies. Hands on labs are important to get the grip on concepts.
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.
This is a quite wonderful course for large-scale data science. I believe I will have learned a lot via completing the courses.
Engaging problemset makes sure that you will get your hands dirty with data. And that is great! Definitely worth your time.
Very good introduction to the topic; requires quite an effort to complete the assignments, but the outcome is worth it.
covers a lot of ground quickly, but you still get a good understanding of the underlying theory or technologies
Very good introduction to relational algebra and map reduce. Also helped scratch up on some python and SQL.
A very good introduction to skills needed for applying data science ideas on large scale data problems.
A great way to start, and become familiar with the nature, requirements & analytics of today's data.
Very very very tough for me. took me 3 months to finish.\n\nBut I learned so much from this course.
Great content. The questions are academic and sometimes hard to understand the desired outcome
Universidade 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.
Sobre Programa de cursos integrados Ciência de dados em larga escalaCiência de Dados em Larga Escala
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