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Avis et commentaires pour d'étudiants pour Data Manipulation at Scale: Systems and Algorithms par Université de Washington

756 évaluations
167 avis

À propos du cours

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...

Meilleurs avis


10 janv. 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 mai 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 sur 163 Avis pour Data Manipulation at Scale: Systems and Algorithms

par suyang z

15 oct. 2015

good for people who have some experience in python and SQL

par Anish M

24 sept. 2015

great exercises and assignments. The course is involving.


14 mars 2016

very interesting materials about RDBMS and nosql systems

par Srikanth G

29 mai 2018

Very wide and fundamentally robust introduction.

par Nayan J

14 déc. 2015

Coding assignments help shed the resistance :)

par Shivanand R K

18 juin 2016

Excellent thoughts and concepts presented.

par Artur S

8 nov. 2015

Brilliant course with amazing test tasks!

par Kevin R

12 nov. 2015

Great exercises one can learn alot from.

par Cesar O

16 août 2020

Nice explanation of mapReduce, love it

par Matthew M

21 janv. 2016

excellent treatment of the material

par Felipe G

7 mars 2016

great course! ... congratulations.

par Roland P

27 juil. 2017

Great intro into wider aspects

par Dan R

25 mai 2017

Great work, very satisfied!!

par Miao J

24 déc. 2015

Great course. Very helpful!

par Luis E

15 avr. 2022

Buen curso, bien explicado

par Shibaji M

17 sept. 2015

This is a great course

par Minh T

24 août 2019

Great for students.

par Menghe L

8 juin 2017

great for learner

par Shambhu R

27 juil. 2016

Very nice course!

par Desiree D

31 juil. 2019

Hard but awesome

par Vaibhav G

16 juin 2017

Awesome content.

par Sebastian O M

21 nov. 2015

100% Recomendado

par devang

4 oct. 2015

Amazing Course!

par Jeffery L T

27 janv. 2017

Great course!

par francisco y

18 janv. 2016

Great course!