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

757 évaluations
168 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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26 - 50 sur 164 Avis pour Data Manipulation at Scale: Systems and Algorithms

par Killdary A d S

4 juil. 2019

Excelente curso, conteúdo fácil de entender e realmente desafiador. Recomendo para quem quer entender como é realizado a extração e análise de dados não estruturados.

par Leonid G

20 juin 2017

Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.

Highly recommended.

par Mahmoud M

18 janv. 2016

The course is very coherent and comprehensive. It covers only important aspects of the fields. Also, the exercises are very well prepared.

par Jun Q

8 août 2016

This is a quite wonderful course for large-scale data science. I believe I will have learned a lot via completing the courses.

par Karol O

22 déc. 2019

Engaging problemset makes sure that you will get your hands dirty with data. And that is great! Definitely worth your time.

par Roberto S

13 juin 2017

Very good introduction to the topic; requires quite an effort to complete the assignments, but the outcome is worth it.

par Daniella B

21 avr. 2016

Lectures are great and well structured. Programming assignments are just amazing and interesting. Great course!

par Itai S

14 nov. 2015

הקורס נותן חשיפה טובה לכלי העבודה העדכניים. המשימות אינן פשוטות למשתמש המתחיל ודורשות התעמקות אך בהחלט אפשריות

par Achal K

5 févr. 2018

A very good introduction to skills needed for applying data science ideas on large scale data problems.

par Raheel H

1 juil. 2019

A great way to start, and become familiar with the nature, requirements & analytics of today's data.

par Bingcheng L

4 août 2019

Very very very tough for me. took me 3 months to finish.

But I learned so much from this course.

par Padam J T

7 août 2021

One of the best Data Science course I've ever taken anywhere. One should definitely go for it.

par Batt J

14 avr. 2018

Very good course for understanding the underlying logic behind emerging big data technologies

par Edwin A P V

12 déc. 2020

It's excellent. Important: Python Dev knowledge is a plus to complete the assignments.

par Usman

27 déc. 2016

A great course. I would just like more assignments and more information about spark.

par BI C

20 janv. 2016

Interesting course, good hands-on exercises. very useful course to practice python

par Kazım S

10 sept. 2017

If you want to head into Data Science, this is a nice course that will help you.

par Daniel A

21 nov. 2015

This was a great course - well planned out and really informative. Thanks!

par Wonjun L

6 mars 2016

If you are interested in data science then this course is the right one.

par Ahmed A

14 avr. 2017

Very good and informative course for data scientists and data engineers

par Asier

20 nov. 2015

Excellent overview of the Big Data field and its relation to eScience.

par Bruno F S

15 févr. 2016

Great course for those who want to know more about big data analysis.

par Muhammad A I

10 sept. 2019

Love the the concept of "learning abstraction rather than tool".

par Gokhan C

28 mai 2016

The assignments are really what make this course stand out.

par NothingElse

5 nov. 2015

speed is too fast, I can hard to keep pace with teacher's s