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Отзывы учащихся о курсе Data Manipulation at Scale: Systems and Algorithms от партнера Вашингтонский университет

4.3
Оценки: 686
Рецензии: 148

О курсе

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...

Лучшие рецензии

HA

Jan 11, 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.

SL

May 28, 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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1–25 из 144 отзывов о курсе Data Manipulation at Scale: Systems and Algorithms

автор: Max E

Nov 12, 2018

Assignments need to be updated, but the material is solid!

автор: Guruswamy S

May 29, 2018

Very wide and fundamentally robust introduction.

автор: Gokhan C

May 28, 2016

The assignments are really what make this course stand out.

автор: Itai S

Nov 14, 2015

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

автор: Anish C

Jan 17, 2018

Thanks for this course.True Parallel computing example would have made it even more awesome .

автор: Anish M

Sep 24, 2015

great exercises and assignments. The course is involving.

автор: suyang z

Oct 15, 2015

good for people who have some experience in python and SQL

автор: Paulo S S S

Feb 06, 2016

Very relevant if you want to understand the theories behind data systems and algorithms. I consider it a bit time consuming but completely worth taking into consideration the amount of topics it covers.

автор: Sebastian O M

Nov 21, 2015

100% Recomendado

автор: Mahmoud M

Jan 18, 2016

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

автор: Vaibhav G

Jun 16, 2017

Awesome content.

автор: Zahid P

Nov 14, 2015

While I haven't been able to keep up and submit most assignments, the material seems highly relevant and good to know. The videos are helpful and assignments provide good practice.

Note: I am currently a software engineer and have an undergrad degree in Industrial Engineering (so I have some exposure to the concepts in the course).

автор: Felipe G

Mar 07, 2016

great course! ... congratulations.

автор: BI C

Jan 21, 2016

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

автор: kazım s

Sep 10, 2017

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

автор: Kevin R

Nov 12, 2015

Great exercises one can learn alot from.

автор: Batt J

Apr 14, 2018

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

автор: Leonid G

Jun 20, 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.

автор: Menghe L

Jun 08, 2017

great for learner

автор: Roland P

Jul 27, 2017

Great intro into wider aspects

автор: Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

автор: Daniel A

Nov 21, 2015

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

автор: Bruno F S

Feb 15, 2016

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

автор: NothingElse

Nov 06, 2015

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

автор: Kairsten F

Sep 22, 2016

This class assumes intermediate-advanced experience coding in Python, so if you are new, you are likely to struggle a lot. The SQL part, however, was taught from a base-level understanding of almost 0 and is much easier for a beginner.