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Data Manipulation at Scale: Systems and Algorithms(으)로 돌아가기

워싱턴 대학교의 Data Manipulation at Scale: Systems and Algorithms 학습자 리뷰 및 피드백

757개의 평가
168개의 리뷰

강좌 소개

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...

최상위 리뷰


2016년 1월 10일

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.


2016년 5월 27일

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.

필터링 기준:

Data Manipulation at Scale: Systems and Algorithms의 164개 리뷰 중 26~50

교육 기관: Killdary A d S

2019년 7월 4일

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.

교육 기관: Leonid G

2017년 6월 20일

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

Highly recommended.

교육 기관: Mahmoud M

2016년 1월 18일

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

교육 기관: Jun Q

2016년 8월 8일

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

교육 기관: Karol O

2019년 12월 22일

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

교육 기관: Roberto S

2017년 6월 13일

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

교육 기관: Daniella B

2016년 4월 21일

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

교육 기관: Itai S

2015년 11월 14일

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

교육 기관: Achal K

2018년 2월 5일

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

교육 기관: Raheel H

2019년 7월 1일

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

교육 기관: Bingcheng L

2019년 8월 4일

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

But I learned so much from this course.

교육 기관: Padam J T

2021년 8월 7일

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

교육 기관: Batt J

2018년 4월 14일

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

교육 기관: Edwin A P V

2020년 12월 12일

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

교육 기관: Usman

2016년 12월 27일

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

교육 기관: BI C

2016년 1월 20일

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

교육 기관: Kazım S

2017년 9월 10일

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

교육 기관: Daniel A

2015년 11월 21일

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

교육 기관: Wonjun L

2016년 3월 6일

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

교육 기관: Ahmed A

2017년 4월 14일

Very good and informative course for data scientists and data engineers

교육 기관: Asier

2015년 11월 20일

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

교육 기관: Bruno F S

2016년 2월 15일

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

교육 기관: Muhammad A I

2019년 9월 10일

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

교육 기관: Gokhan C

2016년 5월 28일

The assignments are really what make this course stand out.

교육 기관: NothingElse

2015년 11월 5일

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