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

4.3
698 notes
152 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

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 sur 148 Examens pour Data Manipulation at Scale: Systems and Algorithms

par Max E

Nov 12, 2018

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

par Anish C

Jan 17, 2018

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

par Guruswamy S

May 29, 2018

Very wide and fundamentally robust introduction.

par Gokhan C

May 28, 2016

The assignments are really what make this course stand out.

par Itai S

Nov 14, 2015

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

par Anish M

Sep 24, 2015

great exercises and assignments. The course is involving.

par Sebastian O M

Nov 21, 2015

100% Recomendado

par 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.

par Vaibhav G

Jun 16, 2017

Awesome content.

par 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).

par Felipe G

Mar 07, 2016

great course! ... congratulations.

par BI C

Jan 21, 2016

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

par Vijai K S

Jan 20, 2016

Going through the content really scares someone like me. At the same time, i feel that the challenge in doing the assignments will only help me improve well. I would suggest beginners to stay away and get a hold of the basics before jumping into the course.

par Shibaji M

Sep 17, 2015

This is a great course

par Sofia C

Nov 15, 2016

The contents were very relevant and more geared to those with some experience already. The assignments are worth doing. The only problem is that some of the assignments have errors which are only listed in pinned posts in the forum (with a link to a ticket but nothing's been done about it). Still, learned a lot so the on the whole would recommend it.

par Dimitrios K

Jan 24, 2016

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.

par Maria P

Oct 28, 2015

4.5 because it was very difficult to access the optional assignments and there was effort expended on reformatting them since the last offering of the course. Otherwise it's an excellent course and I've already been recommending it.

par francisco y

Jan 19, 2016

Great course!

par Dan S R

May 25, 2017

Great work, very satisfied!!

par Miao J

Dec 25, 2015

Great course. Very helpful!

par 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.

par Do H L

Jun 30, 2016

I had a lot, really, a lot of fun in this course.

The first week was really awesome! Although the debugging was very tedious and time-consuming, I felt a great deal of achievement first hand dealing with Twitter data and coded up text analytics algorithms from scratch.

Really a great introductory course to data science! Highly recommend because it's really fun. However, a great amount of comfort with coding and patience for working through ambiguous bug messages will be essential to completing this course :)

par Kenneth P

Dec 06, 2015

Course is well structured, moving on with the lessons is a build up of techniques and concepts. Delivery of the course material is well paced and gives all the required information to grasp the concepts.

par Korbinian K

Nov 07, 2016

Really useful course when you want to learn about big data management and need a starter. It is however definitely recommended to have some programming experience and knowledge about bash/command line. The course met all my expectations, but to make it perfect I would have wished for an extra exercise using Pig or Spark.

par Wonjun L

Mar 06, 2016

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