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Kursteilnehmer-Bewertung und -Feedback für Data Manipulation at Scale: Systems and Algorithms von University of Washington

4.3
697 Bewertungen
152 Bewertungen

Über den Kurs

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...

Top-Bewertungen

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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26 - 50 von 148 Bewertungen für Data Manipulation at Scale: Systems and Algorithms

von Gokhan C

May 28, 2016

The assignments are really what make this course stand out.

von Artur S

Nov 08, 2015

Brilliant course with amazing test tasks!

von Roberto S

Jun 13, 2017

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

von Hernan A

Jan 11, 2016

Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice.

The lessons are well designed and clearly conveyed.

von Shambhu R

Jul 27, 2016

Very nice course!

von Jun Q

Aug 08, 2016

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

von Anish M

Sep 24, 2015

great exercises and assignments. The course is involving.

von suyang z

Oct 15, 2015

good for people who have some experience in python and SQL

von 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.

von 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.

von Jeffery L T

Jan 27, 2017

Great course!

von Bruno F S

Feb 15, 2016

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

von 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.

von Itai S

Nov 14, 2015

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

von kazım s

Sep 10, 2017

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

von NothingElse

Nov 06, 2015

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

von Wonjun L

Mar 06, 2016

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

von Ivan S

May 11, 2017

Nice !

von Kevin R

Nov 12, 2015

Great exercises one can learn alot from.

von Menghe L

Jun 08, 2017

great for learner

von 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 :)

von Batt J

Apr 14, 2018

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

von 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.

von Roland P

Jul 27, 2017

Great intro into wider aspects

von Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.