Data Manipulation at Scale
Systems and Algorithms
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Rating | 3.9★ based on 155 ratings |
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Length | 5 weeks |
Effort | 4 weeks of study, 6-8 hours/week |
Starts | Jun 26 (40 weeks ago) |
Cost | $79 |
From | University of Washington via Coursera |
Instructor | Bill Howe |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Data Analysis Software Development |
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What people are saying
data science
A very good introduction to skills needed for applying data science ideas on large scale data problems.
If you want to head into Data Science, this is a nice course that will help you.
Ultimately the course showed me what I need to learn next to get into Data Science but the first course hasn't given me confidence that the rest of the specialization will be worth the money.
This is a quite wonderful course for large-scale data science.
Really a great introductory course to data science!
Found the assignments were 'very loosely' aligned with the lecture material and had poorly formed problems in places.Lectures were reasonably good but not quite up to the standard set with other U of W Data Science courses or other University Data Science / Machine Learning courses I have taken.
However, the lecture are not related to the exercises and are very hard to follow (I think it's the same thing as Brian's class in Johns Hopkins' data science course) If you are taking Bill Howe's class, just go straight to those exercises and skip lectures.
If you are interested in data science then this course is the right one.
Not very deep and not always structured, but rather focused on the technology principles instead of the data principles.I think that this specialisation suffers the same problem most data science/mining/analytics courses suffer: it ignores the non-technical starting point: scientific or business relevance.
how to start a data science project if all there is is unorganised data and the wish to do 'something' with it.
It roughly covers some concepts of data science, but never at scale, and never very clearly.
If the instructor is trying to teach us how to program in any language, then I can assure you the data science class is not the right place.
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very good
Very good class - the assignments were pretty uninteresting, though.
Very good introduction to the topic; requires quite an effort to complete the assignments, but the outcome is worth it.
The course was very good - especially the map-reduce part I found very well explained and inspirational.
Very good course!
Very good course, but lectures could be more tuned onto the home assignments.
Teacher is very good.
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big data
Very good course for understanding the underlying logic behind emerging big data technologies Good information but lectures were poorly produced and unedited and exercise instructions were blatantly incorrect several times.
Overall I enjoyed this course and got a broad overview of the various technologies used in big data analysis.
Really useful course when you want to learn about big data management and need a starter.
Good introduction to Big Data systems.
- great and very useful overview of concepts important in big data that does not get bogged down in random details- interesting and sufficiently challenging assignments Good focus on ideas vs principles.
Great course for those who want to know more about big data analysis.
Excellent overview of the Big Data field and its relation to eScience.
It introduces fundamentals of big data technologies to those who are new to this field, with some hands-on practices.Cons: The instructions of assignments are not always clear - they are corrected in the discussion forum but why not updating in the assignment page?
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relational algebra
The theory and relational algebra is a little heavy for me (I am very much a practitioner).
The questions are academic and sometimes hard to understand the desired outcome Very good introduction to relational algebra and map reduce.
The focus on relational algebra is a great way to look at data manipulation in general.
Unfortunately, relational algebra is explained quite well, but not really applied after that.
This could be a great course if it really taught to constantly think in terms of relational algebra.Okay-ish explanations of databases and hadoop.
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problem sets
In contrast, a Python data science course on another MOOC platform has 4 times as much content with practice exercises after every video, mid and final exams, weekly problem sets as well as readings.
The problem sets were thought-provoking and really taught me a lot.
See Machine Learning by Andrew Ng to see how to design perfect, easy to operate and submit problem sets.
Interesting problem sets.
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Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
AD, Data Science $47k
Associate Data Science Supervisor $55k
Science writer / data analyst $63k
Genomic Data Science Programmer $75k
Volunteer Director of Data Science $78k
Expert Data Science Supervisor $79k
Supervisor 1 Data Science Supervisor $91k
Guest Director of Data Science $101k
Data Science Architect $105k
Head of Data Science $131k
Assistant Director 1 of Data Science $133k
Owner Director of Data Science $149k
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Rating | 3.9★ based on 155 ratings |
---|---|
Length | 5 weeks |
Effort | 4 weeks of study, 6-8 hours/week |
Starts | Jun 26 (40 weeks ago) |
Cost | $79 |
From | University of Washington via Coursera |
Instructor | Bill Howe |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Data Analysis Software Development |
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