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學生對 华盛顿大学 提供的 大规模数据处理:系统与算法 的評價和反饋

4.3
694 個評分
151 個審閱

課程概述

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...

熱門審閱

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.

篩選依據:

126 - 大规模数据处理:系统与算法 的 147 個評論(共 147 個)

創建者 Hannah M

Nov 19, 2015

It was really frustrating that the autograder and assignment instructions didn't match. This course has been around too long for that big of a mistake. The lectures were the redeeming factor. They were interesting and presented the subject matter in a concise way.

創建者 Coen J

Feb 22, 2016

Good focus on ideas vs principles. 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. 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 does one organise data, get to know completely new data, understand possible value? i.e. how to start a data science project if all there is is unorganised data and the wish to do 'something' with it.

創建者 Hongmin C

Mar 20, 2016

The grader is horrible leaving you with such brief error messages. You never know what is wrong with your code. The forum is not as useful as I expected nor as it is in other Coursera courses. You know, few classmates. The videos provide you with tons of information, but not much of them are well-organized. I often felt tired and confused since these long videos seldom got to the point.

創建者 Ganeshwara H H P

May 06, 2016

1. The title is misleading since "at scale" led people to think that large scale data processing platform such as spark and nosql databases will be central to the course right from the start

2. The assignments need a lot of improvements. I am not happy with how we're often only required to submit a single number as an answer. The biggest problem is that this way the grader won't be able to give you meaningful feedback / hint of where you might be wrong. A grader that only tells you "your answer is incorrect" does little to help you learn from mistakes.

3. I think assignment 2 can be paced differently - now it feels that we have a bunch of very easy parts (a-g) that is not very interesting, where the last three are significantly harder.

創建者 Marcio G

Jan 07, 2017

This course is quite outdated. I didn't learn much beyond what I already knew before I started. The Spark courses from edX are way better than these. Hopefully "Big Data Analysis with Scala and Spark" from the "École Polytechnique Fédérale de Lausanne" (also from Coursera) is good (I know their Scala courses, which are taught by Martin Odersky, are quite good).

There are very few quizzes between lectures and the assignments are not very challenging.

Many of the videos, specially the ones at the end were extremely rushed over. They serve more as a review if you know the subject, otherwise I don't think most people will get much from them.

The audio isn't very good for most of the lectures, many having an very annoying chirping sound (from when you leave an old flip phone near a computer... "teh-teh-teh teh-teh-teh teh-teh-teh teh-tehhhhhh....". Gosh, I haven't heard this sound in maybe over five years...).

創建者 Ben K

May 27, 2016

This course probably deserves 3-4 stars in a better, maintained form, but the entire specialization is not maintained, the lectures have no production values. Basically, it's a money pit that Coursera is keeping up cynically. It's a real shame because the syllabus correctly addresses a gap in most data scientists' skills.

創建者 Jana E

Dec 07, 2017

Quite interesting subjects, but video material is not of high quality and many mistakes are not changed in later sessions but altered via a text in the screen of a note on the next sheet.

創建者 Supharerk T

Mar 24, 2016

The exercises are fun and challenging. 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.

創建者 Ian P

Jan 23, 2016

This course, which sounds promising in title and syllabus, has many glaring deficiencies. In fact, I feel terrible if anyone ponied up $100+ for it. It roughly covers some concepts of data science, but never at scale, and never very clearly. My background is a science Ph.D. with a lot of computational science experience.

The lectures: Clearly poorly planned. Bill Howe has some knowledge about databases, but little skill in communicating it. The organizational structure leaves much to be desired. Much of the lectures are broad-brush and halting, simultaneously being too detailed as times and not broad enough at other times. Technical portions are marked by a number of errors in speaking and on the slides, as well as a lot of hesitation and jargon. It's as if he neither thought about the structure of what he wanted to say or a script of what he might say prior to recoding the session. Phoning in it is an apt description.

The Assignments: The first assignment with Twitter was fun and interesting and gets the course 2 stars instead of one. The lectures prior to this will not prepare you for the assignment though, so might as well just skip them and do it on your own. The SQL assignment followed a set of lectures in which no proper discussion of SQL was ever given. The last assignment on Map-Reduce is acceptable although a number of errors in the homeworks are still uncorrected long after the first offering of this course. The autograder's idea of helpful feedback is similar to "Incorrect value. Try again" Week 4 of this course, which contains a vast amount of information has no exercises at all.

Overall, this class is the polar opposite of a quality online course like Andrew Ng's Machine Learning Course. Do the twitter assignment and skip the rest. Lectures are poor and assignments are well below average. If I were at UW, this is not the kind of course I'd want representing my university in a public setting.

創建者 Alon M

May 16, 2017

This course offers a mixed learning experience:

pros:

(1) some lessons offer deep understanding of SQL and MAP REDUCE algorithms.

(2) the HW is challenging and gives you an opportunity to actually implement SQL, DATA MINING, and MAP REDUCE. (con: those areas are big, and this course only slightly touches each)

cons:

(1) the tasks are very hard, simply because they are written bad(!!!!) : there are discrepancies between the written task and what the Automatic grader checks, insufficient documentation, weird way to submit points among the problems, weak help from mentors (if at all). boy, I found myself pulling my hair out.

(2) this course takes it for granted the you already know SQL, and how to work with linux based OS in a virtual box, and of-course: python is a must. If you are new to one or more of those subjects, this might not be the course for you.

(3) the talks offer very little help in solving the tasks. so basically you are on your own and need to search for clues in the web or read out comments from other desperate learners.

創建者 Andre J

Jun 21, 2016

I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments.

I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

創建者 Diego P

Feb 28, 2017

Many mistakes in the slides and poorly defined problems in the assignments have gone uncorrected for over a year. The content is very basic, as would be for an introductory course, but can even serve as a refresher for CS graduates.

創建者 Igor S

Oct 28, 2015

This course left me with mixing feelings. I learned some new things, but overall I don't think that I got understanding of base concepts. Week 4 seems to have disproportionately more material than previous weeks, as though authors tried to Although free, this is course is also offered as a part of paid specialization, and I would be really disappointed if I'd spent money on a course like this.

創建者 Lei Z

Mar 22, 2017

The course is good. But it does not has lecture slides that is better for students to understand.

創建者 Lloney M

Nov 03, 2017

The course info makes no mention of Python as a prerequisite. Yet the first assignment demands Python knowledge and skills. Without which you can't pass the assignment. Yet the week's lecture is not about Python.

創建者 Catherine Z

Feb 19, 2016

Poorly designed videos, too long and confused

創建者 FilippoV

Sep 19, 2017

very poor!

創建者 Andreea D L

Feb 06, 2016

Th first three classes are very 'thin' in content and the assignments are easy. The fourth class is basically optional and it has TONS of content. What's the point?

創建者 Aitor G R

Feb 20, 2017

Outdated, unintelligibly exercises, terrible lectures.

創建者 tuzunkan

Dec 06, 2015

Lost in details. Professionals(btw I hold a MSc degree in Computer Engineering) cannot get anything from this. What is the point of writing frequency.pl where there is a hist() function in R? 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. I recommend the instructor check ESSEC Business School for analytics subject to better comprehend the Coursera and its goals.

創建者 SHERRY W

Mar 27, 2017

This course totally reminds me of some courses back in college: unorganized material and the assignments are unrelated to the tutorial. The assignments themselves seem to be very helpful but the tutorials did no help of achieving these assignments.

I had a hard time following the instructor despite that I've completed all the certificate for python from University of Michigan. I'm aware of my background of python is still not strong enough so I thought it's probably just me not able to learn it fast enough.

But then I watched the tutorial about SQL. As a data architect / ETL developer, SQL is something I'm familiar with and use it everyday and then I realized that the instructor couldn't explain a nested query well. The reason I was able to understand about the SQL part is because I already know.

創建者 Jan M

Jun 17, 2019

The course material is ok, but the support and assignment grading is horrible - I spend several hours just battling with grader after having the results ready. Definitely wouldn't recommend this course to anyone. I subscribed for the whole Specialization and completed Course 1 and 2. Unfortunately Course 2 finishes with Peer Graded Assignment - I submitted it with a few weeks to go before my subscription expires but there was no one to grade it so once my subscription ended I didn't get the certificate despite completing the whole second course as well and I lost access to all my submissions and the Course material even though I have already paid for it.