Data Manipulation at Scale: Systems and Algorithms
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, …
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Specification: Data Manipulation at Scale: Systems and Algorithms
51 reviews for Data Manipulation at Scale: Systems and Algorithms
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Vijaya S K K –
Mandar B –
Course gives you good overview on different important data science technologies. Hands on labs are important to get the grip on concepts.
Ahmed M E E –
Very good and informative course for data scientists and data engineers
Daniel W –
For me, a really nice combination of 1. a theoretical overview of database and data processing concepts, MapReduce and the most important implementations of the various concepts (SQL and NoSQL databases), 2. practical application of these concepts in real world programming exercises. I like the way Bill explains, and I like the exercises however, to complete those, you need to be ready to learn the technology on your own, the lectures are NOT about learning the technology (Python programming etc.) to do the exercises. For me, that’s fine, but for people who have little or no programming experience it might be frustrating. So, if you like the combined approach of this course, I can really recommend it!
Dylan T –
The course is interesting and well made. Compared the the other two, I found the first assignment quite difficult and required quite a bit of time to complete. Introducing SQL through relational algebra seemed relevant to me, and made the formulation of SQL queries very natural. The section about map reduce may appear difficult to process first but as the student has to go through (and beyond in one case) the examples presented in the course. In the end, I found the assignment very useful in putting thing in place. I received full grade but still have to go through week 4, maybe a small quiz in the end to test our understanding of the different concept would have been handy.
Ivan S –
Kenneth H M N –
Overall a good course, with teachings bit into very manageable lengths of time. My biggest grievance is that your submission has to be in encoded in a particular format (utf 8) if memory serves. So you may have to resave your .txt files if you try to do all the programming on a windows laptop. This may be obvious to some, but it took me a little to figure out.
Alon M –
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.
Joris D –
The course gives a good introduction into handling large amounts of data, the problems it poses, and an overview of the available solutions. Towards the end of the course, it started to feel a bit less polished and more rushed, though
Dan S R –
Great work, very satisfied!!
Daniel V –
If you don’t know Pythonl, don’t take this course.
Menghe L –
great for learner
Roberto S –
Very good introduction to the topic; requires quite an effort to complete the assignments, but the outcome is worth it.
Vaibhav G –
Leonid G –
Comprehensive and clear explanation of theory and interlinks of the up to date tools, languages, tendencies. Kudos and thanks to Bill Howe. Highly recommended.
Timothy R –
Very good introduction to relational algebra and map reduce. Also helped scratch up on some python and SQL.
Chuck C –
Great content. The questions are academic and sometimes hard to understand the desired outcome
Roland P –
Great intro into wider aspects
little touch of everything, it’s good intro for non tech, but way too shallow for a student from tech background
Jim S –
The theory and relational algebra is a little heavy for me (I am very much a practitioner). That said, Prof Howe is *excellent* in is presentation. Very clear and easy to follow. Sometimes beats a dead horse (Map Reduce) and as a result, you definitely know what he’s getting after!
Dany M –
There are times where a user without a very fast connection will struggle to set up, the virtual machine is impossible to get for them. Between the internet and the forum the needed information is there but it makes the first assignment take 15+ hours. A little help on the assignment page on how to get going on Windows would save a lot of people some time. Apart from this ist is quite good. The automatic grading is amazing and the videos quite nice.
valery n –
Excelente curso, contenidos muy completos; sin embargo, deberian actualizar las instrucciones de cada Assignment con las correcciones ya descritas en los foros, para algunos es dificil encontrar estas correcciones fuera del enunciado. Por lo demas, gracias por esta oportunidad, por abrir las puertas de una universidad tan importante a otros estudiantes que jamas podrian asistir a su campus.
kaz1m s –
If you want to head into Data Science, this is a nice course that will help you.
Sreeparna M –
The course is good. It definitely gives a broad overview of the topics. It’s presented in an interesting manner and I would definitely go in depth about these topics. Although, it would have been more helpful had there been more graded quizzes and assignments.
Lloney M –
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.
Damien L –
Excellent course. I just sad about the absence of any assignment or even quiz in Week 4..
Gregory C –
Very good class the assignments were pretty uninteresting, though.
Jana E –
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.
James S –
The material is good. If you can get past the instructor’s mumbling and rapid speaking then you’ll be okay.
Anish C –
Thanks for this course.True Parallel computing example would have made it even more awesome .
Achal K –
A very good introduction to skills needed for applying data science ideas on large scale data problems.
Dwayne B –
Good information but lectures were poorly produced and unedited and exercise instructions were blatantly incorrect several times.
Batt J –
Very good course for understanding the underlying logic behind emerging big data technologies
Guruswamy S –
Very wide and fundamentally robust introduction.
Max E –
Assignments need to be updated, but the material is solid!
Yu Heng H –
It’s pretty tough in assignments especially when there are mistakes in the given description, but I do learn the basic concepts of relational algorithm and MapReduce from them.
Dongying Z –
Pros: The content of the course is great. 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? Usage of Python 2.7 is also somewhat out of date since it’s 2019. Biggest con: The way the lecturer talks is more than annoying. Full of stop words like ‘fine’, ‘ok’, with occasionally correcting mistakes on slides or diverging to other topics there are only a few minutes each video and how much time did the lecturer wasted on talking nonsense? It’s fine if he talks like that on some 90 min long classes but it’s on Coursera. Sometimes I just skimmed the slides rather than listen to him.
Jan M –
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.
Raheel H –
A great way to start, and become familiar with the nature, requirements & analytics of today’s data.
Killdary A d S –
Excelente curso, conteudo facil de entender e realmente desafiador. Recomendo para quem quer entender como e realizado a extracao e analise de dados nao estruturados.
Desiree D –
Hard but awesome
Bingcheng L –
Very very very tough for me. took me 3 months to finish. But I learned so much from this course.
Minh T –
Great for students.
The lecture covers a broad range of materials, from complexity of algorithm to map reduced formulation. The assignments are challenging and up to date. However, I would prefer the lecture to be more technical and coherent.
Muhammad A I –
Love the the concept of “learning abstraction rather than tool”.
Muhammad Z H –
learnt a lot
Karol O –
Engaging problemset makes sure that you will get your hands dirty with data. And that is great! Definitely worth your time.
Anne Marie T –
I don’t recommend this course. Even though I learnt a few things, it has not been maintained for a while. It’s a pain to fix errors in assignments as automatically corrected solutions may not match the actual correct answers to the questions. And the material is not up to date. MapReduce is useful but has been superseded by Spark a few years ago already.
Maria K –
Interesting but outdated
Andrea R –
A couple of comments in the forums were very old and it seemed nobody had been checking the course for a long time.