Getting and Cleaning Data

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5/10 stars
based on  31 reviews
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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

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Humanities
Sciences & Technology
4721 reviews

Course Description

Learn how to gather, clean, and manage data from a variety of sources. This is the third course in the Johns Hopkins Data Science Specialization.
Reviews 5/10 stars
31 Reviews for Getting and Cleaning Data

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Purnesh Tripathi profile image
Purnesh Tripathi profile image
1/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 4 months ago
I had elegant prior experience in R programming but the course is too poorly structured. I was often left wondering, what the question is trying to ask due to insufficient detail, rather than solving the actual problem. The major problem with this course is, its awfully documented, you won't find any relevance of what would be taught, and the assignments provided to you. To make the situation even worse, THE TEACHERS SHOW ABSOLUTELY NO SUPPORT on the discussion forums.
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2/10 starsTaking Now
2 years, 10 months ago
This is a very very badly structured course. Similar to R programming, there is a fundamental disconnect between course materials/videos which is more like a spoken man page. This is how not to put together a course.
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4/10 starsTaking Now
3 years, 1 month ago
Coursera: very good the course and instructors combined: low quality. There is a complete disconnect between what is taught in the videos and what is evaluated. You end up with wacky things like introducing new material in quizzes! The idea that to be a data scientist you must be a "hacker" appears to me to be a cop out for teach a good and well structured course.
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2/10 starsCompleted
3 years, 4 months ago
There is a complete disconnect between what is taught and what is expected in the project and tests. The course is pretty bad. I was considering doing the specialization in Data Science and this course is making me re-think this goal. I understand that you need to be good at 'hacking' to be a good data scientist, but if that's the case then what's the point of paying money to have to Google everything.
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2/10 starsTaking Now
3 years, 8 months ago
Wow, wish i researched it here before buying the whole specialization! While we don't need to be spoon fed, agree that it is annoying that it's mostly reading of slides instead of at least some interactive work within the terminal. I like coursera but rating it low here since they have a bad refund policy for specializations, the first module was easy and then more advanced model become harder while instruction does not improve but coursera refuses to do a partial refund!
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Nirav Desai profile image
Nirav Desai profile image
10/10 starsCompleted
  • 9 reviews
  • 9 completed
3 years, 9 months ago
I found the learning curve on this course to be very steep. I had to struggle a bit on the project but the discussion forums and a little online search was able to help me get to the right answers. I find the skills learned to be very useful in the subsequent courses on exploratory data analysis, statistical inference and regression models. If you are confident using plyr and dplyr libraries, it gives you more time to focus on the important parts of the code during analysis.
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2/10 starsTaking Now
3 years, 11 months ago
Too poorly structured. It is so half assed I was baffled at how y I could not figure out what the lecturer was trying to teach during lectures, and baffled again after encountering a project with so little for instructions.
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4/10 starsTaking Now
4 years, 2 months ago
The instructor literally flies through the slides, missing out on key important points. You'd rather get a good book because you won't get much out of the slides or videos... The project and quizzes are poorly worded, leading to confusion, as evident by the questions on the Forums. Finally, the Community TA is borderline condescending. This course could be so much better. Considering switching to EdX...
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6/10 starsDropped
4 years, 1 month ago
Horrible - instruction and content are lame -just the worst I have ever seen. The community TAs are ignorant of maths . This sort of quality for a JHU class is shocking!
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4/10 starsCompleted
4 years, 1 month ago
The subject of the course are appropriate, but the quality of the lessons are not very good: you must read external material to have a good understanding of the subjects and look for practical exercises outside. More practical exercises are needed in the course (swirl assessment are not enough because it only covers a small part of the subjects) Course project has a messy specification: it is not very difficult , but it takes time to have a clear idea about what is right, in order peer review dont mark you down.
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10/10 starsCompleted
4 years, 1 month ago
I loved this course. It offers great experience in solving real world issues with data. Data is never neatly structured and combined when you get it. This course shows you different ways in how to tackle that. Of course, this doesn't come easy. Just like in the real world you need to learn to read help pages of several R functions and packages to be able to solve the puzzles from quizzes and the peer assignment. But I really learned a lot of new stuff and didn't think it was overly difficult. I really recommend it!
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4/10 starsTaking Now
  • 1 review
  • 0 completed
4 years, 2 months ago
This course is worth taking simply because it actually offers a better summary of the R programming language than the previous course in the sequence, "The R Programming Language," especially in the Week 3 lectures. On the other hand, for a course on "getting and cleaning data," it misses the mark, simply because it doesn't confront the single most common issue in getting and cleaning data—the need to perform operations on variable data to get data points into the correct format for computation. I work with data sets on a daily basis, and by far, more headaches are introduced by data formatting when using or combining data sets than by any other issue. Differences in date representation, numeric formatting, string formatting, slight nuanced differences in the meanings of variables, etc. The task that I spend most of my time on when working with data is in *processing* sets(s) of variable data so that they're in the correct ... This course is worth taking simply because it actually offers a better summary of the R programming language than the previous course in the sequence, "The R Programming Language," especially in the Week 3 lectures. On the other hand, for a course on "getting and cleaning data," it misses the mark, simply because it doesn't confront the single most common issue in getting and cleaning data—the need to perform operations on variable data to get data points into the correct format for computation. I work with data sets on a daily basis, and by far, more headaches are introduced by data formatting when using or combining data sets than by any other issue. Differences in date representation, numeric formatting, string formatting, slight nuanced differences in the meanings of variables, etc. The task that I spend most of my time on when working with data is in *processing* sets(s) of variable data so that they're in the correct format to be merged with sister data sets or to be fed into program X or system Y. Instead, the central project for this course focuses most heavily on variable names (i.e. column headers) and writing codebooks. The actual data acquisition is easy (download a ZIP file), as is the "cleaning" (join two data sets with identical numbers of columns that have identical meanings and formats). From my money, this isn't a course on "getting and cleaning data" so much as it is a course on "navigating/summarizing an already imported data set in R" and "documenting your dataset." That can be useful in its own right, but I found the emphasis on codebooks in particular to be vexing. Yes, documenting what your variables mean is important, but that's at least as much a technical writing task as it is a coding/computing task, if not moreso. I wanted more theory on using R when you have to combine four disparate data sets into a single, coherent whole, then transform half the values to feed the whole into ane existing system. I could have done with less on joining already structurally identical data sets, summarizing them in various ways, and writing plain-English descriptions of their contents.
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Michael Devereux profile image
Michael Devereux profile image
8/10 starsTaking Now
  • 5 reviews
  • 4 completed
4 years, 5 months ago
This course was insightful, in the sense that I have never really considered the "messiness" of real data that was not cleaned up and filtered extensively by the guardians of financial data (Bloomberg and Thomson Reuters Datastream come to mind). It was a fairly broad brush class that covered a whole variety of different methods to obtain data via internet, SQL, pictures etc but probably the best takeaway of the course was the introduction to the concept of "tidy data" as espoused by Hadley Wickham. I feel that that is a standard well worth keeping in mind as we try and navigate massive data sets - it is too easy to mess around so much with data that it makes no sense to anyone else, even a fellow professional/expert!
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4/10 starsTaking Now
4 years, 5 months ago
I understand at this level, learning is strictly the responsibility of the individual, especially when it comes to programming. Now having said that, this course is a mess of wishy washy disjoint collection of rambling from the instructors. It gets worse as the weeks progress. Lecture, quizzes, projects seem like they are pulled from random place for the convenience of the creator, not to make learning an efficient and effective process. If I have to spend almost all of my time on stackoverflow.com and other resources on the web, then it makes me question the value of this class.
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10/10 starsTaking Now
4 years, 5 months ago
I am shocking to see these reviews that gave this course only one star and criticize it for its quality. I have finished 3 classes in this series. The content is great. The project is great. Yes. there is a gap between the course content and quiz/project. But we can fill the gap through google search or other ways. I can not expect more than what I got from such 4-week classes.
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2/10 starsTaking Now
5 years, 3 months ago
One of the worst courses I've take on Coursera. There's no support from the instructors or course staff on the forum at all. The material is outdated and disconnected with the homework. I really want to like this course but I'm finding it very hard to.
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2/10 starsCompleted
5 years, 3 months ago
One of the worst courses I've take on Coursera. There's no support from the instructors or course staff on the forum at all. The material is outdated and disconnected with the homework. I really want to like this course but I'm finding it very hard to.
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Dennis Meier profile image
Dennis Meier profile image
1/10 starsCompleted
  • 2 reviews
  • 1 completed
5 years, 3 months ago
Having completed R Programming in this course series, I was prepared for the incoherence of this course. I have not been disappointed in the first week: the lectures are largely abstract, only occasionally teaching the methods needed to complete the quiz problems. In general, it seems as though the lecturer and the quiz author worked separately and rarely communicated. I'll complete the course, but I know not to get frustrated that the lectures and notes aren't all that useful. It's better to just dive into the quizzes and Google for the information you need. For example, the final question Quiz 1 instructs the student to use a function that isn't supported under the version of R Studio I am running. I just finished R Programming, so everything is fairly recent.  It is extremely frustrating that the course designers pay so little attention to helping the students get squared away into an environment where learning can occur. Ins... Having completed R Programming in this course series, I was prepared for the incoherence of this course. I have not been disappointed in the first week: the lectures are largely abstract, only occasionally teaching the methods needed to complete the quiz problems. In general, it seems as though the lecturer and the quiz author worked separately and rarely communicated. I'll complete the course, but I know not to get frustrated that the lectures and notes aren't all that useful. It's better to just dive into the quizzes and Google for the information you need. For example, the final question Quiz 1 instructs the student to use a function that isn't supported under the version of R Studio I am running. I just finished R Programming, so everything is fairly recent.  It is extremely frustrating that the course designers pay so little attention to helping the students get squared away into an environment where learning can occur. Instead, you'll spend much of your time scurrying around, trying to figure out why what you see on the lecture notes isn't working on your computer. Here's another example, in week 2, you spend significant amounts of time just figuring out how to get and install R packages before you can answer the questions in the quiz. Why not structure the quizzes so that the learner can immediately apply what was shown in the lectures? Give the learner some sense of accomplishment rather than just this chore of continually troubleshooting R until something works? Yes, we will likely download and install packages in R, but let us actually see something that works before showing us that the world is a complicated place. We already know!Frankly, it just doesn't seem worth it. I don't feel as though I am learning anything that I will use. If I need to learn how to add packages to R, I'll learn how as I need to. There must be a better course out there. I'm done with the John Hopkins series.
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Jeff Winchell profile image
Jeff Winchell profile image
4/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 8 months ago
Given that a quarter to half of the time spent in business intelligence/data science projects is spent on ETL (cleaning data to make it useful to analyze) this course topic could have a wealth of information. I've spent many man years doing such work, but wondered if this class would teach me something new (particularly with regards to getting data from the Internet). Nope. However, there is a caveat in my "easy" rating for the MOOC: As others hear have noted, the assignments/quizzes are extremely poorly worded. Like with the other mini- MOOCs in the Data Science Series, if it weren't for the students (and community TA) on these courses, nearly everyone would give up at a much higher rate than is typical for MOOCs. Either you will spend a MUCH larger time than advertised trying to figure things out on your own (which will give other benefits if you are new to the topic or R, if you have the patience), or you go on to the discussion f... Given that a quarter to half of the time spent in business intelligence/data science projects is spent on ETL (cleaning data to make it useful to analyze) this course topic could have a wealth of information. I've spent many man years doing such work, but wondered if this class would teach me something new (particularly with regards to getting data from the Internet). Nope. However, there is a caveat in my "easy" rating for the MOOC: As others hear have noted, the assignments/quizzes are extremely poorly worded. Like with the other mini- MOOCs in the Data Science Series, if it weren't for the students (and community TA) on these courses, nearly everyone would give up at a much higher rate than is typical for MOOCs. Either you will spend a MUCH larger time than advertised trying to figure things out on your own (which will give other benefits if you are new to the topic or R, if you have the patience), or you go on to the discussion forums because invariably others have the exact same confusion. I have pity for the people who've paid significant money for these courses.
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David Neary profile image
David Neary profile image
7/10 starsCompleted
  • 2 reviews
  • 2 completed
5 years, 2 months ago
I completed this course last June. I also volunteered as a TA for the July offering. Overall I think this a decent course. The video lectures are very informative and you learn alot. The course projects are very hands on and a good learning experience. Some have had complaints about ambiguity in the instructions but this has more to do with leaving the projects open ended rather than being unclear. R programming knowledge is a must for this course along with a computer that can handle cleaning large data sets.
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Ramiro Aznar profile image
Ramiro Aznar profile image
8/10 starsCompleted
  • 27 reviews
  • 26 completed
5 years, 2 months ago
I really like the course. Especially, some tips in order to prepare your data to do further analysis. It would be nice to have better summaries.
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Kuber Chaurasiya profile image
Kuber Chaurasiya profile image
1/10 starsTaking Now
  • 1 review
  • 0 completed
5 years, 4 months ago
I have done other courses on coursera, but this one and few others in this specialization seems to be worst on them. I agree with some of the students above that content is very poor, projects and quizzes are poorly documented, one can't simply interpret what the examiner is trying to ask. the forum is full of such posts.. pathetic course.
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El Ninio profile image
El Ninio profile image
1/10 starsTaking Now
  • 2 reviews
  • 0 completed
5 years, 4 months ago
Caveat Emptor! DO NOT PAY FOR THIS COURSE! This is the 3rd course in the "Data Science" track, and it continues the tradition that we have come to expect of the Johns Hopkins' Stats/DataSci courses, namely, that of being half assed and almost entirely useless. It provides great handouts, but 50 bucks is a huge price to pay for a handout with some slides on it. You maybe able to guess your way through Quizzes, but but good luck on the "project". The project is difficult not because it is a hard problem, it is difficult because the teachers aren't very good at teaching. They do not even bother provide any useful information (god knows why, maybe they're trying to mimic "real world conditions" but in real world you can interact with users... Here you are left at the mercy of google, watching the lectures endlessly which DO NOT cover the topics, or cover them a week after the project is due. I mean REALLY??? you couldn't even be bothered... Caveat Emptor! DO NOT PAY FOR THIS COURSE! This is the 3rd course in the "Data Science" track, and it continues the tradition that we have come to expect of the Johns Hopkins' Stats/DataSci courses, namely, that of being half assed and almost entirely useless. It provides great handouts, but 50 bucks is a huge price to pay for a handout with some slides on it. You maybe able to guess your way through Quizzes, but but good luck on the "project". The project is difficult not because it is a hard problem, it is difficult because the teachers aren't very good at teaching. They do not even bother provide any useful information (god knows why, maybe they're trying to mimic "real world conditions" but in real world you can interact with users... Here you are left at the mercy of google, watching the lectures endlessly which DO NOT cover the topics, or cover them a week after the project is due. I mean REALLY??? you couldn't even be bothered, in the second iteration of the course, to at least mention that related information is in the FOURTH week's lectures? This shows an attitude which is disrespectful towards the learners and which treats them as idiots who are there for the amusement of these "gods of stats'... Well gods they maybe in the academy but they're pretty mediocre teachers. And this course is about teaching not about stats theory. I am going to give this course ONE STAR. Only because the TAs are so heroically awesome and helpful. I hope coursera shares some money with them because they are the ones basically making people understand. The teachers are NOWHERE to be found on the forums. Obviously they are too busy doing important things.
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Greg Hamel profile image
Greg Hamel profile image
6/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 5 months ago
Getting and cleaning data is the third course in the first wave of John Hopkins’s data science specialization track on Coursera. It is recommended that you take this course after taking the data scientist's toolkit and R programming courses. The title of the course pretty well sums up the content: the entire class is about loading data into R and cleaning it up so that it can be used of data analysis. You'll learn how to load various data formats into R, such as json, xml, csv, excel files and get data from other sources like MySQL and web APIs. The course also discusses subsetting data, adding variables, merging data, regular expressions and working with dates. This course is a good summary of many of the things that are useful to know when trying to access and prepare data for analysis. Similar to R programming, it suffers from overuse of static slides with voice-overs, a lack of instructor face time and a lack of interactive conte... Getting and cleaning data is the third course in the first wave of John Hopkins’s data science specialization track on Coursera. It is recommended that you take this course after taking the data scientist's toolkit and R programming courses. The title of the course pretty well sums up the content: the entire class is about loading data into R and cleaning it up so that it can be used of data analysis. You'll learn how to load various data formats into R, such as json, xml, csv, excel files and get data from other sources like MySQL and web APIs. The course also discusses subsetting data, adding variables, merging data, regular expressions and working with dates. This course is a good summary of many of the things that are useful to know when trying to access and prepare data for analysis. Similar to R programming, it suffers from overuse of static slides with voice-overs, a lack of instructor face time and a lack of interactive content or in-lecture quizzes to help you learn and retain as you go along. You'll be introduced to many R packages and syntax that you probably won't remember after a week or two, but you'll be exposed to many common data formats so that you can refer back to the course materials or other web resources to deal with them in the future.
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Laurie Blome profile image
Laurie Blome profile image
1/10 starsCompleted
  • 3 reviews
  • 3 completed
5 years, 3 months ago
This course was as equally bad as the rest of the courses in the "Data Science" track through Johns Hopkins University.  While I understand this format of learning cannot be on par with a university degree, I frankly am quite shocked that Coursera is taking payment for these courses from those who sign up for a verified certificate.  There is not even an attempt to engage the learner in the lectures, which consist almost solely  of the instructor (?) reading overheads with a whole lot of "and so's" in between.  The presentation material itself is very poorly organized, rife with mistakes and very important steps in the processes he is trying to teach completely left out.  I did learn, but this was mostly through my own trial and error trying to make sense of the presentation material, the quizzes and the assignments. The discussion boards are chock full of thoroughly confused students trying to help each other out, and this is where ... This course was as equally bad as the rest of the courses in the "Data Science" track through Johns Hopkins University.  While I understand this format of learning cannot be on par with a university degree, I frankly am quite shocked that Coursera is taking payment for these courses from those who sign up for a verified certificate.  There is not even an attempt to engage the learner in the lectures, which consist almost solely  of the instructor (?) reading overheads with a whole lot of "and so's" in between.  The presentation material itself is very poorly organized, rife with mistakes and very important steps in the processes he is trying to teach completely left out.  I did learn, but this was mostly through my own trial and error trying to make sense of the presentation material, the quizzes and the assignments. The discussion boards are chock full of thoroughly confused students trying to help each other out, and this is where the real learning occurred.  With the exception of a few TAs that single-handedly saved the course, there was no professor interaction on the boards, nor was there any attempt to clarify, correct mistakes or otherwise show at least a minimal interest.  Shameful pretty much sums up this effort.
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Richard Taylor profile image
Richard Taylor profile image
1/10 starsCompleted
  • 29 reviews
  • 28 completed
5 years, 4 months ago
This is a horrible course. The material is boring, presented in a confusing way and without any hint of enthusiasm or motivation. The course project is a total letdown, uninteresting and badly worded leading to a total chaos in peer reviewing. The course to me has been a total failure and the way it was presented very unprofessional.
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Bojan Tunguz profile image
Bojan Tunguz profile image
3/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 4 months ago
Very frustrating course. Material is all over the place, and the lectures are moderately interesting at best. No interactive content in lectures. Quizzes EXTREMELY confusedly worded, and you have to guess your way though at least a few questions. No feedback whatsoever on quizzes. The final project counts for 40% of the overall grade. This is a bit excessive to begin with, but even more so considering that its instructions are, again, VERY poorly worded. Most of the class has been spending weeks in the discussion forums trying to figure out what is going on. The course feels VERY lazily assembled, especially compared to the other Coursera courses I've taken. It's a required part of the Data Science specialization, but at this point I am seriously contemplating dropping it and not finishing the specialization. It's not worth all the frustration, time and money required for completion.
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Tamás Nagy profile image
Tamás Nagy profile image
6/10 starsCompleted
  • 7 reviews
  • 7 completed
5 years, 3 months ago
It is an important lecture, but maybe it needs some improvement. Although I understand that the lecturer did not want to explain every data type, some concepts were not very well explained (e.g. I'm still quite unsure about how to handle JSON format).
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Erin K profile image
Erin K profile image
3/10 starsCompleted
  • 3 reviews
  • 3 completed
5 years, 4 months ago
The quizzes in this course were irritating, as I frequently got errors that the professor didn't address. My issues in dealing with these errors would have been alleviated had the professor advocated use of the discussion forums as a good place to discuss issues with the quizzes, errors, software, and more. The instructor didn't even *mention* using the forums for purposes other than Coursera problems. As I found in the Maps Coursera course I took, the discussion forums are a great place to supplement your learning from the course. In a programming course such as this, they could be used to learn tricks and tips and also get help on errors, but since so few students used them (or were likely aware of them at all), they didn't live up to their full promise. The lectures, as stated above, were poorly done and didn't even include working links. As far as the final project, it was as equally as maddening as the quizzes, both for the... The quizzes in this course were irritating, as I frequently got errors that the professor didn't address. My issues in dealing with these errors would have been alleviated had the professor advocated use of the discussion forums as a good place to discuss issues with the quizzes, errors, software, and more. The instructor didn't even *mention* using the forums for purposes other than Coursera problems. As I found in the Maps Coursera course I took, the discussion forums are a great place to supplement your learning from the course. In a programming course such as this, they could be used to learn tricks and tips and also get help on errors, but since so few students used them (or were likely aware of them at all), they didn't live up to their full promise. The lectures, as stated above, were poorly done and didn't even include working links. As far as the final project, it was as equally as maddening as the quizzes, both for the same reasons that the quizzes were, but also in a positive way. The final project pretty fairly replicated what happens in the real world when you are given a disgustingly awful looking data set and are asked to do something with it. I found that even though I hated it as I was working on it, in the end it proved to teach me a lot about how to make my data into a useable set while also learning the importance of explaining the decisions I make along the way, as these can be very arbitrary and will impact analysis on the data in the end. The course did require a lot of R knowledge, and I hadn't taken the earlier R programming course (since no prerequisites were stated…), so I felt slightly overwhelmed, but I came out of the course knowing far more R than when I started, but I didn't learn much of it from the course directly. Most of it was trial and error on my own. This added to the number of hours per week I spent on the course. I suppose this did save me the time of taking that R course, though… I have some additional problems with this course: -At one point the instructor says to create your own mySQL database so that the students don't accidentally take over all the other MySQL databases of the internet and take up valuable space and potentially delete things. This is a sensible thing to say. But the instructor proceeds to NOT include instructions or a link to a place that instructs one in how to do this… How many students are going to make the effort to do it, especially with no guidance? -The instructor didn't even ask for feedback at the end of the course, which I find irritating. Overall I learned: -methods for good variable naming -lots about R programming -manipulating data and reading various forms (csv, xml, sql, etc…) into R -merging data -keeping just the data you need, deleting what you don't need -dealing with null values While the course had frequent frustrating moments, I would say that I did learn a lot, but in order for the course to be more effective, the lectures need to be drastically re-tooled and the discussion forums need to be used to their full potential.
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Hamideh Iraj profile image
Hamideh Iraj profile image
8/10 starsCompleted
  • 70 reviews
  • 60 completed
5 years, 5 months ago
Having passed "computing for data analysis" and "data analysis" both by John Hopkins university , the course was quite easy for me. It is recommended as the third course on data science specialization. However, I think it is better to pass it before R Programming. The course content seems simple but very important esp subsetting. Many of the actions on data preparation in R is done by subsetting. So generally I recommend this course.
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  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.