This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.
The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans
by Kevin M•
This course lacked written material to accompany the videos and the reference books are presented in a much different flow, so you are left to jump through books and posts to get through anything. Having the content packaged and delivered in succinct format is what I was looking for and this did not provide that.
by Sergei Z•
Absolutely terrible learning support. The professor does not supply helpful information what so ever for the assignments. He expects us to go out of our way to look up information on StackOverflow.com in order to solve the problems. His incompetence in actually demonstrating how this works is abhorrent.
by Eklavya S•
This course makes you give up on data science and MOOCs.
Seriously, the content is poorly presented he keeps on speaking , telling 2-3 lines about a function and so on.
I highly recommend stay away from this pathetic specialization.
by Zhenxun Z•
I really like Prof. Brooks's way of teaching. He developed a very good introductory level course. Apart from some talks about data science in a whole, he concentrated on the preparatory work in this field -- data cleaning. Instead of delving into theories, he paid most of his attention to how to make things work by using python. I actually have a background in C, and I was a bit reluctant to learn python at first since C is already strong enough to attack most tasks. However, I have fallen in love with python now, and I think it is a much more suitable language for daily use especially when your projects aren't very large. Among its many merits, the best thing about python is of course its numerous libraries like numpy and pandas which free us from tedious low-level programming. I am quite convinced that I will move to python from now on.
In addition to lectures, I truly recommend you go over extra reading materials. Those articles are very thought provoking. For example, the first one "50 Years of Data Science" totally changed my previous view towards this field. It made me realize that data science is not a simple combination of statistics and machine learning, that it is a distinct way of obtaining new knowledge, and that its advancement shall benefit the whole science society.
About the assignments, those taught in the lecture are not enough and you should refer to python documents and stack overflow. I think knowing how to solve problems and where to find help is more important than solving problems itself, and that's why I consider those assignments well designed.
Finally, thanks to all the efforts made by the teaching staff.
by Fabiano B•
If you are looking for in-depth theory, you may be looking at the wrong place. The videos skim through some fundamentals, and sometimes give you some valuable hints.
But if you are looking for a challenging experience that emulates the real world, this course is definitely for you. The assignments will throw you to the wolves very early. You will have to research way beyond the videos to finish them in a elegant manner. It also encourages you to code in a "pandorable" way, which is a valuable skill.
by Bruno S P P•
My background: Industrial Engineer with a decent programming background (including Python), but rusty with statistics.
My review: The instructors clearly know what they are talking about and explains useful concepts. However, the videos are very short, and some concepts feels rushed. The assignments are pretty challenging, which is a nice thing. The last one in particular is very nice and don't feel fabricated - you actually test an interesting hypothesis based on some data you have to extract and manipulate. To be able to finish the assigments, I had to use Google a lot. It kinda felt like cheating, but the course is pretty clear that you should look in the documentations and ask questions on Stack Overflow.
Include more exercises to practice what was taught in the videos.
Include a solution for each assignment - some questions I got it right, but I am sure my answer was not the most efficient or "pandorable" one. It would be nice to have a benchmark to compare after we pass the assignemnts.
by Günter G•
This course is really tough, especially the assignments, which are never doable in the estimated 3 hours. That is very frustrating when one is experiencing this.
The course material is mainly a book and a few videos. I needed lots of hours studying on my own to tackle the assignments.
Now I got the certificate and when I look back I can say it was really a tough time but I learned a lot.
by Mr. Q A•
The assignments took too long for me to complete .
by Jonathan J•
great course, but the auto grader needs updating
Dreadful course. Instructors saw no value in presenting elements of course that would help learners complete the assignments; rather you are sent off to teach yourself about uncovered techniques needed to complete the assignments. From some of the posts from previous students on GitHub, they resorted to deriving the answer from another means (Excel?) and simply providing the answer as a constant value, in order to receive credit for particular questions. Not exactly sterling knowledge transfer, from instructor to student! This course should be presented as a challenge course to people that have already learned Python Pandas from some other venue. (BTW, Pandas documentation is also dreadful, as of this writing.) This is definitely not the way to learn Python for Data Science if you are a busy professional software engineer. (Wish I had a good recommendation as an alternative.)
The only positive aspect of this course is the challenge to work with defined datasets, to complete specific tasks, during week 3. (This was as much time as I could afford to allocate to this course.)
From a 40+ year software engineer, with doctorate in CS, a part-time instructor at a private university, with a very challenging technology job in a multi-national corporation.
by Jun-Hoe L•
Decided to rate this course after I've gone through all 5 courses in the Speclisation. I originally completed this course in January 2020.
So from someone who has completed this Specialisation, I'd say this 5 courses are not worth it.
Here's how I would rank the courses from best to worst:
1. Social Network Analysis: 4.5 stars
2. Applied Machine Learning: 3.5-4 stars
3. Applied Text Mining: 3.5 stars
4. Intro to Data Science: 2
Note that that worst courses are those handled by Professor Brooks himself. His video lectures tend to very superficial (or once in a while, unnecessarily detailed like going into the backend of matplotlib). The assignments on the other hand, are somewhat challenging and go way beyond the video lectures. And that's why you see many comments asking what's the point of purchasing this course when you spend 95% of the time googling? Which is made worse by the outdated autograder which uses and old panda version, and makes googling harder since you had to revert to outdated code.
My advice: Unless you really want the Specialisation cert, I think you should look elsewhere to learn pandas.
by Marc B•
The assignments are good practice, but the course teaches you nearly nothing. You have to do your own research to figure out how to do them.
There are some very useful Mentors on the forums to help the assignments, and if it were not for them, this course would be unbearably frustrating and useless.
by Michael B•
Video lessons go way too fast and don't actually try to teach you anything. If you're already a wiz at using Python to do data analysis, then you could certainly keep up, but then you wouldn't need the course in the first place. Very poorly paced.
by Walter G•
This is not an introductory course! There is a very large assumption that you already know a lot of about the pandas library, as well as extensive knowledge about dataframes and series.
by Muhammad A•
I would not recommend this course at all. This is for a number of reasons.
The lectures are not really lectures, they are more of a narration of someone else writing code on screen, the intructor just whizzes through what's happening without giving any proper explanation (I cannot stress this enough). The limited explanation provided is just on what's happening on the screen rather than why we're doing it this way compared to any other way. There is also not enough guidance given in the lectures but told to just figure it out and go post on Stack Overflow. Anyone familiar with Stack Overflow should know, they *really* do not like beginners posting repetitive questions - so I find that advice from the instructor really odd.
The courses makes use of Numpy, but gives zero explanation on what Numpy is and why we use it. It just dives into it by using Numpy arrays and expects you to either magically understand it or go learn what/why Numpy, from someone else.
Speaking about assignments, a lot of the excercises require you to do something which hasn't been covered in the sessions at all. I understand giving a challene in assignments, but I would much rather prefer those challenges be related to things taught or from resources given / pointed to. But, unfortunately, you have to figure a lot out on your own and the videos are of no help.
It also doesn't help that the assignment feedback is very lacking. The grader also does not tell you what answer it expects, so you have no way of knowing how far off your answer is.
This is further not helped by the out-dated version of Pandas running (0.19.2). It has a 4 year old version. I tried to do the assignments locally, but then coming onto Coursera to find the methods I've used aren't supported. This causes further frustation with the "go learn on your own" approach, as every resource you'll find will be using methods/functions from the latest versions. You then have to spend hours more finding legacy methods for what you're trying to do (which, in practice, will be useless as you will always be working on updated packages)
In my opinion, this course is not worth the money. I would highly recommend you trial its contents before deciding whether to pay for it or not.
by Amir M O•
Wish I could give it zero star.
1- The lectures are extremely poor (read the most helpful reviews and you see that a lot of people share this opinion).
2- Assignments are super difficult and not related to the lectures.
3- Assuming that you manage to solve the questions, now you have to deal with their defective auto grader which is royal pain.
4- They insist on using Jupyter (in my opinion a really messy environment). I used PyCharm which is the default IDE for python nowadays but their auto grader caused me so much headache.
Overall, this course requires significant changes and more respect towards the students who spend a lot of time on it. For me personally, it killed my motivation for pursuing Data Science and taking more courses from this instructor.
by Rahul R•
This course is very difficult. This is first of all not a introductory course. The instructor teaches basic stuff but the assignments were look like mountain. It is quite impossible for a beginner to solve this type of assignment problems without having a very good background in python programming and data structure handling.
I should recommend, the instructor should revise the course content. Please bring balance between what you are teaching and what you are expecting from student.
After taking this course, I personally demotivated from taking further courses in this specialization.
*********** I will recommend going for IBM data science specialization.********
by Maria K•
Task formulations and goals explanation in assignments description are extremely bad, I've almost turned grey trying to understand what is the main purpose of exercise.
May be you should be able to read minds of Christopher Brooks or to be some sort of psychic to complete these assignments. :)
The only way to deal with this is to googling and searching for the answer in discussions forums, StackOverflow and Github.
But, unfortunately, I haven't find anything better on coursera for my current programming level.
P.S. If you are beginner in python programming (as me), I highly recommend you to try DataQuest, it is much more understandable.
by Marshall J V•
Would give this class a half star if I could. The material is covered way too fast and the assignments require knowledge of items not even mentioned in the class (let alone discussed). If you know the material well enough to get through this class, you don't need the class. The prof and TA refer to using Stack Overflow to figure it out early and often! Found this class to be a waste of time and money. I wanted to learn the material, but had to drop the class because I had no clue how to do the assignment after watching the lectures multiple times.
by Kyung H K•
I have no idea who rated this class five stars. The lectures do not prepare you for the assignments and the auto-grader will grade your answer as incorrect if you return a 17 dtype='float64' and they were expecting a 17 dtype='float'. Also, there's absolutely no feedback on your work except from the auto-grader, so there's no opportunity to go back and see a more elegant way of writing your code. I managed to get 90%+ for every assignment, but it was only because I spent over 10+ on the homework assignments for the last two weeks.
by Thileepan P•
This is definitely not an introductory course. This is more of an intermediate level course. The teachers explain complex techniques in one or two sentences. The notebook demonstration in the video lectures are also very fast.
There is a huge gap between the contents in the lectures and the assignment questions. These points should be kept in mind while choosing this course. I think, I will not take other courses in this specialization.
by Benjamin G H•
Serious issues with the assignment grading system will result in you pulling out your hair due to only getting credit for erroneous assignment grading system instead of learning.
Literally have to read the discussion forums to figure out how to replicate the errors the grading system is looking for. Huge waste of your time.
Wait atleast a year from this review to consider taking the course and pray they have finally edited it by then.
by David S•
This course is poorly organized, the instructor doesn't clear the most important basic concepts and pitfalls, instead just gives a brief through what can be done. The assignments are terrible, cannot state the problem clearly, didn't say anything about text files issues which causes submission problems, waist a lot of time on it.
by Dan D•
This was the WORST course I have ever taken on Coursera. The final exercise questions were not specific enough and the autograder SUCKED ASS. I couldn't even refer to a column in my dataframe after I closed the browser 3x and rebooted my machine and it still did not work. This course is a WASTE OF TIME. MOVE ON!!!
by Saulet Y•
Very disappointed! The assignments are unclear. To complete the assignments, you need to google on each question especially in Week 2 and 3. If you go to "Forum" page, you could see that there are more than 1400 threads in Week 2,3, which means a lot of students ask questions. The course is really really bad!