To much focus on Foursquare API

Hi,

I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".

Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)

Course 1: approximately 9 hours to complete

Course 2: approximately 16 hours to complete

Course 3: approximately 9 hours to complete

Course 4: approximately 22 hours to complete

Course 5: approximately 14 hours to complete

Course 6: approximately 16 hours to complete

Course 7: approximately 16 hours to complete

Course 8: approximately 20 hours to complete

Course 9: approximately 47 hours to complete

This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.

(*): https://www.coursera.org/professional-certificates/ibm-data-science?utm_source=gg&utm_medium=sem&campaignid=1876641588&utm_content=10-IBM-Data-Science-US&adgroupid=70740725700&device=c&keyword=ibm%20data%20science%20professional%20certificate%20coursera&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=347453133242&hide_mobile_promo&gclid=Cj0KCQjw0Mb3BRCaARIsAPSNGpWPrZDik6-Ne30To7vg20jGReHOKi4AbvstRfSbFxqA-6ZMrPn1gDAaAiMGEALw_wcB

The worst course by far in the 9 courses bundle

Please consider remove this course from the bundle

more exercises, more projects.

This is really challenging course, especially that you get hint on how to use a RESTful API (of Foursquare), how to create heat maps, or create different marking on a map using folium. The Capstone was really challenging, because you can practice what you have learned during the courses of the specialization, like how to start from the scratch a project, how to apply the data science methodology, like business understanding, gathering, analyzing, and cleaning data (most of your time you will spend on this), applying the right machine learning algorithm to solve the problem (modeling), using Jupyter Notebook on IBM cloud and using github. In the end you should also prepare your final report including the business understanding, describing your data, presenting your result, and placing a discussion section in the end. It took me 4 full days to complete the capstone, but I learned a lot.

This is the best part of the specialization and I learned a lot in this Capstone Project. If you've been doing all of the 8 previous courses, believe me those 8 courses are nothing compared to this course when it comes to putting time and hard work. You will learn a lot of things including web scraping, connecting to a url, using geolocation services to get data about a location. You'll also use foursquare API to get popular venues in a particular location. This project is super interesting but at the same you have to put in a lot of work too! It took me more than a month to do this capstone alone but it can be easily done in around 3 weeks if you're dedicated in completing it.

Good class, very useful. Peer grading is a great idea, don't like the practice of posting notes in the forum with subjects like "You grade mine and I'll grade yours." At the least, it gives the appearance of cheating. It is also wasteful, as it leads to some assignments being graded multiple times while others are waiting in the queue. This is a practice that Coursera encourages, which is baffling to me. Even in the last class in a 9 class series, I ran into people submitting blank or nearly blank assignments, with no content or inappropriate content, who were apparently hoping for a pity pass or cheating.

This course is really good and give enough challenge on the final project, especially on how to get data from multiple sources: scraping data from web, call APIs, and visualize it on map after call the clustering algorithm. I like the way we should prepare all material to complete the course like visual presentation with slide/blog post, report, and share the code in GitHub. Really glad I take all these 9 courses, can't wait to take other specialization course.

Very Good. This is my first contact with data science with python and associated packages.

In the end of the course I'm able to deal with data using python and a lot of tools that helps the job and let this job more fun.

A very well organized and balanced course with videos and very good material for practical labs.

I have a Swisse Knife with me to deal with future researches on data science.

Thanks.

The course was very helpful in presenting me the world of data science, what exactly are the things we need to be proficient in to excel in this field ! Best course of all was Machine learning with Python, you will enjoy doing it ! and we need more questions in quiz to test what student has gained at every step.

This is a very useful capstone project in which you can apply all the learning you have done throughout the course, the more practice you do the more you learn. I like this course from coursera and recommend it for data science aspirants.

Thank You!

A must take capstone project. Enroll for it and you will be moved by the project... Very interesting !

Just terrible , No Any Idea what to build no any suggestion what methods to use togeher

Outdated and poorly taught specialisation. My best experience on Coursera has been Andrew Ng's ML course and maybe it raised the bar too high. But that was a course that taught the student (keyword: taught). This does not even come close. Would not recommend.

It has been a fantastic journey of completing the 9 course specialization over the past two months!

I practically started with no prior exposure to data science but learned a lot of useful Python skills, tips-and-tricks, and knowledge about data science. The course material and instructors were excellent, and the Jupyter Notebooks were very challenging at times - at least for someone who had left programming around 15-years ago.

The specialization, as it says, is of beginner level but would definitely equip the students to move forward on their own in their quest for data science.

I would suggest adding courses on probability and statistics, linear algebra, and calculus in the specialization.

What a wild ride! The IBM Data Science Professional Certificate was my first exposure to coding and Data Science. The courses prior to the Capstone really do give you enough rope where you can do some cool stuff for the Capstone Project. Throughout the program, I learned where to go when I needed to teach myself something, while I was exposed to the basics.

I'm probably not normal, but I spent 40 or so hours on the capstone project itself. Lots of Frustration at times, but lots of fun as well and I was proud of what I was able to accomplish.

When I started this course I didn't know how fast I could grab these concepts in Data Science, it has been a challenging journey, especially learning a new programming language Python with all the libraries and packages, but Don't underestimate the little efforts, It leads to greatness. Now through Coursera and IBM I can boast courses that has prepared and given me head start into my new career. Coursera has made it possible for me to study at my own pace. This is amazing!, it worth recommending!.

A great course that tests your skills to apply your cumulative knowledge on Data Science since you embarked on the Programme.

As for me, even though I don't intend to become a full-fledged Data Science, this course along with the Specialisation Programme provide sufficient understanding and practical hands-on learning to better appreciate benefits and constraints of Data Science, particularly on the importance of data and machine learning ability.

I really like using everything I learned into my own example built from the beginning to end. I felt a little bit unsecured with the assignment at the beginning, because I was really a beginner in Python before I started this course. But it encouraged me a lot to finish it and to learn more. Now I have a basis and a lot of information, that I need to sort out. I am planning to do more Python courses and continue practicing.

It was very good.. Overall. few things I like to add.

Sharing my notebook from Cloud was not working a lots time.

GitHub , or Jupiter Notebook with simple 2 lines of coding did not work..

Also, a lot of time, cloud machine just spins.. -- without showing it

My resource got close to limit , - Could not add more code..

Instructions were out of date, could not be applied current version

Working with Cloud machine was challenging !!

Great to put into action the theoretical knowledge acquired in the previous 8 classes. What could be improved? The peer-rating system can be very slow. A common practice is to go on the forum to beg for a rating by another learner. This can be tiring.

This course is instructive and challenging at the same time. Now I do wish I know a bit more about python and pandas before I jump in this course. But in the end I managed to get through.

Felt a bit constrained by the requirement to include the Foursquare API.

Very easy to understand and remember this material

I finished all he 9 courses in this specialisation. Therefore, this comment basically applies most of the 9 courses.

The video contents and the practice exercises are very good and on point. Instructors are great. However, there are serious problems with the assessment mechanism and this is the reason why I am giving a 4 start.

If you work hard on the assignments, meaning that you study and research well to understand the code, you might end up getting a low score on assignments. This is because of peer-graded assignments. Your work might be graded by someone who doesn't understand the material as much as you do, or even someone who submitted a blank file just to see others' work. You rarely get feedback for the missing marks. As a simple example, I once submitted my work and received 4/11 with no feedback. I instantly re-submitted the same work and received 9/11 from another peer.

Another problem is that you get to see the rubric only after you submit. Some assignments are not clear on what the specific expectations are. The rubric must be clear before you submit the work. Even if you try to be flexible in your solution to address the vagueness, the peers may not show the same flexibility although you do the work properly.

And finally, the biggest issue.. Plagiarism! When I say plagiarism here, I mean copying someone else's work line by line all the way. It is utterly disgusting that it is more widespread than I initially thought. Such cases have been posted multiple times in the forum. I encountered at least 2 cases of plagiarism. The only thing you can do is to flag the submission, but obviously this doesn't stop anyone. What's worse is that those people who plagiarised someone else's work line by line get to peer-grade your own work.

Assessment section of these courses is a mess and has to be seriously re-evaluated. Peer graded assignments can be accepted to a certain extend but not for assignments that require hours and hours of our effort.