This is a fantastic course for students to learn from scratch what is Data Science about? Articulating and defining Data Science Problem, choosing and curating a dataset, Data wrangling and web-scraping, along with SQL for Data Science for querying data from the databases. Data Analysis, feature identification, identifying independent and dependent variables etc. Various plotting techniques were introduced including Plotly, Plotly Express, Seaborn and Folium. Generating Dashboard using Dashly app development for interactive Exploratory Data Analysis (EDA). Model selection, best Hyper Parameter selection, confusion matrix and model outcome / Performance evaluation etc. Highly recommended for beginners to take this course to up their skills and begin solving Data Science problems....

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!.

The whole certificate is fantastic. It teaches you a great overview in many data science skills.

However, if you live in Germany, it will not help you get a job in the field at all - I mean AT ALL. It's completely worthless. You either need work experience in the filed or a Bachelors or Masters degree in statistics or computer science, but then again, you wouldn't need this certificate. So it's a great course to learn things, but it's a poor course to enhance your job opportunities.

This capstone course is what you make of it. I reviewed several final submissions (we are required to review one and optionally can review several). Some people made the extra investment to do more than just apply the labs to other cities, they really learned by applying all the past learning and doing a project to apply and showcase their learning. There is lots to learn and apply here. My advice is for you to invest all you can so you can showcase your abilities!

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.

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.

Dengan mengikuti kelas ini, saya mengerti tentang hal yang berkaitan dengan data science, dan menyelesaikan kasus yang nyata pada bidang ini. Tools yang digunakan juga bagus dan mudah dipelajari. Terima kasih Coursera <3

Excellent chance to push in every concept learnt so far. Best thing about the course is the chance to self identify a problem and find the solutions all by ourselves. Great Content. Thanks !

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.

Excelente curso, muchas gracias a todo el personal que intervino, en la elaboración y en el soporte para evacuar dudas. Felicitaciones!

Thank You for such a great end to end project, learnt a lot !! Presentation was damn lengthy though

Hi, I didn't receive the Badge and certification too. Please, Can You solve it for me?

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

Very easy to understand and remember this material

I would have liked to come up with a capstone project which did not encourage me to use location data from Foursquare, and rather use other data to come up with problems and solutions which do not necessarily have to do with location data. But all in all, it was an interesting course and great knowledge was gained

Some things need to be updated. Such and such is "Deprecated" errors, typo errors, and less and less thought put on in designing the exercises. I can feel the preparers dropping the ball towards the end of this specialization. Current visualization trends should be included like Tableau.

Overall, the course is very practical, you have the chance to do a project use the technology you have learned so far, and get a feeling of what Data Science work looks like. Recommend for those who interested in Data Science Area.

This project is beneficial by providing the chance to explore Foursquare API and generate some realistic results. However the discussion forum is not well used, questions posted there are rarely answered by teach staff.

Some things were outdated and did not work properly for me.

Peer-graded assignments where one has to create a github repository is totally unnecessary.

The assignments are either correct or incorrect and the scores are depends on your classmates grade to you.

Did not like Foursquare and was obliged to use it to complete the course

The whole course desperately needs a proof read. There are sections where sentences don't make any sense. The scripting and dialogue can be really clunky as well - phrases are repeated over and over . There are also parts where the exam section or final coursework has been updated with new content but the rest of the course has not been. For example one section you are asked to construct a pie chart the help links it gives you are for a bar chart. The videos are much more of an introduction and the real practice takes place in work sheets. I would like to see more videos explaining the processes in the worksheets as well as intro videos. Also Watson is pointless you make us sign up for a free account but nothing is really done with the programme, it is all jupiter or dash. Make everything in Watson or take the whole thing out.

However, there is a lot of information in the work sheets and it is a good starting point for Data Science. The writers are clearly passionate about Data Science and that comes through in the course. It gives you a very good start point for a career in Data Science.