I am still working on my data science project and until now, it is going well. This is not always easy to find time to work on this but fortunately, the first courses of the IBM data science track on Coursera have not been too difficult. The next ones will probably be more challenging however.
I could complete the first courses relatively fast. Actually, I think the amount of work needed that is indicated on the courses is not really accurate. Some exercises are supposed to take one hour to complete, but actually, it takes rather ten to fifteen minutes, even if you are not familiar with the subject.
Data science project: IBM data science courses
Course 1: What is data science?
I had completed this course in March, as I had had the the opportunity to test one Coursera course with my job, as the company I am working was establishing a partnership with Coursera and needed voluntary employees to test courses.It is a good introduction if you want to understand better what data science is. It includes several interviews of data scientists. The course is rather easy. There are not difficult concepts to master and the assignments and tests are not difficult.
Course 2:Open Source Tools for data science
I completed this course on September 2nd. This course was also not that difficult but I found it useful because it is a good introduction to the tools that are most used in data science, especially Jupyter Notebooks. Although I had learned to code with Python on the Codecademy website, I was not very familiar with Jupyter Notebooks, so it was nice to have a presentation. The course includes several quizzes, practical exercises and an assignment. For the final assignment, you have to create and share a Jupyter Notebook.
Course 3: Data Science Methodology
I completed the course on September 13th. The course presents the different steps a data scientist is following when facing a new problem. First, I found it a little confusing but it was still interesting. The course includes quizzes, practical exercises and a final assignment. As for the final assignment, I was not that sure what was really expected from me though. As the assignment was peer-graded, I could see the assignments of two other persons. One wrote much less than I did and the other wrote more, using a real set of data. So I guess everyone understood the subject a little differently.
Course 4: Python for data science and AI
I am still working on this course. I have already completed around 4/5 of the course. The three first parts of the course focus on basic Python language skills. This was not too difficult for me thanks to the Codecademy Python course. I liked the way the different concepts were explained in the videos however. The 4th week focuses on panda and numpy. The course includes many quizzes, some practical exercises and a final assignment.