I finally completed my IBM data science specialization. I completed the 8 first courses of the specialization very fast but the last course took me for ever. I met all kinds of technical issues and had to simplify my final project to the point that I feared I would not get a passing grade. However, this worked and I am really relieved the course is finally over.
This does not mean I am over with data science. I want to learn more about this field. The IBM data science specialization was great to have a broad view of different aspects of data science and improve my Python skills. However, I will need to review some of the points seen if I really want to master them.
Here is a list of the 9 courses of the specialization:
- What is data science?
- Tools for data science
- Data Science methodology
- Python for data science and AI
- Databases and SQL for data science
- Data Analysis with Python
- Data visualization with Python
- Machine learning with Python
- Applied data science capstone
I also took a couple of other courses as I was blocked so long on the last course. I guess you can call it procrastination but I am still happy I took these courses. Here are the other courses I took:
- Data Science Math Skills, Duke University: this course aims to give you the basic math skills you need for data science. This was a good reminder for me as I had seen many of the notions of the course when still in high school and I had forgotten some of them.
- AI for Everyone, deeplearning.ai : This is a very good introduction course fabout artificial intelligence. It was easy to follow but I still learnt a lot.
- Programming for Everybody (Getting Started with Python), University of Michigan: I have already followed a course on Python with Codecademy so the course was easy for me. This is the first of the 5 courses of the Python for Everybody specialization of the University of Michigan. I wanted to try the course to learn Python in a deeper way and see how well I do with the assignments.
You can see below my previous posts on the project:
- My goals for September -> first presentation of the project
- Book review: Ultralearning (Scott H Young) ->the post is actually about the book Ultralearning but at the end of the article I am showing how the book helped me to define my data science project better
- Data science project: first weeks -> description of the first courses I took
- Data Science project: October update -> an update on the data science project
- Data science project: November update-> an update on the data science project
- Data visualization and machine learning -> an update on the courses 7 and 8
- Data science project: machine learning -> an update on the courses 8 and 9