Ratings and Reviews for Fitting Statistical Models to Data with Python

Week 3 starts to get unreasonably difficult and hard to understand. Apart from that, the course is still worthwhile to take.

These whole three certifications lays the foundation for learning Machine Learning a more in-depth way.

Another interesting course - the final one in this specialisation - but the difficulty really ramped up in Week 3 after the final peer marked assignment. I had been so impressed with the clear explanations, revision and review, and the opportunities to apply new knowledge. However, it all became very abstract - I thought Mark did a good job but perhaps Bayesian is a whole different specialisation. Overall, I really enjoyed the specialisation and I am pleased to have received a good grounding in statistics ahead of my Data Science diploma. Thank you to Brenda and Brady especially but everyone was very strong and the future is bright with some enthusiastic young talent coming through at Michigan. Edward

This course wants to do too much. Week 3 and week 4 are like a math course but without the math so it's really hard to understand what's going on.

Overall a fair course , but i felt it was a bit too fast paced and more focused on theoritical statistics with serious lack in Pyrhon practising.I mena the notebooks were a great deal but the instractions on them and the video coures were not what i expected compared to previous lectures.It was a little bit difficult to follow on with the theoritical courses - weren't explanatory enough for me. And for sure i needed more Python practsing , lecturing and of course assessments.

The course is great, but I would suggest that the subject of week 3 be divided into two weeks.

Thanks U. Michigan..

Great statistical lessons, I did not realize there were more regression-type models besides Ordinary Least Squares, which expanded my learning horizon, and of course, applied using Python Jupyter Notebooks. Python Code was comprehensive and enabled easy following. It was immensely helpful as I did not know how to even begin constructing a linear model study, using independent or dependent data.

Overall, the course was a great refresher of statistical theory and application with some great Python exercises. However, some of the Python coding instruction itself could have been more detailed.

Overall it's very good for someone who has a fair background in statistics, except for some small mistakes in slides and notebooks.