AA
4.0Reviewed Jun 19, 2020
The course was wonderful however, sometimes I felt that a little bit more details could be provided when python code was being explained for week 2.
Fitting Statistical Models to Data with Python
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.
Status: Logistic Regression
Status: Statistical Programming
IntermediateCourse15 hours
Featured reviews
BS
5.0Reviewed Jan 17, 2020
I am very thankful to you sir.. i have learned so much great things through this course.this course is very helpful for my career. i would like to learn more courses from you. thank you so much.
TW
4.0Reviewed Sep 4, 2020
Good for advance topics like Marginal and Multilevel modelling. The Bayesian model could be explained in a detailed manner by providing more python assignments.
VO
5.0Reviewed Sep 17, 2019
Good course, but the last of three was the most difficult one. I hope that it were a good introduction to the fascinating world of statistics and data science
ST
4.0Reviewed Jan 23, 2021
Week 3 starts to get unreasonably difficult and hard to understand. Apart from that, the course is still worthwhile to take.
EP
4.0Reviewed Oct 10, 2020
Great course. In my view, the lectures were too long and the assignments a bit easy. But, overall, great course.
ST
4.0Reviewed Jun 15, 2021
It was very technical and a lot of the mathematics behind the models were not explained properly. The codes were also not explained properly
NU
4.0Reviewed Jun 4, 2020
The course is great, the only improvement I would make is to be a little more didactic in the last two units because it is a more complicated subject.
JJ
4.0Reviewed May 23, 2020
Very informative. But had few confusions in the last course. Also the python code explanations were not good as the instructor was rushing through it without explaining.
KA
5.0Reviewed Jul 11, 2021
Just like the other courses in the specialization, very well thought out and planned! Up to date, great professors . . . couldn't ask for more!
AP
4.0Reviewed Sep 6, 2020
I think the notebook walkthroughs, while useful, could use some extra reinforcement in the statistical concepts
CV
5.0Reviewed Jun 27, 2021
Excellent, the explanations were perfect and its theorical focus was the thing why I loved this course.