1. Congratulations!
Congratulations on completing the course! Before we sign off let's quickly review what we covered and some possibilities for the next steps.
2. Summary - Models
Using the linear model as a starting point we learned about the two most widely used GLMs logistic and the Poisson regression. Given the differences in the response, we introduced link functions, which enabled the model to be a linear combination of the parameters and enabled easier model inference. Keep in mind that the interpretation of the models differs quite a lot.
3. Summary - Python
In Python we used the statsmodels library and its glm function to fit the model with the appropriate distribution family.
4. Next steps...
To extend your statistical horizons further consider other DataCamp courses which cover some specific topics we mentioned in the course like regularization and validation techniques for example. Also, there are many other forms of regression models that might be useful in your practical work like hierarchical and mixed-effect models, generalized additive models, etc. There are many excellent reference sources out there where some of my favorites are Regression Modeling strategies, An introduction to Categorical data analysis and Applied predictive modeling.
5. Happy modeling!
Congratulations on completing the course. I hope you enjoyed it and more importantly learned a lot. Until next time happy modeling!