Fitting Statistical Models to Data with Python

They will broaden understanding of statistical inference techniques in this course by concentrating on the science and art of fitting statistical models to data. They will expand on the ideas offered in the Statistical Inference course (Course 2) to stress the relevance of linking research topics to data analysis methodologies. They will also look at a variety of modeling goals, including as inferring connections between variables and making predictions for future data. This course will cover linear regression, logistic regression, extended linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference approaches, among other statistical modeling techniques.

The course will stress alternative modeling methodologies for different types of data sets, based on the research design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). Learners will go through tutorials concentrating on specific case studies during these lab-based sessions to help consolidate the week's statistical ideas, which will include deeper dives into Python libraries such as Statsmodels, Pandas, and Seaborn. This course makes use of Coursera's Jupyter Notebook platform.

THE SKILLS YOU WILL DEVELOP:

• Bayesian Statistics
• Python Programming
• Statistical Model
• Statistical regression

Rating: 4.4/5

Enroll here: coursera.org/learn/fitting-statistical-models-data-python