- Statistical Inference
- Bayesian Statistics
- Python Programming
- Statistical Methods
- Statistical Modeling
- Statistical Programming
- Statistical Software
- Logistic Regression
- Jupyter
- Model Evaluation
- Predictive Modeling
- Statistical Analysis
Fitting Statistical Models to Data with Python
Completed by Vinicius Cocorullo Fabri Moraes
March 11, 2021
14 hours (approximately)
Vinicius Cocorullo Fabri Moraes's account is verified. Coursera certifies their successful completion of Fitting Statistical Models to Data with Python
What you will learn
Deepen your understanding of statistical inference techniques by mastering the art of fitting statistical models to data.
Connect research questions with data analysis methods, emphasizing objectives, relationships between variables, and making predictions.
Explore various statistical modeling techniques like linear regression, logistic regression, and Bayesian inference using real data sets.
Work through hands-on case studies in Python with libraries like Statsmodels, Pandas, and Seaborn in the Jupyter Notebook environment.
Skills you will gain
