- Data Visualization Software
- Logistic Regression
- Statistical Methods
- Python Programming
- Regression Analysis
- Statistical Machine Learning
- Predictive Modeling
- Statistical Inference
- Jupyter
- Bayesian Statistics
- Statistical Analysis
- Statistical Software
Fitting Statistical Models to Data with Python
Completed by Adita Parson
February 9, 2020
14 hours (approximately)
Adita Parson'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
