Inferential Statistical Analysis with Python
This is one of the best online statistical analysis courses. This course will go through the fundamentals of using data for estimating and evaluating hypotheses. This course will analyze both categorical and quantitative data, beginning with single-population procedures and progressing to two-population comparisons. You'll find how how to make confidence intervals. This course will also examine sample data to see if a theory about a parameter's value is supported by the data. The correct interpretation of inferential results will be a significant focus.
Learners will apply what they've learned in Python within the course environment at the end of each week. Learners will work 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.
What you will learn
- Determine the assumptions that will be used to compute confidence intervals for each population parameter.
- In Python, create confidence intervals and understand the findings.
- When studying real data, go over how inferential processes are used and evaluated step by step.
- In Python, run hypothesis tests and evaluate the findings.
Skills you will gain
- Confidence Interval
- Python Programming
- Statistical Inference
- Statistical Hypothesis Testing
Instructors: Brenda Gunderson and 2 more instructors
Coursera rate: 4.6/5.0, 785 ratings
Offered by: University of Michigan
Enroll here: https://www.coursera.org/learn/inferential-statistical-analysis-python
