Inferential Statistical Analysis with Python
In this course, you'll learn the fundamental principles of using data to estimate and evaluate theories. Starting with one population technique and expanding to handle comparisons of two populations, you will analyze both categorical and quantitative data. You'll discover how to create confidence intervals. You'll also use sample data to see if a hypothesis about the value of a parameter is supported by the data. The proper interpretation of inferential results will be a major 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 focusing on specific case studies during these lab-based sessions to help solidify the week's statistical concepts, which will include deeper dives into Python libraries such as Statsmodels, Pandas, and Seaborn. This course makes use of Coursera's Jupyter Notebook environment.
This course offers:
- Shareable certificate: Get a Certificate when you complete
- 100% online
- Course 2 of 3 in the Statistics with Python specialization
- Intermediate level: High school algebra, successful completion of Course 1 in this specialization or equivalent background
- Approx. 19 hours to finish
- Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Course ratings: 4.6/5
Enroll here: https://www.coursera.org/learn/inferential-statistical-analysis-python
