In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.
This course is part of the Statistics with Python Specialization
Offered By
About this Course
High school algebra, successful completion of Course 1 in this specialization or equivalent background
What you will learn
Determine assumptions needed to calculate confidence intervals for their respective population parameters.
Create confidence intervals in Python and interpret the results.
Review how inferential procedures are applied and interpreted step by step when analyzing real data.
Run hypothesis tests in Python and interpret the results.
Skills you will gain
- Confidence Interval
- Python Programming
- Statistical Inference
- Statistical Hypothesis Testing
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Offered by
Syllabus - What you will learn from this course
WEEK 1 - OVERVIEW & INFERENCE PROCEDURES
WEEK 2 - CONFIDENCE INTERVALS
WEEK 3 - HYPOTHESIS TESTING
WEEK 4 - LEARNER APPLICATION
Reviews
- 5 stars74.10%
- 4 stars17.30%
- 3 stars5.60%
- 2 stars1.55%
- 1 star1.43%
TOP REVIEWS FROM INFERENTIAL STATISTICAL ANALYSIS WITH PYTHON
This is a very great course. Statistics by itself is a very powerful tool for solving real world problems. Combine it with the knowledge of Python, there no limit to what you can achieve.
The best part of this that it is designed in a way that it encourages people to dig deeper and explore more. The instructors have done a great job in making the curriculam this good.
this is absolutely a great course. i happy learning this one. the subjects they explained was crystal clear and i would suggest this to my mates.
Great in-depth content of further statistics, applied using Python Jupyter Notebooks. Python Code was comprehensive and enabled easy following.
About the Statistics with Python Specialization

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