Inferential Statistical Analysis with Python

This course is part of Statistics with Python Specialization

Instructors: Brenda Gunderson +2 more

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What you'll 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'll gain

  •   Python Programming
  •   Jupyter
  •   Statistical Hypothesis Testing
  •   Statistical Methods
  •   Bayesian Statistics
  •   Statistical Inference
  •   NumPy
  •   Probability & Statistics
  •   Statistics
  •   Statistical Analysis
  •   Matplotlib
  •   Data Analysis

    • There are 4 modules in this course

    At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

    WEEK 2 - CONFIDENCE INTERVALS

    WEEK 3 - HYPOTHESIS TESTING

    WEEK 4 - LEARNER APPLICATION

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