Statistical Inference and Hypothesis Testing in Data Science Applications

This course is part of Data Science Foundations: Statistical Inference Specialization

Instructor: Jem Corcoran

What you'll learn

Skills you'll gain

  •   Statistics
  •   Statistical Analysis
  •   Sample Size Determination
  •   Sampling (Statistics)
  •   Statistical Hypothesis Testing
  •   Statistical Methods
  •   Quantitative Research
  •   Data Ethics
  •   Statistical Inference
  •   Probability & Statistics
  • There are 6 modules in this course

    This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

    Fundamental Concepts of Hypothesis Testing

    Composite Tests, Power Functions, and P-Values

    t-Tests and Two-Sample Tests

    Beyond Normality

    Likelihood Ratio Tests and Chi-Squared Tests

    Explore more from Probability and Statistics

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