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DataCamp Statistical Thinking in Python Part 2

Created/Published/Taught by:
Justin Bois
Hugo Bowne-Anderson
Vincent Lan
Yashas Roy

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$0.00 - $29.00

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From DataCamp:

“After completing Statistical Thinking in Python (Part 1), you have the probabilistic mindset and foundational hacker stats skills to dive into data sets and extract useful information from them. In this course, you will do just that, expanding and honing your hacker stats toolbox to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing. You will work with real data sets as you learn, culminating with analysis of measurements of the beaks of Darwin’s famous finches. You will emerge from this course with new knowledge and lots of practice under your belt, ready to attack your own inference problems out in the world.”

This course consists of five chapters:

  1. Parameter estimation by optimization
  2. Bootstrap confidence intervals
  3. Introduction to hypothesis testing
  4. Hypothesis test examples
  5. Putting it all together: a case study

Recommended Prerequisites: Statistical Thinking in Python Part 1

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