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The Data Science Specialization – 6. Statistical Inference

May 7, 2015

Week 1/Unit 1

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

  • Had a brief introduction to statistical inference.
  • Learned the basic rules of probability.
  • Learned the basic rules of conditional probability.
  • Learned what an expected value is.
  • Be able to distinguish between sample and population quantities.

Week 2/Unit 2

Week 3/Unit 3

Week 4/Unit 4

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