The statistics short course is both a resource and survey of the areas of probability and statistics that are foundational for the data science immersive at Galvanize.

Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Stats Shortcourse | 41 | 3 years ago | bsd-3-clause | TeX | ||||||

The statistics short course is both a resource and survey of the areas of probability and statistics that are foundational for the data science immersive at Galvanize. | ||||||||||

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This workshop surveys the concepts in probability and statistics that are covered in the statistics, math, and probability interview for Galvanize's Data Science Immersive program. | ||||||||||

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The statistics short course is both a resource and survey of the areas of probability and statistics that are foundational for the data science immersive at Galvanize. | ||||||||||

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Readme

Course website: https://galvanizeopensource.github.io/stats-shortcourse/

As part of the admissions process for the Galvanize immersive program in data science there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.

The main topics to be covered will be as follows:

Day 1: Probability, Probability distributions, Bayesian and frequentist paradigms

Day 2: Random variables, Statistical inference, Regression, Classification, Evaluation metrics

We well begin in the first day with a gentle introduction to probability and the major distributions used in statistics. We will finish with a concept-driven explanation of frequentist and Bayesian statistics.

On the second day we will dive a bit further into how to make use of probability distributions for inference and hypothesis testing. We will then introduce regression and classification through the use of examples. Finally, we will discuss some of the commonly use methods of evaluating model results.

We will use Python to illustrate the concepts, but no working knowledge of Python or any other language is required.

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