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Showing 4 open source projects for "statistics"

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  • 1
    Statistics for Data Scientists

    Statistics for Data Scientists

    "Statistics for Data Scientists: 50 Essential Concepts"

    The “statistics-for-data-scientists” repository is a pedagogical resource designed to bridge rigorous statistics theory and practical data science workflows. The code and materials are intended to help data scientists and analysts grasp statistical principles (e.g. inference, regressions, hypothesis testing, probability, confidence intervals) in contexts relevant to real data analysis tasks. The repository includes Jupyter notebooks, R scripts, worked examples, and possibly problem sets...
    Downloads: 1 This Week
    Last Update:
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  • 2
    stat-cookbook

    stat-cookbook

    The probability and statistics cookbook

    A compact “Probability and Statistics Cookbook” offering concise mathematical recipes for key statistical concepts—expectation, variance, distributions and inequalities—packaged as LaTeX and R-based executable documents.
    Downloads: 0 This Week
    Last Update:
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  • 3
    swirl

    swirl

    Learn R, in R

    swirl is an R package that allows interactive, in-R learning of statistics, data science, R programming etc. The idea is that you load swirl in R, and it presents you with lessons (within R’s console or RStudio) that ask you to type commands, check results, and progress through tutorial material—without leaving the R environment. It is used for teaching R, especially for beginners, as well as for self-paced learning of packages, data manipulation, visualization, etc. Lessons and content...
    Downloads: 1 This Week
    Last Update:
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  • 4
    Data Science Specialization

    Data Science Specialization

    Course materials for the Data Science Specialization on Coursera

    ... topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow.
    Downloads: 3 This Week
    Last Update:
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