Showing 4 open source projects for "data cleaning"

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    The Data Engineering Handbook

    The Data Engineering Handbook

    Links to everything you'd ever want to learn about data engineering

    ...It includes beginner and intermediate boot camps, interview guides, data cleaning and transformation resources, and curated lists of newsletters and industry communities, making it useful both for self-study and technical interview preparation. The repository is actively maintained and widely starred, reflecting its role as a go-to reference for newcomers and experienced practitioners alike.
    Downloads: 5 This Week
    Last Update:
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  • 2
    Perfect Roadmap To Learn Data Science

    Perfect Roadmap To Learn Data Science

    Basic To Intermediate Python data science guide

    ...What makes it particularly valuable is its holistic nature: rather than focusing only on modeling or theory, it also addresses the broader lifecycle of data-science work, data ingestion, cleaning, EDA, feature engineering, model building, validation, deployment, etc.
    Downloads: 0 This Week
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  • 3
    R4DS (R for Data Science)

    R4DS (R for Data Science)

    R for data science: a book

    ...Includes many example datasets, diagrams, code samples, and “hands-on” exercises. Comprehensive coverage of data-science workflow: data import, cleaning, transformation, exploration, modelling etc. Includes topics beyond basics: relational data (joins), date/time, strings, working with missing values, visualizing data, etc.
    Downloads: 7 This Week
    Last Update:
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  • 4
    Data Science Specialization

    Data Science Specialization

    Course materials for the Data Science Specialization on Coursera

    ...It spans essential 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: 2 This Week
    Last Update:
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