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12.6: Assessments

  • Page ID
    58346
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    Learning Objectives

    1. Apply Python analytics libraries (NumPy, pandas, scikit-learn) for data manipulation, analysis, and modeling.

    2. Use Jupyter Notebooks for interactive, reproducible analytics workflows.

    3. Apply R and tidyverse packages (dplyr, ggplot2, tidyr) for data wrangling and visualization.

    4. Write SQL queries for analytics: aggregation, joins, window functions, and CTEs.

    5. Compare Python, R, and SQL for analytics tasks and select appropriate tools for given scenarios.

    6. Design reproducible analytics workflows integrating multiple programming tools.


    This page titled 12.6: Assessments was last modified on Wed, 10 Jun 2026 23:39:35 GMT and is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Felix Amoruwa.

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