Diploma in Data Science



    Data Science Basics and Data Scientist Toolbox:

    Basics of data sciences and toolbox, the workflow of CLI and git, big data analysis, and experimental design.

    Data Analysis with Python:

    Pandas, time deltas, python plotting, data structures, and computational tools.

    Getting Data:

    Raw data, processed data, tidy data, web reading, API, data summarization and merging, regular expressions, and text variables.

    Data Analysis and Research:

    Graphical devices and plotting systems, basics of reproducible research, clustering, exploratory graphs, and basics of literate statistical programming.

    Statistical Inference and Regression Models:

    Probability and statistics, basics of statistical inference, regression models, distributions and likelihood, binary and count outcomes, and residual variations.

    Machine Learning:

    Caret, prediction with motivation, regression, and model and cross-validation.

    Developing Data Products and Working with NumPy:

    Shiny, solidify, googleVis, and numPy.

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