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.
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.
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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