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Learn data analysis fundamentals using the Julia programming language through this comprehensive 13-part tutorial series spanning over 7 hours. Master essential analysis techniques starting with basic data exploration using the Iris dataset and DataVoyager, then progress through supply-demand modeling and mortgage calculations with CairoMakie visualizations. Explore optimization problems including the knapsack problem using JuMP and PlotlyJS, while building a solid foundation in statistics fundamentals with StatsBase and HypothesisTests packages. Develop proficiency in statistical plotting across multiple visualization libraries including GR, StatsPlots, PlotlyJS, and CairoMakie. Gain extensive experience with DataFrames through a three-part deep dive covering PrettyTables, TypedTables, CSV handling, HTML output, data wrangling techniques, descriptive analysis, HTTP requests, DataFramesMeta operations, and Gadfly plotting. Create sophisticated geographic visualizations and choropleth maps using VegaLite, then advance to interactive data exploration with GLMakie widgets including sliders, buttons, and menus. Apply Monte Carlo simulation techniques for mathematical estimation problems and master Julia's Dates module for comprehensive date and time data handling. Requires familiarity with Julia syntax and semantics but assumes no prior analysis experience.
