{"id":29127906,"url":"https://github.com/kgosiruri/learning-julia","last_synced_at":"2026-04-16T11:01:52.514Z","repository":{"id":297809851,"uuid":"996346256","full_name":"kgosiruri/Learning-Julia","owner":"kgosiruri","description":"This repository documents my journey learning Julia, a high-performance programming language designed for technical computing. It includes practice scripts, example projects, and notes on core Julia concepts such as syntax, data structures, performance optimization, and working with packages.  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It follows exercises and concepts from the *Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence* textbook.\n\n---\n\n## 📌 Purpose\n\nThe notebook is a hands-on exploration of Julia's capabilities in:\n\n- Basic scripting and control flow  \n- Data science essentials  \n- Visualization  \n- Mathematical functions and statistics  \n- Package management and plotting\n\n---\n\n## 🚀 Getting Started\n\n### 📦 Prerequisites\n\nEnsure you have [Julia](https://julialang.org/downloads/) installed and install the IJulia package:\n\n```julia\nusing Pkg\nPkg.add(\"IJulia\")\n```\n\nAlso, install required packages:\n```\nPkg.add.([\"Random\", \"Plots\", \"Statistics\", \"Roots\", \"LinearAlgebra\", \"StatsBase\", \n          \"LaTeXStrings\", \"Measures\", \"Images\", \"HTTP\", \"JSON\"])\n```\n\n## 🧪 Running the Notebook:\n\t1.\tStart Jupyter Notebook from your terminal or Anaconda.\n\t2.\tEnsure you select the Julia kernel (IJulia).\n\t3.\tOpen and run full 2.ipynb.\n\n## 🔍 Features Used:\n\t- \tprintln, for loops, array comprehensions\n\t- \tMath operations (e.g., squaring, square roots)\n\t- \tUse of Julia’s package system (Pkg.add)\n\t- \tPlotting and visual presentation via Plots.jl\n ## 📊 Example Outputs\n\nThis notebook includes practical examples and outputs from exercises based on the textbook:\n\n**_Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence_**\n\nBelow are some sample outputs generated during exploration:\n\n### ✅ Descriptive Statistics\n- Summary statistics, histograms, and boxplots\n```julia\njulia\u003e describe(randn(100))\n```\n\n## 🎓 Learning Resource\n\nThis notebook is built as a personal exercise space to practice topics from the textbook:\n\nStatistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkgosiruri%2Flearning-julia","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkgosiruri%2Flearning-julia","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkgosiruri%2Flearning-julia/lists"}