text: var dstStacks = [];
  
  var dst01 = [
      "Data Pipeline",
      "Document Object Model"
  ];
  dstStacks.push({ title: "Web Crawling", cards: dst01 });
  
  var dst02 = [
      "Survey Medium",
      "Survey Types",
      "Empirical Research Paradigm",
      "Quantitative Methodology",
      "Survey Process",
      "Survey Process - Sample",
      "Survey Process - Research Model",
      "Survey Process - Operationalization",
      "Survey Process - Survey & Data Collection",
      "Survey Process - Data Analysis"
  ];
  dstStacks.push({ title: "Surveys", cards: dst02 });
  
  var dst03 = [
      "Correlation & Causality",
      "Lab vs Field Experiments",
      "Experiments - Main advantages",
      "Experiments - Issues & Limitations",
      "Induced Value Theory",
      "Recommendations for experimental design"
  ];
  dstStacks.push({ title: "Experiments", cards: dst03 });
  
  var dst04 = [
      "Tidy data structures",
      "Data Cleaning",
      "Wide and Format",
      "Dummy coding",
      "Outliers",
      "Standard Error",
      "t-Test",
      "Linear regression models",
      "Linear Regression Models and Variance Structure",
      "Linear Regression - Examples with R2",
      "Anscombe’s Quartet",
      "Hypothesis & Statistic Hypothesis Testing",
      "Evaluation Metrics",
      "Linear Regression - Special Cases"
  ];
  dstStacks.push({ title: "Data, Tests, Regression", cards: dst04 });
  
  var dst05 = [
      "Background: Network Analysis",
      "Background: Social Science",
      "Practical Applications",
      "Social Network Analysis",
      "SNA - Networks",
      "SNA - The Strength",
      "SNA - Key Players",
      "SNA - Cohesion",
      "Typical Social Network Characteristics"
  ];
  dstStacks.push({ title: "Network Analysis", cards: dst05 });
  
  var dst06 = [
      "dplyr: Basics",
      "Time series analysis",
      "Time series analysis - Linear filters",
      "Time series analysis - Non-Linear filters",
      "Cluster analysis",
      "Similarity",
      "Distance Measures",
      "Distance Between Clusters",
      "Cluster Algorithms",
      "Quality Assessment"
  ];
  dstStacks.push({ title: "Filter & Clustering", cards: dst06 });
  
  var dst07 = [
      "Explaining vs Predicting",
      "Overfitting",
      "Neural Networks",
      "Neural Networks: Forward Pass",
      "Neural Networks: Error Calculation",
      "Neural Networks: Error Minimization",
      "Neural Networks: Backward Pass",
      "Data Division",
      "Model strategies",
      "Evaluation Metrics",
      "Recipe for better ML models"
    ];
    dstStacks.push({ title: "Machine Learning", cards: dst07 });
  
    var dst08 = [
      "Definition of Data Visualization",
      "Data Visualization Techniques",
      "Data Visualization - Geospatial Data",
      "Data Visualization - Vernoi Diagram",
      "Data Visualization - Distribution",
      "Data Visualization - Ranking",
      "Data Visualization - Evolution",
      "Data Visualization - Correlation",
      "Data Visualization - Flow",
      "Data Visualization - Proportion",
      "Principles of Data Visualization",
      "Principles - Gestalt Theory",
      "Principles - Visual Variables",
      "Principles - Visual Asthethics",
      "Principles - Typography",
    ];
    dstStacks.push({ title: "Data Visualization", cards: dst08 });
  
  addCourse("Data Science Toolbox", "DST", dstStacks);
  