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Data Visualization with Julia Syllabus

Course Overview

This course introduces students to the principles and techniques of data visualization using the Julia programming language. The course will cover various visualization libraries, data preparation, and best practices for effective visual communication.

Course Objectives

By the end of this course, students will be able to: 1. Understand the fundamentals of data visualization. 2. Utilize Julia’s visualization libraries to create various types of charts and graphs. 3. Apply best practices for creating clear and informative visualizations. 4. Develop interactive visualizations for data exploration. 5. Communicate data-driven insights effectively through visualizations.

Prerequisites

  • Basic knowledge of programming concepts.
  • Familiarity with Julia programming language.
  • Understanding of basic statistics.

Course Outline

Week 1: Introduction to Data Visualization - Importance of Data Visualization - Overview of Julia Programming Language - Introduction to Julia’s Data Visualization Ecosystem

Week 2: Setting Up the Environment - Installing Julia and Jupyter Notebooks - Introduction to the Plots package - Basic Plotting with Plots

Week 3: Understanding Data and Basic Visualizations - Data Types and Structures in Julia - Creating Bar Charts, Line Graphs, and Scatter Plots - Customizing Plots: Titles, Labels, and Themes

Week 4: Advanced Plotting Techniques - Working with Multivariate Data - Creating Histograms, Box Plots, and Heatmaps - Introduction to Gadfly and Makie packages

Week 5: Data Preparation for Visualization - Cleaning and Transforming Data - Aggregating Data for Summary Visualizations - Dealing with Missing Data and Outliers

Week 6: Interactive Visualizations - Introduction to PlotlyJS - Creating Interactive Plots and Dashboards - Using Dash for Julia

Week 7: Specialized Visualizations - Geographical Visualizations with GeoMakie - Time Series Analysis and Visualization - Network Graphs and Visualization

Week 8: Best Practices in Data Visualization - Principles of Effective Data Visualization - Avoiding Common Pitfalls and Misleading Graphs - Case Studies and Real-World Examples

Week 9: Project Development - Choosing a Project Topic - Data Collection and Preparation - Initial Visualization and Analysis

Week 10: Final Project Presentation - Refining Visualizations - Creating a Comprehensive Story with Data - Presentation and Peer Review

Assessment Methods

  • Weekly Assignments (30%)
  • Quizzes (10%)
  • Midterm Project (20%)
  • Final Project (40%)