Introduction

High-dimensional data, often found in fields like genomics, finance, and machine learning, poses a unique challenge for data visualization. As the number of features increases, it becomes increasingly difficult to visualize and interpret the data effectively. This course will provide participants with the tools and techniques necessary to visualize high-dimensional datasets, explore relationships, and uncover insights that might otherwise remain hidden. From dimensionality reduction techniques to interactive visualizations, this course will equip participants with the skills to handle and visualize high-dimensional data efficiently.

Objectives

By the end of this course, participants will:

• Understand the challenges and techniques involved in visualizing high-dimensional data.
• Gain proficiency in dimensionality reduction methods such as PCA, t-SNE, and UMAP.
• Learn how to create scatter plots, parallel coordinates plots, and other visualizations for high-dimensional data.
• Master the use of clustering and classification techniques to group similar data points and enhance visual clarity.
• Explore advanced visualization tools and libraries (e.g., Plotly, Seaborn, Matplotlib, and TensorFlow).
• Learn best practices for communicating insights from high-dimensional data effectively.

Who Should Attend?

This course is ideal for:

• Data scientists, analysts, and machine learning practitioners who work with high-dimensional datasets.
• Researchers and professionals in fields such as genomics, finance, and marketing, where high-dimensional data is common.
• Data visualization professionals who want to expand their toolkit to handle complex data.
• Anyone interested in learning how to visualize and interpret high-dimensional data to uncover actionable insights.

Day 1: Introduction to High-Dimensional Data and Challenges in Visualization

Morning Session: Understanding High-Dimensional Data

• Definition and characteristics of high-dimensional data.
• The curse of dimensionality: Why higher dimensions make visualization difficult.
• Overview of common sources of high-dimensional data: Genomic data, financial data, image data, etc.
• Key concepts: Features, samples, dimensionality, and sparsity.
• Hands-on: Exploring high-dimensional datasets and examining the structure of the data.

Afternoon Session: Basic Visualization Techniques for High-Dimensional Data

• Scatter plots and pair plots: Visualizing relationships between two or more variables.
• Correlation matrices: Understanding the relationships between dimensions.
• Heatmaps and clustering: Displaying similarities between data points in a matrix format.
• Hands-on: Creating basic visualizations (scatter plots, pair plots, and correlation heatmaps) using Python (Matplotlib, Seaborn).

Day 2: Dimensionality Reduction Techniques for Visualization

Morning Session: Introduction to Dimensionality Reduction

• What is dimensionality reduction and why is it important for high-dimensional data?
• Overview of common dimensionality reduction techniques: PCA (Principal Component Analysis), t-SNE (t-Distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection).
• The trade-offs: Interpretability vs. preservation of variance and local structure.
• Hands-on: Applying PCA to a high-dimensional dataset and visualizing the result in 2D or 3D.

Afternoon Session: Advanced Dimensionality Reduction Techniques

• Understanding t-SNE: Strengths, weaknesses, and when to use it.
• UMAP: A powerful alternative to t-SNE with faster computation and better preservation of global structure.
• Visualizing clusters and patterns after dimensionality reduction.
• Hands-on: Using t-SNE and UMAP on a dataset and comparing the results with PCA visualizations.

Day 3: Advanced Visualization Techniques for High-Dimensional Data

Morning Session: Clustering for High-Dimensional Data

• Introduction to clustering algorithms: K-means, DBSCAN, hierarchical clustering.
• How clustering can enhance high-dimensional data visualization by grouping similar data points.
• Visualizing clusters in lower dimensions using dimensionality reduction techniques.
• Hands-on: Applying K-means clustering to a high-dimensional dataset and visualizing the clusters with PCA or t-SNE.

Afternoon Session: Parallel Coordinates and Other Advanced Visualizations

• Introduction to parallel coordinates plots for visualizing high-dimensional data.
• Strengths and weaknesses of parallel coordinates in high-dimensional analysis.
• Creating radar charts and star plots to visualize multivariate data.
• Hands-on: Creating a parallel coordinates plot to visualize multiple variables simultaneously.

Day 4: Interactive Visualizations and Web-Based Tools

Morning Session: Creating Interactive Visualizations

• The need for interactivity when exploring high-dimensional data.
• Tools and libraries for creating interactive visualizations: Plotly, Dash, Bokeh.
• Adding interactivity to dimensionality reduction plots: Zoom, hover, and click functionality.
• Hands-on: Building an interactive 3D PCA visualization with Plotly and Dash.

Afternoon Session: Web-Based Tools for High-Dimensional Data

• Overview of web-based platforms for interactive data visualization: Google Colab, Streamlit, and Tableau.
• Integrating Python visualizations into web applications for dynamic data exploration.
• Hands-on: Using Streamlit to create an interactive dashboard for high-dimensional data visualization.

Day 5: Best Practices and Final Project

Morning Session: Best Practices for High-Dimensional Data Visualization

• Key principles of effective data visualization: Simplicity, clarity, and focus on insights.
• Choosing the right visualization for your data: What works and when.
• How to handle and present outliers, missing data, and noise in high-dimensional datasets.
• Case studies: Best practices in genomics, finance, and marketing.
• Hands-on: Reviewing and critiquing real-world high-dimensional data visualizations.

Afternoon Session: Final Project and Course Wrap-Up

• Final project: Participants will work on their own high-dimensional dataset to create a comprehensive visualization using techniques learned throughout the course.
• Presentations: Each participant will present their project, explaining their approach, techniques used, and insights gained from the visualization.
• Group discussion and feedback on final projects.
• Review of key concepts, tools, and techniques covered during the course.
• Q&A session and course wrap-up.

Materials and Tools:

• Software and Tools: Python (Matplotlib, Seaborn, Plotly, Scikit-learn), Jupyter Notebooks, Dash, Streamlit, Tableau.
• Reading: “Data Visualization: A Practical Introduction” by Kieran Healy, “The Visual Display of Quantitative Information” by Edward Tufte.
• Resources: Sample datasets (e.g., Iris, MNIST, financial data), Python scripts, and course slides.

Post-Course Support:

• Access to course materials, recorded sessions, and additional resources.
• Post-course webinars on advanced topics in high-dimensional data visualization.
• A community forum for sharing projects, asking questions, and continuing to learn.
• Opportunities for one-on-one consulting on specific high-dimensional data visualization challenges.