Data Visualization Fundamentals

Study Notes

Purpose: To communicate insights and patterns in data through visual representations, making it easier to understand and interpret.
Types of Visualization:
- Quantitative: Scatter plots, bar charts, histograms, and heatmaps to display numerical data.
- Categorical: Pie charts, stacked charts, and treemaps to display categorical data.
- Geospatial: Maps and 3D visualizations to display geographic data.
Best Practices:
- Choose the right type of visualization for the data and message.
- Avoid 3D visualizations unless necessary, as they can be misleading.
- Use color effectively to highlight important information.
- Label axes and provide context to ensure clarity.

Definition: A subfield of artificial intelligence that involves training algorithms to learn from data and make predictions or decisions.
Types of Machine Learning:
- Supervised Learning: The algorithm is trained on labeled data to learn a mapping between input and output.
- Unsupervised Learning: The algorithm is trained on unlabeled data to discover patterns or structure.
- Reinforcement Learning: The algorithm learns through trial and error by interacting with an environment.
Machine Learning Steps:
1. Data Preparation: Collect, preprocess, and transform data into a suitable format.
2. Model Training: Train the algorithm on the prepared data.
3. Model Evaluation: Assess the performance of the trained model using metrics such as accuracy, precision, and recall.
4. Model Deployment: Deploy the trained model in a production environment.
Common Machine Learning Algorithms:
- Linear Regression: A linear model for predicting continuous outcomes.
- Decision Trees: A tree-based model for classification and regression.
- Random Forest: An ensemble model that combines multiple decision trees.
- Neural Networks: A complex model inspired by the structure of the human brain.

Data visualization is used to communicate insights and patterns in data through visual representations, making it easier to understand and interpret.
There are three main types of visualization:
Quantitative visualization (scater plots, bar charts, histograms, heatmaps) for numerical data
Categorical visualization (pie charts, stacked charts, treemaps) for categorical data
Geospatial visualization (maps, 3D visualizations) for geographic data
Best practices for data visualization include:
Choosing the right type of visualization for the data and message
Avoiding 3D visualizations unless necessary, as they can be misleading
Using color effectively to highlight important information
Labeling axes and providing context to ensure clarity

Machine learning is a subfield of artificial intelligence that involves training algorithms to learn from data and make predictions or decisions
There are three main types of machine learning:
Supervised learning (training on labeled data to learn a mapping between input and output)
Unsupervised learning (training on unlabeled data to discover patterns or structure)
Reinforcement learning (learning through trial and error by interacting with an environment)
The machine learning process involves four steps:
Data preparation (collecting, preprocessing, and transforming data)
Model training (training the algorithm on prepared data)
Model evaluation (assessing the performance of the trained model)
Model deployment (deploying the trained model in a production environment)
Common machine learning algorithms include:
Linear regression (a linear model for predicting continuous outcomes)
Decision trees (a tree-based model for classification and regression)
Random forest (an ensemble model that combines multiple decision trees)
Neural networks (a complex model inspired by the structure of the human brain)