AI academy- Probability for Data Science: Concepts, Experiments, and Applications

Probability is a core component of data science, machine learning, and decision-making under uncertainty. This workshop provides a structured and intuitive introduction to probability theory, focusing on both conceptual understanding and practical application.

Participants will explore fundamental probability concepts such as events, independence, unions, intersections, and probability distributions. These topics are introduced through physical experiments (coin tosses, dice rolls) and visual demonstrations to build intuition before moving to computational approaches.

The workshop also covers key statistical measures such as mean, variance, and standard deviation, and introduces commonly used probability distributions. Participants will learn how to interpret probability distributions and cumulative functions, which are essential in understanding real-world data.

Through Python-based simulations, participants will connect theoretical concepts with practical applications, including scenarios from domains such as healthcare, business analytics, and risk assessment.

1. Introduction to Probability and Randomness

• What is probability?
• Sample space and events
• Basic probability notation
• Classical vs empirical probability
• Understanding randomness and uncertainty

2. Probability Rules and Event Relationships

• Independence of events
• Disjoint (mutually exclusive) events
• Union and intersection of events
• General addition rule
• Multiplication rule

3. Probability Distributions

• What is a probability distribution?
• Probability Mass Function (PMF)
• Probability Density Function (PDF)
• Cumulative Distribution Function (CDF)
• Discrete vs continuous distributions

4. Statistical Measures and Data Understanding

• Mean (expected value)
• Variance and standard deviation
• Percentiles and quantiles
• Interpreting spread and variability

5. Common Probability Distributions and Applications

• Overview of common distributions:
• • Binomial distribution
  • Normal distribution
  • Uniform distribution
• Real-world applications:
• • Risk estimation
  • Quality control
  • Basic biomedical signal variability (e.g., heart rate variation)

By the end of the workshop, participants will be able to:

• Understand fundamental probability concepts and notation
• Apply probability rules to real-world scenarios
• Distinguish between different types of probability distributions
• Compute and interpret statistical measures
• Simulate probability experiments using Python
• Analyze and visualize probabilistic data
• Apply probability concepts in data science and decision-making

Intensive one-day workshop format: The training combines conceptual understanding with interactive experiments and computational practice.