Data science rarely starts with machine learning. It begins with questions, messy datasets, exploratory analysis, and careful reasoning before any model is trained.

In this hands-on workshop, participants will explore how real-world data science workflows unfold—from structured analytics and exploratory data analysis to the development of an applied AI system. Using guided problem-solving and practical examples drawn from university research and projects, the session demonstrates how analytical thinking, documentation, and interpretability connect data analytics to practical machine learning systems.

Participants will follow along in interactive notebooks using classroom environments provided through DataCamp. Through these guided exercises, attendees will see how professional data scientists approach problems step by step: exploring data, forming hypotheses, testing ideas, and translating insights into working analytical and machine learning solutions.

During the workshop, participants will:

• Work through guided data exploration exercises in interactive notebooks
• Perform exploratory data analysis (EDA) to understand real-world datasets
• Apply analytical reasoning to identify patterns and meaningful variables
• Follow a structured data science workflow from analysis to model application
• Observe how machine learning models are integrated into a broader analytical pipeline

By the end of the session, participants will have experienced a realistic data science workflow—from raw data exploration to applied modeling—while understanding the reasoning that connects each stage of the process.