Data Science and AI Foundations
Day 1:
Foundations of Data Science
Introduction to data science and its interdisciplinary applications.
Overview of the data lifecycle: Collection, cleaning, storage, and retrieval.
Tools and technologies: Excel, SQL, and Python basics.
Case study: Transforming raw data into structured formats for analysis.
Day 2:
Exploratory Data Analysis (EDA)
Statistical measures: Mean, median, mode, variance, and correlation.
Data visualization techniques using Matplotlib and Seaborn.
Identifying outliers and handling missing data.
Hands-on exercise: Conducting EDA on a sample dataset.
Day 3:
Machine Learning Fundamentals
Supervised vs. unsupervised learning: Definitions and examples.
Regression and classification algorithms: Linear regression, logistic regression.
Clustering methods: K-means and hierarchical clustering.
Practical session: Building a simple predictive model.
Day 4:
Advanced AI Concepts and Applications
Neural networks and deep learning fundamentals.
Introduction to natural language processing (NLP).
Explainable AI (XAI): Techniques for model transparency.
Group activity: Designing an AI solution for a hypothetical scenario.
Day 5:
Ethics, Compliance, and Implementation
Ethical considerations in AI development and deployment.
Regulatory frameworks governing data privacy and security.
Strategies for integrating AI into organizational workflows.
Final project presentation and feedback session.
