Data Science

Reference Material

Comprehensive exploration of data science methodologies spanning the digital landscape, data mining techniques, machine learning algorithms, and semantic web technologies. From data collection through insight generation.
Data Science Data Collection Processing & Mining Machine Learning Insights & Visualization Linked Data Digital Panorama WORKFLOW INSIGHTS

Course Materials

Foundation

Introduction to Data Science

Fundamental concepts of data science, its interdisciplinary nature, methodologies, and the complete data science lifecycle from problem formulation to insight delivery.

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Context

Panorama of the Digital World

Comprehensive overview of the digital landscape, data ecosystems, technological infrastructure, and the evolving role of data in modern society and business.

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Discovery

Data Mining Techniques

Pattern discovery, clustering algorithms, association rules, classification methods, and exploratory data analysis techniques for extracting knowledge from large datasets.

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Intelligence

Machine Learning

Supervised and unsupervised learning, regression, neural networks, ensemble methods, model evaluation, and practical applications of machine learning algorithms.

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Semantic Web

Linked Open Data

RDF frameworks, ontologies, SPARQL queries, knowledge graphs, and leveraging linked data for semantic interoperability and data integration.

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Practical Sessions

GitHub Repository

Data Science - Practical Exercises

Comprehensive collection of hands-on data science projects and exercises covering data analysis, machine learning implementations, visualization techniques, and real-world case studies.

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