This course bridges the gap between knowledge graph technology and data science. Aimed at data scientists and analysts, it explores how semantic knowledge graphs can enhance data analytics, data integration, and business intelligence. Participants will learn to unify and query diverse datasets using semantic models, apply graph analytics algorithms, and leverage knowledge graphs to uncover insights that traditional data techniques might miss. Given that graph technologies are becoming core to data innovation (with Gartner predicting 80% of data analytics innovations will use graph technology by 2025dataversity.net), this course equips professionals with cutting-edge skills in data connectivity and analysis. (Some experience with data analysis and basics of SQL/RDF is recommended.)
Curriculum:
Module 1: Introduction to Knowledge Graphs in Analytics – Why knowledge graphs matter for data science; how they unify data with context and enable flexible data integration across sourcesdataversity.net for comprehensive analysis.
Module 2: Querying Graph Data for Insights – Using SPARQL for data exploration and analysis: writing analytical queries, aggregations, and combining data from multiple sources (e.g., querying an enterprise knowledge graph to answer complex business questions).
Module 3: Graph Analytics and Algorithms – Applying graph theory algorithms on knowledge graphs for data science use cases: centrality measures, community detection, path analysis, and recommendations, and how these differ from standard SQL analytics.
Module 4: Integrating Knowledge Graphs with Data Science Tools – How to bring knowledge graph data into familiar data science environments: using Python notebooks to query graphs (SPARQL/Python integration), converting graph query results into pandas dataframes, and blending semantic data with other datasets for analysis.
Module 5: Data Visualization and Storytelling – Techniques for visualizing knowledge graph data and query results (network visualizations, knowledge cards), and presenting insights derived from semantic analysis to stakeholders.
Module 6: Use Cases in Data Science – Case studies where knowledge graphs improved analytics (e.g., enriching customer data for better segmentation, connecting research data in healthcare for discovery), and a capstone exercise where learners design an analysis that combines data from multiple sources via a knowledge graph.
