**Introduction to big data. **Characteristics of big data. Big data and data science.** **Relational databases and big data. Distributed data systems. Hadoop ecosystem.

**Big data management. **Structured and semi-structured data models. Non-relational (NoSQL) data models. Data models and database systems for big data. Domain-specific languages for big data. Monitoring big data systems.

**Big data processing. **Querying and retrieval.

Paradigms for computing with data. Processing pipelines and aggregators. Basic algorithmic building blocks and patterns. Hadoop. Spark.

**Data analytics with big data. **Data analytics tools. Basic statistics. Clustering. Associations. Predictive modeling. Spark machine learning library MLib.

**Big data and graph analytics.** NoSQL graph databases for big data. Neo4j graph database. Graph querying with CYPHER. Basic graph analytics with Neo4j and CYPHER.

**Practical aspects of big data analytics. **Processing heterogeneous data.** **Processing data streams.

- nosilec: Matjaž Kukar