With a focus on applications in large-scale data analytics projects, the area of focus includes relational database systems, the relational model, normalization process, and structured query language (SQL). Topics related to data integration and cleaning, database programming for extract, transform, and load (ETL) operations. In addition, areas of NoSQL technologies for working with unstructured data and document-oriented information retrieval systems. Exercises also discuss index and score documents for effective and relevant responses to user queries. All of these exercises require hands-on programming experience for data preparation and data extraction using various data sources and file formats.
Theory and Concept Objectives:
- Define key terms, concepts and issues in data preparation and database management systems.
- Discuss the nature of data: structured data vs. unstructured data, small data vs. big data, clean data vs. messy data.
- Review and interpret Entity Relationship Diagrams. Generate and explain the business rules of a given data model.
- Discuss the architectures and technologies for relational database systems, data warehousing, information retrieval and search engines.
- Discuss the fundamental concepts of data storage, retrieval, analysis and data processing for data analytics applications.
- Apply the normalization process to analyze, design, and create relational database applications.
- Explain the concepts of indexing and relevance in information retrieval/search engines.
- Discuss the process used to collect data from internet of things (IoT) and social media servers.
Programming Skills objectives:
- Use a high-level programming language for data extraction, preparation and exploration.
- Compare and contrast SQL database and NoSQL database systems and demonstrate the ability to interact with SQL and NOSQL databases.
- Construct indexes for information retrieval.
- Store and retrieve geospatial data and plot data on interactive maps.