Prof Milan Vojnovic

This course is available on the BSc in Actuarial Science and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.

Programming for Data Science (ST101) or Managing and Visualising Data (ST115)

The goal of this course is to cover basic concepts of database management systems, including relational and other types of database management systems. 

The topics covered will include basic concepts of the relational model, creating and modifying relations using Structured Query Language (SQL), integrity constraints and relations, querying relational data, views, basic SQL queries using SELECT operator, nested queries, aggregate operators such as GROUP BY, application development using JDBC, Internet protocols such as HTTP and XML, storage and indexing, tree-structured indexing using B+ trees, hash-based indexing, query evaluation and algorithms for relational operations, external sorting, transaction management and concurrency, database schema and normal forms, and overview of no-SQL databases such as key-value stores, document, and graph databases. 

The course will demonstrate how various theoretical principles are implemented in practice in a variety of database management systems such as SQLite, MySQL and PostgreSQL. 

20 hours of lectures and 15 hours of seminars in the LT.

Students are required to use their own laptops and install Python on their laptops. 

Students not having a laptop of their own, which can be used for the purpose of the course, will be offered to use personal computers available in seminar rooms.

Students will be expected to produce 10 exercises in the LT.

A set of exercises will be given in each teaching week.

Essential Reading: 

• R. Ramakrishnan and J. Gerhke, Database Management Systems, McGraw-Hill, 2002
• J. Hellerstein and M. Stonebraker, Readings in Database Systems, 4th Edition, 2005

Additional Reading: 

• C. J. Date, An Introduction to Database Systems, 8th Edition, Addison-Wesley, 2000
• J. Leskovec, A. Rajaraman and J. Ullman, Mining of Massive Datasets, 2nd Edition, Cambridge University Press, 2014, http://www.mmds.org/

Coursework (40%) in the LT.
Project (60%).

Students are required to hand in solutions to 4 sets of exercises, each accounting for 10% of the final assesment. 

The project will require solving a practical task involving data model design, loading of data into a database, and querying the imported data in the database.