Prerequisites

Data mining is a broad field that combines techniques from different areas in computer science and statistics. Our model curriculum assumes that students have basic background knowledge in the following areas:

Database Systems:

Data models, query languages, SQL, conceptual database design, query processing, and transaction processing.

Statistics:

Expectation, basic probability, distributions, hypothesis tests, ANOVA, and estimating a distribution parameter.

Linear Algebra:

Vectors and matrices, vector spaces, basis, matrix inversion, and solving linear equations.

Algorithms and Data Structures:

We assume familiarity with basic data structures and general maturity of students to understand algorithms written in pseudo-code.

We believe that most computer science seniors either have covered this material in previous courses, can pick up missing material in self-study, or that the missing material is introduced by the course instructor as necessary.