Data science is a complex technical field and therefore some prerequisite training in mathematics and computer science is essential for success in this program. These prerequisites may be achieved through an undergraduate program, through professional experience, recommended online self-study resources, or a combination of the above.
Our program is academically rigorous and assumes some knowledge of the following areas:
A good predictor of academic success in this program is a high level of mathematical maturity with some background in calculus and linear algebra. Specifics include:
- Calculus, preferably acquired through a university-level course, including topics such as differentiation and integration rules for single variable calculus, mathematical functions; differentiation, and integration of multiple variables.
- Linear algebra, developed in a stand-alone or as part of a discrete mathematics course including basic vector and matrix manipulations and matrix inversion.
If you’d like a refresher on these topics, we encourage you to use: Integration Rules, Definite Integrals, and Mathematical Functions, Basic Vector, Basic and Intermediate Matrix Skills.
The program is built around coding data science procedures. Incoming students do not need to have mastery in any particular computer language. They should instead have a solid understanding of the principles of computer programming typically developed in a course on basic programming in a language such as Java, C++, or Python. Some specific topics should include: variables and assignment, conditional operations, logical operators, loops, functions, parameters, debugging, exception handling, and file manipulation.
Ability to communicate clearly, coherently, and professionally in spoken and written form.