Course overview
The exceptional development, since the 1990s, of new hardware dedicated to high-performance mathematical computations, and the parallel availability of large data sets due to the rise of the internet, has made viable the field of data analytics, where large quantities of data can be handled and studied. This course introduces learners to two of the mathematical pillars of data analytics, linear algebra and calculus. The emphasis is on developing skill sets allowing learners to progress to more advanced courses. The also introduces Python as a means of implementing mathematical methods with technology.
- Matrices
- Differentiation
- Analysis, Verification, And Differential Calculus In Data Science
- Visualising Data And Calculus In Data Science
- Solving Linear Equations
Course learning outcomes
- Perform algebraic transformations on linear equations and inequalities, on quadratic, exponential, and logarithmic equations
- Perform vector and matrix algebra, including obtaining determinants and matrix inverses
- Obtain matrices associated to special transformations (projection matrices, orthogonal matrices)
- Obtain eigenvalues and eigenvectors
- Use the methods of calculus to solve basic optimisation problems