Subsections
[Cr:4, Lc:3, Tt:0, Lb:0]
- Review of Python and relevant packages (numpy, matplotlib, scipy, seaborn, pandas, Flask2)
- Introduction to R.
- Review of basic linear algebra: vector spaces, linear transformations, diagonalization, computation of eigenvalues, eigenvectors.
- Basic Optimisation: Linear programming, Simplex, Duality, Primal Dual Algorithms, Gradient Descent, Newton’s Method, Stochastic Gradient Descent, Lagrange Duality
- What does it mean to learn from data? Motivational Case Studies, Data Science Process, Key Components of Learning, Population vs. Sample, Decision Boundary, Types of data, Typical Issues with Data, Types of Learning, Learning as search
- Handling of Big Data, Database management systems, Compression techniques, Missing Value imputation, scaling, normalization
- Instance and Hypothesis Space, Introductions to Search Algorithms, Cost Functions Version Spaces, Linear Regression, Nearest Neighbours
- P.G. Ciarlet, Introduction to Numerical Linear Algebra and Optimisation, Cambridge University Press.
- Kenneth Baclawski, Introduction to Probability with R, Chapman and Hall/CRC.
- Roger Peng and Elizabeth Matsui, The Art of Data Science, Lulu Publishers.
- Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley Publications.
