Lin­ear Al­gebra and Ap­plied Stat­ist­ics

Course content

This course's primary focus is to provide students with the essential mathematical tools from linear algebra and applied statistics needed by the students in the Data Science programme. Linear algebra forms the foundation for many data science concepts and algorithms, while applied statistics equips the students with techniques to analyze and interpret the results effectively to draw meaningful insights from data.

Furthermore, this course provides knowledge about,

• Discrete mathematical foundations such as set theory, functions, and relations
• Fundamental concepts of linear algebra concepts such as vectors, vector spaces, span, basic and linear independence of vectors.
• System of linear equations, Gaussian elimination, echelon form, and free variables
• Matrices, dimensions, row, column, and null spaces of matrices, rank of a matrix inner Product
• Eigenvectors and eigenvalues and their significance to diagonal matrices.
• Elementary probability theory, standard probability distributions, conditional probability and Bayes theorem
• Statistical concepts such as hypothesis testing, drawing conclusions from data, and statistical inference using estimation and confidence intervals.

The course will combine lectures, hands-on exercises, and mandatory assignments on the topics covered to solidify your understanding. The students will have ample opportunities to apply these learned concepts in real-world data science problems.