Singapore University of Social Sciences

Mathematical Foundations for Data Science

Mathematical Foundations for Data Science (DSM101)

Applications Open: 01 October 2020

Applications Close: 01 December 2020

Next Available Intake: January 2021

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Science & Technology

Schemes: Lifelong Learning Credit (L2C)

Funding: To be confirmed

School/Department: School of Science & Technology


Synopsis

Mathematical Foundations for Data Science will introduce students to the essential matrix algebra, optimisation, probability and statistics required for pursuing Data Science. Students will be exposed to computational techniques to perform row operations on matrices, compute partial derivatives and gradients of multivariable functions. Basic concepts on minimisation of cost functions and linear regression will also be taught so that students will have sound mathematical foundations to proceed and understand standard algorithms in Data Science and Machine Learning.

Level: 1
Credit Units: 5
Presentation Pattern: Every January

Topics

  • System of linear equations
  • Elementary row operations
  • Partial differentiation
  • Lagrange multipliers
  • Minimization of cost functions
  • Measures of central tendency
  • Discrete random variables
  • Binomial and Poisson models
  • Simple linear regression
  • Sampling distributions
  • Multiple regression models
  • Statistical Inference on coefficients

Learning Outcome

  • Determine the gradient or directional derivative of a multivariable function in a given direction.
  • Implement elementary row operations to reduce given matrix to row echelon form.
  • Interpret regression model parameters from data.
  • Employ probability models in practical settings.
  • Apply linear and multiple linear regression models.
  • Use Python to perform calculations in matrix algebra, differential multivariable calculus and statistical analysis.
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