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Mathematics and Statistics for Data Science

This module aims to cover the key statistical concepts and techniques you will need to interpret the results you might generate through data analysis.

The areas covered in this module include probability theory, likelihood, common distributions, confidence intervals, hypothesis tests, parametric and non-parametric tests.

Topics covered

  • Review of differential calculus
  • Vectors and matrices
  • Geometry of matrices and derivatives: linear transformations and partial derivatives
  • Descriptive Statistics: Data and Data Presentation, Measures of Location and Variability
  • Probability Theory
  • Probability Distributions
  • Sampling Distributions
  • Statistical Significance and Tests of Hypothesis
  • Analysis of Variance
  • Linear Regression and Correlation.

Credits

15 (150 hours)

Assessment

  • Summative coursework (30%)
  • Written examination (70%)