Page 178 - FINAL_HANDBOOK_20242025

Faculty of Science Handbook, Academic Session 2024/2025

SIT3018 SIT3020
NON-PARAMETRIC PYTHON FOR DATA
STATISTICS SCIENCE
Introduction to hypothesis Description: Introduction to
testing, sign test and signed Python programming; Control
rank test, Mann-Whittney test, statement and program
Kruskal-Wallis test, runs test, development; Python data
contingency tables, median structures, strings and files;
test, goodness of fit test, Functions; Lists and Tuples;
Spearman's rank test, Dictionaries and sets; Array-
Kolmogorov Smirnov test, oriented programming with
permutation test, kernel density NumPy; Pandas series and
estimation, spline regression DataFrame; Data wrangling;
estimation. Object-oriented programming;
Python libraries for data
Assessment: analysis such as Jupyter
Continuous Assessment: 40% Notebook, SciPy, mglearn and
Final Examination: 60% matplotlib.

Data science: Basic descriptive
SIT3019 statistics; Simulation and
INTRODUCTION TO static/dynamic visualisation;
BAYESIAN STATISTICS data mining tools such as
principal component analysis
Bayes' Theorem. Bayesian and discriminant analysis.
framework and terminology.
Bayesian inference. Prior Big Data and Cloud case study:
formulation. Implementation Deep learning; convolutional
via posterior sampling. and recurrent neural networks;
Bayesian decision theory. Reinforcement learning;
Hierarchical models. Network analysis.
Application to real-world
problems. Assessment:
Continuous Assessment: 50%
Assessment: Final Examination: 50%
Continuous Assessment: 40%
Final Examination: 60%
SIT3021
INDUSTRIAL TRAINING
Candidates are required to
spend a minimum of 16 weeks

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