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CS105

Statistical Thinking for Data Science

1 CreditsTerm 2

Description

This course is an introductory course in probability and statistics. It lays the mathematical foundation to prepare the students for computer science courses and their applications, in particular data science and related areas such as machine learning and artificial intelligence. The main topics covered in this course include probability, random variables, limit theorems, statistics, regression and inference, coupled with hands-on activities to illustrate their relevance to data science.

Requisites

Prerequisites: MATH001/COR1201 - Pre-req

Co-requisites: None

Anti-requisites: None

Attributes

Department: SCIS

Course Level: Undergraduate

Tracks: N/A

Areas: Advanced Business Technology Major Business Options Econ Major Rel/Econ Options Grad Req - Dig Tech/Data Ana (Intake 2024 onwards) IT Solution Development Core Information Systems Electives Social Sciences/PLE Major-related

Learning Outcomes

1) Model real-world scenarios using probability and random variables. 2) Apply appropriate distributions to solve real-world problems. 3) Fit a regression model on a given set of samples. 4) Perform Bayesian inference with discrete and continuous hypotheses. 5) Perform frequentist inference with maximum likelihood and confidence intervals. 6) Perform Naive bayes modelling and evaluation of classification models. 7) Perform K-means clustering and analyse Gaussian mixtures. 8) Develop programming skills and ability to implement theoretical concepts for statistical inference on reasonably large datasets.

Graduate Learning Outcomes

Disciplinary Knowledge, Critical thinking & problem solving, Self-directed learning

Competencies

Data Analytics, Formal Proof Construction, Computational Modelling, Data Engineering, Data Visualisation

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