CS105

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Statistical Thinking for Data Science

Course (UG/PG)

Undergraduate

Offering Unit/Department

Course 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.

Course 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.

Discipline-Specific Competencies

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

SMU Graduate Learning Outcomes

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

Grading Basis

GRD - Graded