STAT1121 Data Science - Flinders University

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Overview

Design of experiments: experiments, observational studies, sample surveys; measurements & variables; replication & pseudo-replicationDescriptive statistics: graphical & numerical summaries; the shape of a distribution; data screening & outliersExploring relationships: predictor-response data; the least-squares line; residuals & transformations; prediction; the sample correlation coefficient; time seriesProbability: basic concepts; conditional probability & independence; … For more content click the Read More button below.

Design of experiments: experiments, observational studies, sample surveys; measurements & variables; replication & pseudo-replication
Descriptive statistics: graphical & numerical summaries; the shape of a distribution; data screening & outliers
Exploring relationships: predictor-response data; the least-squares line; residuals & transformations; prediction; the sample correlation coefficient; time series
Probability: basic concepts; conditional probability & independence; random variables, the binomial distribution, the normal distribution
Statistical inference: samples & populations; estimation, confidence intervals, hypothesis testing; inference for normal samples (one-sample); inference for proportion
Data management including selection, sampling and cleaning; big data storage and management issues; tools and techniques for exploratory data analysis and prediction; limitations and interpretations of inductive inference and case studies

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Tuition pattern

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Aims

This topic is directed towards students with little quantitative experience.

It serves as an introduction to the interdisciplinary and emerging field of data science. Students will learn how to combine tools and techniques from statistics, computer science and data visualization. It aims to impart an understanding of the key issues in the analysis of statistical data together with practical experience in using a modern statistical package to perform elementary statistical analysis in a wide range of applications

Assessments

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Requisites information

Anti-requisites:

Handbook

Data Science