Courses :: Module, INTRODUCTION TO DATA SCIENCE 2

	Overview
	To provide students with a deeper and more practical understanding of data exploration and analysis Topics include: Messy data and data cleaning Observational Studies Controlled experiments Sample Surveys Data privacy and GDPR Introduction to data anonymization Image and text data Supervised and unsupervised learning Predictive Analytics Model validation and evaluation

Overview

To provide students with a deeper and more practical understanding of data
exploration and analysis

Topics include:
Messy data and data cleaning
Observational Studies
Controlled experiments
Sample Surveys
Data privacy and GDPR
Introduction to data anonymization
Image and text data
Supervised and unsupervised learning
Predictive Analytics
Model validation and evaluation

	Learning Outcomes
	On successful completion of the module, students should be able to: Know how to uncover useful information from messy data; Be aware of various ways in which data are collected and compiled; Be aware of the differences between observational and experimental studies; Have a knowledge of the biggest challenges currently facing data security and privacy; Be familiar with image and text data; Be familiar with common methods used in predictive analytics for making predictions about future unknown events; Be informed about model evaluation and validation approaches with the goal of understanding how we can make our models as generalizable as possible;

Learning Outcomes

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? Yes Continuous assessment mark is carried forward to the Autumn University scheduled written examination (Autumn): 120 minutes, On-campus

Autumn Supplementals/Resits

	Pre-Requisites
	DS151

Pre-Requisites

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Courses / Module

INTRODUCTION TO DATA SCIENCE 2

Semester 1

Semester 2