Introductory description

This module introduces basic concepts and techniques for data analysis with diverse types, including tabular, text, time series, and geospatial data. Students will gain practical skills in data cleaning, dimensionality reduction, visualisation, and exploratory analysis, as well as methods and algorithms for managing and analysing real-world datasets efficiently. It serves as a taster for students considering further study in the Computational Data Analytics module.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Overview of common data modalities: tabular, JSON, time series, text, networks, and geospatial data

Data cleaning and preprocessing: standardisation, type casting, outlier detection

Exploratory data analysis: descriptive statistics, pattern discovery

Improving data quality: handling missing data, noise filtering, class balancing

Dimensionality reduction: feature projection, embeddings, variance preservation

Data partitioning and sampling: train-test splits, cross-validation, stratified sampling

Data visualisation: matplotlib, seaborn, ggplot2, t-SNE, and geospatial mapping

Correlation and dependency analysis: covariance, mutual information, partial correlation

Scalable data analysis: 5 Vs, stream processing, MongoDB, PySpark, TensorFlow, cloud computing, data lakes and warehousing

Learning outcomes

By the end of the module, students should be able to:

• Apply data cleaning, preprocessing, and quality assessment techniques to prepare datasets for analysis.
• Perform exploratory data analysis and visualisation to uncover patterns, anomalies, and structural insights.
• Implement methods for data integration, dimensionality reduction, and feature projection.
• Utilise scalable tools and frameworks to analyse and process large datasets.

Indicative reading list

Reading lists can be found in Talis

Research element

Coursework will include a research element.

Subject specific skills

in line with the learning objectives students will acquire skills in:
Applying data cleaning, preprocessing, and quality assessment techniques to prepare datasets for analysis;
Performing exploratory data analysis and visualisation to uncover patterns, anomalies, and structural insights;
Implementing methods for data integration, dimensionality reduction, and feature projection;
Utilising scalable tools and frameworks to analyse and process large datasets.

Transferable skills

Being able to apply Data Analytics knowledge and understanding of specialist theoretical and methodological approaches, suggesting and incorporating interrelationships with other relevant disciplines in abstract and unpredictably complex contexts.

Students will obtain the cognitive skills to critically contribute to existing discourses and methodologies in Data Analytics, suggesting new ideas, and designing systematic studies in Data Analytics based on critical analysis and evaluation.

Students will obtain practical skills in organising and communicating information, improving interpersonal, team
and networking skills through engaging in classes and computer laboratories. Formative assessment will allow students to strategically enhance their own learning.

Data Analytics is an area with immediate relevance for increasing ethical awareness and its practical application regarding privacy concerns. The associated values will help understanding the importance of personal responsibility and ethical leadership.