Data Analytics in the Age of Big Data - CIS7201

Assessment 1: Quizzes / polls

Students will complete a formative assessment consisting of questions at the end of each week. After attempting the questions, they will receive immediate feedback with the correct answers and explanations. This process helps students identify knowledge gaps and reinforces learning before moving on to more advanced topics.

Quizzes / polls

Assessment 2: Portfolio

Individually develop and deploy an advanced analytics application that consumes data from disparate sources, uses historical data to create a predictive model and a subsequent exploratory visualisation.

Portfolio

Assessment 1: Written Assignment

Individually produce a report resulting from investigating, analysing and describing the state of the art in the deployment of advanced analytics to a particular sector, e.g. engineering, retail marketing, weather forecasting, supply chain and logistics, manufacturing, etc. This will include an investigation of the technical and non-technical (legal, trust, institutional, human interface) related issues.

Written Assignment

<p>This module is designed to provide you with the opportunity to explore large data sets and big data analytical systems in depth. You’ll gain experience working with big data technologies and develop the skills needed to analyse and interpret large, complex datasets commonly found in real-world professional settings.</p>

School of Computing and Engineering

<p><a href="https://library.hud.ac.uk/readinglists/CIS7201">https://library.hud.ac.uk/readinglists/CIS7201</a></p>

Computing and Information Systems

CIS7201 - Data Analytics in the Age of Big Data

Data Analytics in the Age of Big Data

Apply appropriate techniques to real-live situations, such as vibro-acoustic data signals and other maintenance scenarios, in sustainable manufacturing.

Deploy software and tools for Big Data, Open platforms and emerging architectural frameworks, graph-based search and similar technologies.

Understand accurately established techniques of analysis and enquiry into Big Data fundamentals including multidimensional Big Data acquisition and integration, multi-linear subspace learning, noise reduction and sampling.