Online Postgraduate Award in Data Visualisation and Warehousing

Objective

To provide a comprehensive understanding of big data tools and techniques as well as a critical understanding of an open source software framework for distributed data storage and processing. You will have the opportunity to gain a practical understanding of the issues related to the integration and deployment of big data management systems in the context of enterprise deployment.

Description

This module provides students with detailed knowledge of big data tools and techniques, and big data management frameworks that can be used to support advanced analytics. This module will consider big data management frameworks in general, but with a focus on the Hadoop open-source data storage and processing platform and its underpinning sub-systems. It also provides detailed awareness of how big data systems support data-driven decision making.

Curriculum

• Introduction to big data and Apache Hadoop, terminology and basic concepts
• Big data ecosystems and the big data landscape, the six Vs of big data
• Components of the Hadoop stack, attributes and uses of MapReduce, the Hadoop Distributed File System (HDFS) and Yarn, installing Hadoop and running ‘large dataset programs’ with Hadoop
• Modelling and managing big data, Big Data Management Systems (BDMS) and practical work with Cloudera Data Management Virtual Machine
• Big data integration and processing; configuring and working with BDMS schemes, including further work with the Cloudera Virtual Machine
• Data frames and document oriented big data systems, predictive analytics with Pandas Dataframes, MongoDB, Splunk and Datameer
• Big data processing pipelines and graph analytics, distributed processing with Apache Spark components (Spark core, pipelines, transformation engines, SparkSQL and Spark GraphX)
• Big data enterprise deployment, integration and security issues

Duration:

8 weeks

Objective

To provide a detailed understanding of the concepts and challenges of creating data warehouses. You will have an opportunity to create a data warehouse using open source technologies and free public data sets. You will also be introduced to why data visualisation is important and how to communicate these insights to tell a ‘data story’.

Description

This module introduces students to the concept and challenges of creating data warehouses and how data visualisation tools can be used to ‘tell a story’. As a student, you will have the opportunity to gain practical experience of how to create data warehouses using data from multiple sources. The module will also introduce best practices in data visualisation and data storytelling, using state-of-the-art data visualisation technology.

The module is intended to help you understand the approaches and benefits of data warehousing, and show you how to quickly gain and communicate insights into the data stored in data warehouses using data visualisation techniques.

Curriculum

• Review of the rational for, and benefits of, data warehousing, common challenges (integration, data cleansing) and different architectures (transactional, dimensional), the business case for data warehousing
• Design of relational databases (normalising vs. de-normalising, defining dimensions and facts) using public data sources and an open source database platform
• Data accuracy and data cleansing, defining rules for data warehousing
• Loading data into a data warehouse and ensuring that relevant business case objectives are met
• Benefits of visualisation and data storytelling, best practice for visualisation, how to avoid common mistakes and exploration of a selected data visualisation system
• The process of building visualisations to answer common business questions illustrated using the visualisation system introduced in earlier in the module
• Combining visualisations into a single dashboard to "tell a story" and provide insights, concerning data stored in a data warehouse and appropriate conclusions drawn
• Expanding and building on existing insights by adding trends and forecasts; comparison with other types of data analysis, such as predictive and prescriptive data analytics.

Duration