Online Postgraduate Award in Big Data Analytics

Structure

The online Postgraduate Award in Big Data Analytics programme consists of three modules. The first introduction module takes four weeks to complete and subsequent hands-on, practical modules take eight weeks to complete.

Modules

  1. Learning and Developing in the Digital Age
  2. Big Data
  3. Data Mining

Objective:

To provide an understanding of the core concepts related to online education with University of Liverpool Online and to help you develop the skills required for successful progression within the programme. Successful completion of this module is required to continue on to subsequent modules.

Description:

This module is intended to prepare students for successful online study as well as to ensure they understand the academic requirements associated with the programme. As a student, you will learn how to make the best use of the learning platform, to navigate the online classroom and the University of Liverpool’s online library confidently. You will also learn how to produce high quality academic work that draws on classroom materials, collaborative activities with peers and your own professional experience.

Curriculum

  • The University’s model of online learning, with respect to independent learning and continuing professional development
  • Collaborative communication of professional and academic concepts to fellow students and independently within formal assignments
  • Engagement with topical literature to develop academic arguments, carry out library research and reference sources correctly in the appropriate style
  • Self-reflection and application of feedback to improve future academic work
  • Development of an effective and realistic study plan, to anticipate the time required to complete a module successfully

Duration:

4 weeks

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, critical understanding of the concepts of data mining, the end-to end data mining process and the ability to deploy appropriate tools and techniques in line with organisational and business requirements. As a student, you will have the opportunity to gain experience in implementing data mining algorithms and evaluating their performance.

Description

This module provides students with an in-depth understanding of the concepts of data mining, including the end-to-end process and major data mining tools and techniques in common use. As a student, you will have the opportunity to apply such tools and techniques to a variety of example data sets so as to gain vital insights into their operation, and when and where these tools can best be applied. You will also have the opportunity, using the 'R' programming language, to implement several different kinds of data mining algorithms to gain a comprehensive understanding of their operation.

Curriculum

  • Data Mining concepts, including: the end-to-end Data Mining process, different types of data that can be used in this process, problems that may be encountered and methods for dealing with these problems
  • Decision Tree extraction algorithms and their implementations, coupled with a critical analysis of the advantages and disadvantages of a range of Decision Tree induction techniques
  • Association Pattern mining algorithms and their implementations together with a critical comparison of Association Rules and those extracted by Decision Tree induction algorithms
  • Cluster analysis algorithms, including the use of Self Organising Maps and the use of Neural Networks for mining data clusters
  • Mining Data Streams, including: the detection of Concept drift and how best to deal with Volume, Velocity and Veracity (VVV) problems associated with Data Streams
  • Mining text data, including a critical comparison of different Document Similarity detection techniques
  • A critical comparison of web mining techniques, including Crawlers
  • A comprehensive analysis of social network mining techniques for extracting useful knowledge from social networks, and for visualising the relationships in social networks; concluding with material on how to match Data Mining algorithms with problems.

Duration:

8 weeks

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