Advanced Analytics

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Identify the statistical models appropriate for analysing the various decisions with complex/big data.
• Assess the relevance of statistical package outputs to the decisions being addressed.
• Work with current software packages to create models using complex data sources.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Solutions and technologies specifically designed for handling and extracting patterns from big data.
• Interpret the output of advanced analytics techniques used for complex data analytics applications.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Critically analyse practical difficulties that arise when implementing advanced data analytics methods.
• Demonstrate an ability to use software for data analytics and to interpret its output.

The topics covered include:

• Data Management: Introduction to databases, both traditional and unstructured, queries and SQL, Introduction to NoSQL and unstructured data management.
• Sources of different types of data (e.g. unstructured, network, etc.)
• Using advanced analytics model (e.g. advanced generalized linear models, random forests, deep learning, etc.) to conduct analysis with various types of data to solve different business problem settings
• Implementing Big data technologies in practice

The precise topics covered may change slightly in response to what is determined to be the most relevant based on academic and industry practice.

Teaching and learning methods

The module is delivered through pre-course reading and both theory and applied lectures. The various concepts will be illustrated using real-life data and advanced software. In addition there will be some in-lecture exercises.

Resources & Reading list

Internet Resources

Deep Learning.

Textbooks

Chollet, F. (2017). Deep Learning With Python. Manning Publications.

Hastie, T. Tibshriani, R. (2013). The Elements of Statistical Learning.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Feedback

• Assessment Type: Formative
• Feedback: Formative group feedback on students’ performance in in-lecture exercises will be provided verbally immediately after the exercises. If possible, students will use technological tools to answer questions in the exercises.
• Final Assessment: No
• Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.