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Centre for Professional Development and Lifelong Learning

Postgraduate Modules


Postgraduate Modules

Big Data Analytics

Overview

This course examines the theoretical and practical techniques used in the analysis of large data sets. It covers data collection, data pre-processing, predictive, prescriptive and descriptive analytics as well as social network analytics. The course will facilitate discussion of case studies that involve big data analytics including fraud detection, web analytics and recommender systems. This course targets students who intend to pursue careers as data scientists work in the field of data analytics. Due to the data and technology intensive nature of the material, this course will be delivered primarily through face-to-face instruction and hands-on lab sessions.

What will I Learn?

On successful completion of this course, students will be able to:

· Describe the analytics process model
· Identify the requirements of the analytical model
· Explain the nomenclature used in data analytics
· Prepare data for pre-processing and collection
· Produce cleaned data to be used for analysis
· Analyse large data sets using different techniques
· Recommend data collection and analytical approaches to use based on project requirements.

Who Should do this Course

Computer Science/ IT graduates who want to acquire data science skills; Graduates with statistical knowledge who want to acquire data science skills IT managers/ leaders with data analytics job requirement

Important Information

The goals of this course are to:

· Equip students with the skills required to collect large organizational data sets
· Explore the techniques used to extract tactical and strategic insight from large data sets.

At a Glance

  • Admissions Term: 2021/2022 Semester I
  • Date: (September - October)
  • Time: TBA
  • Duration: One (1) Semester
  • Certificate Awarded: Professional Development Certificate of Competence
  • CRN: 10158
  • Course Code: COMP6362
  • Capacity: 10
  • Cost: BDS $2,100 (US $1,050) {with assessment} ; BDS $1,790(US $895) without assessment}

The following topics/concepts/theories/issues will be addressed:
· Basic nomenclature and the analytics process model
· Data collection, sampling and pre-processing
· Predictive, descriptive and prescriptive analysis
· Social network analytics
· Data analytics issues: o Benchmarking o Data quality o Backtesting o Privacy

There are no specific requirements to be eligible to enroll is this course.

This course will be delivered using a combination of interactive lectures, discussions and laboratories. Students are required to read preparatory material and complete assigned homework given by the lecturer and be prepared to participate in discussions. Laboratory sessions will be used to provide hands-on experience with big data analytics. Students are expected to prepare assigned developmental exercises prior to arriving at the laboratory. Credit Hours Four (4) hours of lectures and four (4) hours of labs each week for six (6) weeks.

Dr. C. Gittens (Coordinator)