With access to massive amounts of data and computers being able to store and process this data faster than ever, the availability of data provides increasingly more opportunities for organizations to make data-driven decisions. Big Data Analytics is key to improve innovation, productivity and competition in today business environment. This module focuses on developing the skills and knowledge of a data scientist. It provides an understanding of key theories and applied methodologies relating to data analytics in the context of the business environment. It examines data quality, data mining, and analysis and data modeling in the context of translating data into real-world decisions.
Upon successful completion of the module, a student will be able to gain deeper knowledge on how to manage and analyse big data.
- Understand the nature of data quality.
- Being able to work with massive amounts of data.
- Identify technologies to store and mine data.
- Select reliable and high quality data.
- Interpret results that are useful to the end user.
- Introduction to data analytics
- Data acquisition and manipulation
- Data representation and visualization
- Descriptive statistics
- Hypotheses testing & Regressions
- Basics on machine learning
80% for group coursework, 20% for online individual exam.
Current students should refer to Moodle for specific details of the current year’s assessment.
Lecture notes will be provided during the course. The suggested books are listed below.
Big Data: Using SMART Big data, Analytics and Metrics to Make Better decisions and improve performance, by Marr, B (Wiley & Sons Inc., 2015)
Data Smart: Using Data to Transform Information into Insight, by Wiley, J. (John Wiley & Sons Inc., 2014)
Complete Business Statistics, 7th Edition, by Aczel and Sounderpandian (Irwin, 2008)
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, by Wes McKinney (O’Reilly, 2012)
Business Statistics for Competitive Advantage with Excel 2010, Third Edition, by Cynthia Fraser (Springer 2013)