BSc Information Technology Management for Business / Course details

With the increasing availability of personal, social and business data, data analytics has become an essential and important part of business analytics and business intelligence. This importance is driven by the versatility and flexibility provided by the large variety of data analytics techniques and the more frequent and mainstream use of data analytic programming languages such as R, for problem-solving. This course unit will therefore continue to introduce students to new methods for data analytics, emphasising the potential flexibility provided by a mainstream data analytics programming language. This unit will provide students with an introduction to a statistical/data analytics programming language in order to tackle multiple types of data sources and to be able to provide insights into different business avenues.

Students undertaking this unit will increase their data analytics toolbox, will learn how to use one of the most popular data analytic programming languages, and will learn how to create different visualisations and dynamical reports for the effective communication of business insights.

Core and only available for BSc ITMB with/without IPE.

Core and only available for BSc ITMB with/without IPE.

Students will learn how to use a prominent data analytics programming language. They will gain practical experience in different analytical techniques, such as network analysis and time series analysis. This course, in addition to the analytical techniques, will also emphasize in the creation and usage of programmable visualisations for the communication of business insights by means of lab studies and or presentations.

At the end of the course unit, the student should be able to:
1.    Apply data analytical methods for both descriptive and simulation tasks.
2.    Demonstrate the ability to produce dynamic reports.
3.    Demonstrate the ability to use one of the most popular data analytics programming languages.
4.    Apply data analytical methods for predictive tasks.
5.    Demonstrate the ability to analyze non-standard data types.
6.    Demonstrate the ability to produce and communicate effective programmable visualisations.

•    Introduction to programming in a leading data analytics programming language.
•    Introduction to dynamical report writing.
•    Introduction to Data Simulation.
•    Introduction to programmable data visualisation and review of multiple linear regression.
•    Storytelling and effective communication with data.
•    Principal Component Analysis.
•    Interface and packages for gathering data from the Web.
•    Introduction to Network Analysis.
•    Time Series Decomposition.
•    Advance Machine learning.

Lectures and Labs
•    Lectures - 18hours

•    Labs - 22 hours
Group Presentations - 20mins per group in semester 2.

Formative:

Online quizzes reinforcing the understanding of content provided and of reading material and which consist of a mix of multiple choice questions, calculated response questions, fill in the blanks, and others.

Summative: 

Two midterm online quizzes, one quiz per semester each quiz providing (20%) of the grade of the module.
Group presentations (60%)

•    Informal advice and discussions during the lectures and seminars.
•    Responses to student emails and questions from course coordinator including feedback provided to a group via an online discussion forum or in Microsoft teams.
•    Written and/or verbal comments on assessed or non-assessed coursework.
•    Generic feedback posted on Blackboard regarding overall performance.

• Anderson, D. R.. (2010). Statistics for business and economics (Second edition). Andover: South- Western Cengage Learning.
• Knaflic. (2015). Storytelling with data¿: a data visualization guide for business professionals. Wiley.
• Newman, M.E.J (2010) Networks : an introduction . Oxford, Oxford University Press.
• Everitt, B. and Hothorn, T. (2011) An Introduction to Applied Multivariate Analysis with R . New York, NY, Springer New York.
• James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning : with Applications in R . New York, NY, Springer New York.
• Alan Agresti (2007) Introduction to categorical data analysis (Third Edition). Hoboken, NJ, Wiley.

Pre-requisites: BMAN11060Fundamentals of Data Analytics and BMAN24621Business Data Analytics
Co-requisites: None Dependent courses: None
Programme Restrictions: Compulsory for and available only to students on the BSc ITMB programmes.

For Academic Year 2023/24

Updated: March 2023

Approved by: March UG Committee