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Business Data Analytics

This subject is available under ICMS undergraduate degrees, please click the button below to find an undergraduate course for you.

Subject Code:

DAT102A

Subject Type:

Specialisation 

Credit Points:

3 credit points

Pre-requisite/Co-requisite: 

None

Subject Level:

100

Subject Rationale:

Business data analytics offers students a set of powerful tools to collect, transform, analyse and report data enabling organisations to make more informed strategic decisions. The subject is designed to equip students with the relevant business analytics techniques and tools to define, analyse, solve and communicate business problems. Students will learn how to gather business requirements and how to collect and transform internal and external data for further analyses. The challenges of building a robust data model based on business requirements and available data will be covered.

Students will explore contemporary analysis techniques to identify patterns in structured and unstructured data and to derive actionable insights that help decision-makers to solve a variety of business problems. They will also learn about the statistical concepts that underpin the different types of analysis such as measures of central tendency, dispersion and position, sampling, and hypothesis development and testing. Students will also be introduced to the foundations of effective communication and visualisation techniques.

Learning Outcomes:

a) Describe key data analytics concepts, tools and methodologies.

b) Evaluate business requirements and translate them into analytical use cases

c) Evaluate and prepare data for further analysis.

d) Apply a range of data analysis techniques to solve specific business problems, in line with ethical, regulatory and compliance requirements.

e) Interpret, effectively visualise and communicate the results of data analyses to key stakeholders.

Student Assessment:

WordPress Table

Broad Topics to be Covered:

W1: Introduction to business data analytics 

  • Evolving need for business data analytics and application areas  
  • Terminology (definitions, differences and similarities between business intelligence, analytics, data science) 
  • Business data analytics process overview  
  • Business data analytics ecosystem (architecture, roles and responsibilities, external stakeholders) 
  • Overview of business data analytics techniques (transactional vs. analytics processing, overview of analytics types including descriptive, diagnostic, predictive, prescriptive) 
  • Integration of system applications 
  • Security and protection of privacy 
W2: Developing organisational business data analytics capabilities 

  • Building the business case (business process support, use cases, rules) 
  • Building the data case (data types, data quality, sources, integration) 
  • Building the technical case (infrastructure including software) 
  • Ethical, regulatory and compliance requirements Assessing readiness and implications for organisational culture 
  • Creating a high-level business data analytics road map 
  • Developing scope, plan, and budget 
  • Program and project approval 
  • Common pitfalls 
W3: Data exploration with descriptive statistics  

  • Data types (structured vs. unstructured data, numerical & categorical data)  
  • Measures of central tendency (mean, median, mode) 
  • Measures of dispersion (SD, variance, range) 
  • Measures of position (percentiles, quartiles, boxplots) 
  • Data distribution (Normal distribution and Binomial distribution) 
  • Population and sampling strategies  
W4: Applied diagnostic analytics  

  • Outlier and anomaly detection 
  • Root-cause analysis 
  • Correlation analysis 
  • Causal relationships (correlation vs. Causation) 
  • Visualisation for diagnostic analytics  
W5: Exploring relationships with inferential statistics 

  • Sampling (random sampling, systematic sampling, stratified sampling, cluster sampling) 
  • Hypothesis development and associated tests (parametric and non-parametric tests) 
  • Statistical significance 
  • Estimations (point and interval estimation)  
W6: Applied predictive analytics 

  • Data mining for predictive analytics 
  • Clustering  
  • Classification  
  • Association rule mining 
  • Time-series analysis 
W7: Applied prescriptive analytics  

  • Model-based decision making and categories  
  • Decision-making under uncertainty  
  • Mathematical model components for prescriptive analytics and linear programming model 
  • Sensitivity analysis 
  • What-if analysis 
  • Goal seeking 
  • Decision trees  
  • Simulation methodologies (Monte-Carlo and discrete event simulation) 
W8: Text, web, and social media analytics  

  • Overview of text analytics and text mining 
  • Natural Processing Language (NLP) 
  • Text mining applications and processes 
  • Sentiment analysis 
  • Web analytics (content and structure mining, metrics) 
  • Search engine optimisation 
  • Social analytics (social network analysis, connections, distributions, segmentation) 
W9: Effective data visualisation 

  • Human perception  
  • Charts and graphs  
  • Chart and graph chooser decision tree 
  • Human perception and processing of visualisations  
  • Gestalt principles 
  • Polishing visualisations 
  • Dashboard design and best practices 
W10: Effective data storytelling and presentation  

  • Why data storytelling is relevant 
  • Psychology of data storytelling (human perception and processing) 
  • Anatomy of data story (six essential key elements) 
  • Structure of the data story (storytelling arc) 
  • Visual storytelling 
  • Creating efficient slide decks  
W11: Ethics in data science and future trends in business data analytics 

  • Importance of ethics in data science 
  • Mason’s PAPA ethics framework 
  • Privacy and data protection 
  • Fairness and bias mitigation 
  • Transparency and accountability 
  • Informed consent and user autonomy 
  • IoT analytics  
  • Analytics automation 
  • Real-time data visualisation 
  • Data-as-a-Service 
  • DataOps 

Please note that these topics are often refined and subject to change so for up to date weekly topics and suggested reading resources, please refer to the Moodle subject page.