Analytics for Business Intelligence

Analytics for Business Intelligence is available as a postgraduate-level subject offered by the International College of Management, Sydney (ICMS). Please click the button below to find a postgraduate course.

Subject Code:

DAT701A

Subject Type:

Specialisation

Subject Level:

700

Credit Points:

4 credit points

Subject Aim:

The ability to harness data is key for any organisation to successfully navigate complex and uncertain business environments. By integrating concepts in the field of business intelligence, this subject equips students with the skills that they will need to guide businesses in generating data-driven insights for competitive business advantages. Students will be introduced to contemporary business intelligence techniques and tools that enable them to develop a plan for the collection, preparation, analysis and reporting of internal and external financial as well as non-financial data.

Students will utilise a range of descriptive, diagnostic, predictive and prescriptive data analytics techniques in various business situations, supporting businesses in making timely and relevant operational and strategic decisions. This subject also covers techniques used to effectively visualise data and to provide quick access to the results of the analysis using interactive dashboards.

Learning Outcomes:

a) Critically evaluate contemporary business intelligence (BI) and analytics concepts and frameworks and their application across various industries.

b) Deconstruct complex business challenges and translate them into analytical questions to lay the foundation for further analysis.

c) Develop analytical BI solutions by applying relevant descriptive, diagnostic, predictive and prescriptive techniques and tools in alignment with organisational strategic goals.

d) Effectively communicate data analytics insights and recommendations to relevant stakeholders and design dashboard solutions to support strategic decision making.

e) Evaluate and apply the latest trends in the field of business intelligence and data analytics.

Assessment Information:

Learning outcomes for this subject are assessed using a range of assessment tasks as described in the table below.

Broad topics to be covered: 

Week 1: Introduction to analytics for business intelligence

• Evolving needs for business intelligence
• Business intelligence ecosystem
• Data analytics lifecycle
• Transaction and analytical processing
• Business intelligence alignment with business strategy

Week 2: Business intelligence strategy and management

• Business intelligence governance
• Analytics maturity assessment
• Roles and responsibilities
• Business intelligence infrastructure
• Roadmap planning
• Business intelligence software tools and functionalities

Week 3: Business intelligence and decision-making

• Business problems identification and classification
• Decomposition of business problems
• Translating business problems into analytical questions
• Decision-making and modelling

Week 4: Data warehouse and business intelligence

• Data warehouse characteristics
• Data marts
• Extract, load, transform processes
• Data quality management
• Online Analytical Processing (OLAP)

Online Transactional Processing (OLTP)

Week 5: Descriptive and diagnostic analytical methods in business intelligence

• Data taxonomy
• Measures of central tendency
• Measures of dispersion
• Exploring data distribution (percentiles and boxplots, frequency tables and histograms, density plots and estimates)
• Correlation analysis

Week 6: Data sampling distribution and significance testing

• Random sampling and sampling distribution
• Bootstrapping
• Confidence intervals
• Null and alternative hypothesis
• Statistical significance
• Basic statistical tests (t-test, Anova, chi-square test)

Week 7: Predictions with regressions

• Simple and multiple linear regression
• Model assessment
• Least squares
• Regression diagnostics (multicollinearity, heteroskedasticity, non-normality)

Week 8: Predictions with classification

• Naive Bayes classifier
• Logistic regression
• Linear discriminant analysis
• Classification evaluation

Week 9: Prescriptive analytics with optimization and simulation

• Decision-making under certainty, uncertainty, and risks
• Linear programming for optimisation
• Simulation with decision-trees
• Monte-Carlo simulation
• Discrete event simulation

Week 10: Data visualisation and dashboard design

• Visualisation types and selection criteria
• Human perception and data visualisation
• Visualisation of different data variations
• Calibration of visualisations
• Dashboard types and design principles

Week 11: Business intelligence trends

• Augmented analytics
• Collaborative and integrative business intelligence
• Automation
• Self-service business intelligence
• Business intelligence as a-Service
• Business intelligence and NLP

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.