MODULE 1: INTRODUCTION TO COMPUTATIONAL MODELLING FOR BI PROFESSIONALS
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K1 Types of algorithms and advanced computational methods
K2 Range and application of various statistical algorithms
K3 Range and application of various types of data models
A1 Identify appropriate statistical algorithms and data models to test hypotheses or theories
A3 Utilise a range of statistical methods and analytics approaches to data

Rationale for Sequencing of the Units

The first learning unit introduces learners to advanced computational methods. Learners are taught the various types of statistical algorithms, the characteristics and assumptions and limitations of computational methods. There is a discussion of analytic use cases, e.g. diagnostic analytics, clustering, prediction, network analysis, text analytics, image analytics, video analytics, modelling and simulation.

MODULE 2: TECHNOLOGY STRATEGY
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K4 Usage of analytics platforms and tools
A2 Use appropriate analytics platforms and analytical tools given specific analytics and reporting requirements

Rationale for Sequencing of the Units

Subsequently, the next learning unit teaches learners technology strategy by first going through trends in analytic development and platform strategy.
The analytics platforms and analytical tools learners introduced to learners include R, SAS, Gretl, Orange.

MODULE 3: ADVANCED COMPUTATIONAL MODELLING DEVELOPMENT
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K5 Statistical modelling techniques
A4 Conduct statistical modelling of data to derive patterns and/or solutions
A6 Conduct tests on the actions taken and outcomes to assess effectiveness of the model
A10 Draw relevant trends and insights from data analysis to support decisions

Rationale for Sequencing of the Units

In the next learning unit, the learners move to the next step in the sequence where they are brought through statistical modelling techniques for machine learning, e.g.
- Supervised machine learning –decision tree
- Unsupervised machine learning –clustering
Learners work on exercises to practice Decision Tre, K-mean clustering, hierarchical clustering.

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MODULE 4: ADVANCED COMPUTATIONAL MODELLING CODING
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K6 Coding languages for programming of algorithms and signals
A5 Perform coding and configuration of software agents or programs based on a selected model or algorithm
A8 Propose changes or updates to the model or algorithms applied
A9 Implement changes to the coding and configuration of software agents or programs

Rationale for Sequencing of the Units

In the next learning unit, the learners are taught data processing, data training and testing split, data sample balancing (optional), model setting and model result report.
The software agents or programs learners use include Python, R, Gretl, JMP, SAS, Orange.

MODULE 5: POTENTIAL RISKS OF ANALYTICS MODEL
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Rationale for Sequencing of the Units

The last learning unit teaches learners on potential risks of analytics models by looking at unintended outcomes, the potential reasons for unintended outcomes and how to diagnose various unintended outcomes.

DNA-106: Computational Modelling for Business Intelligence Professionals