DNA-106: Computational Modelling for Business Intelligence Professionals

• Types of algorithms and advanced computational methods
• Range and application of various statistical algorithms
• Range and application of various types of data models
• Usage of analytics platforms and tools
• Statistical modelling techniques
• Coding languages for programming of algorithms and signals
• Potential reasons for unintended outcomes
• Identify appropriate statistical algorithms and data models to test hypotheses or theories
• Use appropriate analytics platforms and analytical tools given specific analytics and reporting requirements
• Utilise a range of statistical methods and analytics approaches to data
• Conduct statistical modelling of data to derive patterns and/or solutions
• Perform coding and configuration of software agents or programs based on a selected model or algorithm
• Conduct tests on the actions taken and outcomes to assess effectiveness of the model
• Diagnose unintended outcomes produced by analytical models
• Propose changes or updates to the model or algorithms applied
• Implement changes to the coding and configuration of software agents or programs
• Draw relevant trends and insights from data analysis to support decision

MODULE 1: INTRODUCTION TO COMPUTATIONAL MODELLING FOR BI PROFESSIONALS
Topics:

• Introduction to advanced computational methods
• Discussion of analytic use cases
• Characteristics of statistical algorithm and data model requirements
• Assumptions and limitations of computational methods

Mapped to:

• 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
Topics:

• Trends in analytic development and production platform
• Technology strategy for development and operation of analytics

Mapped to:

• 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
Topics:

• Introduction to Statistical Modelling
• Introduction to Machine Learning Modelling
• Introduction to Time Series Forecasting Modelling

Mapped to:

• 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.

Range of Application:

• Machine Learning

MODULE 4: ADVANCED COMPUTATIONAL MODELLING CODING
Topics:

• Data processing
• Data training and testing split
• Data sample balancing (optional)
• Model setting
• Model result report

Mapped to:

• 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
Topics:

• Unintended outcomes produced by analytical models and their potential reasons

Mapped to:

• K7 Potential reasons for unintended outcomes
• A7 Diagnose unintended outcomes produced by analytical models

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.