MODULE 1: INTRODUCTION TO FOUNDATION OF STATISTICS FOR DATA ANALYTICS
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K1 Organisational domain(s) and key business processes
K4 Methods to identify and prioritise the problems to be solved
K11 Use of statistical techniques, experimental techniques, and hypothesis testing
A1 Apply predictive data modelling techniques to identify underlying trends and patterns in data using statistical computing tools, methods and procedures

Rationale for Sequencing of the Units

The first learning unit starts with an overview and introduction of the Foundation of Statistics course, to begin the building blocks in statistical techniques, hypothesis testing and predictive analytics.

MODULE 2: DATA SCOPING, EXPLORATION AND SENSE MAKING
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K3 Methods to use exploratory visual analysis and predictive modelling
K7 Methods to use data mining to discover new business insights
A2 Identify patterns across multiple data sets to derive insights
A4 Make decisions about which patterns are meaningful, and which to further analyse

Rationale for Sequencing of the Units

Subsequently, the next learning unit teaches learners on data scoping, data exploration and data sense making through predictive modelling, data mining, etc.
With the data set, they begin to learn about data pattern recognition and which patterns to analyse and investigate further.

MODULE 3: DATA INSIGHT EXTRACTION AND EXPLANATION
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K8 Methods to interpret patterns in data and their relevance to business issues
A6 Use machine learning techniques to gain new insights from data
A7 Mine data to find relevant insights to develop ongoing improvements
A8 Assess the business insights presented to determine impact of insights on organisation

Rationale for Sequencing of the Units

In the next learning unit, the learners move to the next step in the sequence whereby they extract data for insight and analysis.
Learners are taught methods such as clustering and decision tree.

MODULE 4: DATA MODELS AND PROTOTYPING ALGORITHMS
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K5 Methods to develop prototype algorithms
K6 Methods to build a data model
A3 Develop prototype algorithms and proof of concept demonstrations
A5 Assemble data aggregations to build data models to help test problem hypotheses

Rationale for Sequencing of the Units

In the next learning unit, the learners are brought through data models and developing proof of concept algorithms.

MODULE 5: DATA STORYTELLING
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K2 Methods to use analytics to tell the story of the data
K9 Range of established and novel tools and techniques used in developing new business insights
K10 Methods to apply complex software tools to analyse data
A9 Manage the creation of interactive visualisations of data and data study outcomes
A10 Use industry standard tools and techniques for data visualisation in line with organisational procedures

Rationale for Sequencing of the Units

The last learning unit pulls the building blocks together.
Learners have been brought through data analysis, to data analytics and learn about data visualisation and data storytelling.

DNA-105: Foundation of Statistics for Data Analytics