DNA-105: Foundation of Statistics for Data Analytics

• Organisational domain(s) and key business processes
• Methods to use analytics to tell the story of the data
• Methods to use exploratory visual analysis and predictive modelling
• Methods to identify and prioritise the problems to be solved
• Methods to develop prototype algorithms
• Methods to build a data model
• Methods to use data mining to discover new business insights
• Methods to interpret patterns in data and their relevance to business issues
• Range of established and novel tools and techniques used in developing new business insights
• Methods to apply complex software tools to analyse data
• Use of statistical techniques, experimental techniques, and hypothesis testing
• Apply predictive data modelling techniques to identify underlying trends and patterns in data using statistical computing tools, methods and procedures
• Identify patterns across multiple data sets to derive insights
• Develop prototype algorithms and proof of concept demonstrations
• Make decisions about which patterns are meaningful, and which to further analyse
• Assemble data aggregations to build data models to help test problem hypotheses
• Use machine learning techniques to gain new insights from data
• Mine data to find relevant insights to develop ongoing improvements
• Assess the business insights presented to determine impact of insights on organisation
• Manage the creation of interactive visualisations of data and data study outcomes
• Use industry standard tools and techniques for data visualisation in line with organisational procedures

MODULE 1: INTRODUCTION TO FOUNDATION OF STATISTICS FOR DATA ANALYTICS
Topics:

• Introduction to business analytics
• Use cases of predictive analytics in business
• Introduction to Foundation of Statistics

Mapped to:

• 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
Topics:

• Business measurement and data
• Data connection, size and quality
• Data exploration visually

Mapped to:

• 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
Topics:

• Data insight extraction
• Data analytics results interpretation
• Data analytics results recommendation

Mapped to:

• 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
Topics:

• Algorithm development process framework
• Data selection and model development to support algorithms training and testing
• Algorithm comparison
• Algorithm recommendation

Mapped to:

• 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
Topics:

• Introduction to Data Storytelling
• Intrinsic characteristics of data
• Overview of data visualisation and patterns
• Alignment of reporting needs and choice of metrics and visualization

Mapped to:

• 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.