EPSM unit 7 syllabus

This unit is intended to give you an appreciation of the importance and usefulness of data analytics to business and how it can be applied to the accountant’s role. The unit explains how to use commercial awareness to articulate business questions, and then identify, manipulate, and analyse relevant data by applying appropriate techniques. Following sceptical analysis of the data, valid conclusions can be drawn and recommendations made. The unit also explains how findings from analysis should be effectively visualised and communicated and finally it considers the ethical and security issues associated with data analytics. 

Section 1: Unit overview

a) Introduction

Section 2: The CRISP framework:

a) Business understanding

b) Data Preparation

c) Modelling

d) Evaluation

e) Deployment

Section 3: Big data and data analytics

a) What is big data?

b) The 3 Vs of big data

c) The value and lessons to be learned from big data

d) Platforms for big data storage and processing

e) CRISP-DM and big data quiz

Section 4: Sources of data

a) Internal sources

b) External sources

Section 5: Types of analytics

a) Analysis with descriptive analytics

b) CRM data analysis activity

c) Predictive analytics

d) Prescriptive analytics

Section 6: Data analytics methodologies

a) Artificial intelligence

b) Robotics

c) Machine Learning

Section 7: Mainstream tools and key applications for data analytics

a) Tools and applications for descriptive analytics

b) Tools and applications for predictive analytics

c) Tools and applications for prescriptive analytics

d) AI, machine learning and data analytics tools quiz

Section 8: Data visualisation and communication

a) What is data visualisation?

b) The purpose and benefits of data visualisation

c) The history of data visualisation

d) Types of data visualisation – comparison

e) Types of data visualisation – composition******************

f) Types of data visualisation – relationship

g) What makes a good visualisation?

h) Data visualisation quiz

Section 9: Scepticism

a) Scepticism in data analytics

Section 10: Ethical considerations in the use of data

a) Introduction

b) Scepticism and ethical considerations in data analytics quiz

Section 11: End of unit quiz 

Section 12: Unit summary

The learner is given data about a railway network and factors which influence ticket sales. They are required to carry out statistical regression to analyse these to predict ticket sales for a given set of factors. They are also required to evaluate the effect of changes in variables on overall ticket sales, applying professional scepticism.