W1: FINM4100 Introduction to Data Analytics

W1: FINM4100
Introduction to Data Analytics
Techniques
A look at data driven insights and the ‘soon-to-be’
conventional wisdom of data-driven decision-making
as we enter the Fourth Industrial Revolution.
FINM4100 Roadmap
Week 1
Introduction
Week 2
Analytics &
Accounting
Week 3
Fintech
Week 4
Regtech 1
Week 5
Regtech 2
Week 6
Presentation
Q&A
Week 7
Economic
Modeling
Week 8
Data Analytics
Workshop 1
Week 9
Data Analytics
Workshop 2
Week 10
Data Analytics
Workshop 3
Week 11
Data Analytics
Workshop 4
Week 12
Presentation
Q&A
Lesson Learning Outcomes

A Vital Commodity

A Vital Commodity

A Vital Commodity
Data is an essential commodity of the
Fourth Industrial Revolution.
The Big Data Environment
216,000TB
Amount of new information
generated per person per year
90%
Proportion of the world’s total
big data created in the past 3
years.
$65 million
Boost in net income for every
Fortune 1000 company (if
data access is boosted 10%)
83%
Proportion of surveyed
businesses (Accenture)
investing in Big Data
initiatives.
Inevitable Transition
Force multiplier – Big data analytics and analytics
infrastructure is the means by which institutions apply force to
achieve geo-economic advantage.
Commercial activities will increasingly rely on sophisticated
network-based logistics, communications systems and a big
data ecology to recommend products, retain customers and
mitigate churn.
The goal is to turn data into information, and information into
insight.
Techniques
There are a number of widely used analysis techniques to
extract valuable insights from data.
• Association rule learning
• Classification tree analysis
• Genetic algorithms
• Machine learning
• Regression analysis
• Sentiment analysis
• Social network analysis
Association Rule Learning
Association rule learning is a method for discovering interesting
correlations between variables in large databases. It was first used by
major supermarket chains to discover interesting relations between
products, using data from supermarket point-of-sale (POS) systems.
“Are people who purchase tea more or less
likely to purchase carbonated drinks?”
Association Rule Learning
Potential applications:
• place (correlated) products in better proximity to each other
in order to increase sales
• extract information about visitors to websites from web
server logs to better understand their traffic pattern.
• analyze biological data to uncover new relationships
• monitor system logs to detect intruders and malicious
activity
• identify if people who buy milk and butter are more likely to
buy diapers (cross selling).
Activity 1
Q1. Think of another association rule learning use case*. You might want to
consider how consumer purchase decisions are correlated.
Q2. Identify the things that MAY have an association. E.g. cheese and wine,
home insurance and contents insurance.
Q3. Outline a test (e.g. hypothesis test) that can be applied to investigate the
existence and strength of that association.
Q4. Discuss how these insights may be applied to create value.
* A use case is an example that highlights the use of an instrument or framework.
Classification Tree Analysis
Statistical classification is a method of identifying categories that a new
observation belongs to. It requires a training set of correctly identified
observations – historical data in other words.
Classifying customers correctly will maximise sales and minimise expenses
(cost of acquisition, discounts, bad debt etc).
“Are these mortgages investment grade or
sub-prime?”
AAA BBB D
Classification Tree Analysis
Statistical classification is being used to:
• automatically assign documents to categories;
• categorize customers into groupings (e.g.
insurance);
• develop profiles of students who take online
courses.
Activity 2
Q1. Think of another classification tree use case*. E.g. consider a game of Black
Jack and the classification of a hand as ‘winning or losing’.
Q2. Identify the things or objects that require classification. These are
essentially the pertinent variables that influence the final outcome / decision.
Q3. Outline a methodology or procedure that can be applied to partition and
stratify those features / objects along a set of classification categories.
Q4. Discuss how these insights may be applied to create value.
* A use case is an example that highlights the use of an instrument or framework.
Genetic Algorithms
Genetic algorithms are inspired by the way evolution works – that is,
through mechanisms such as inheritance, mutation and natural selection.
These mechanisms are used to “evolve” useful solutions to problems that
require optimization.
“Which TV programs should we offer viewers,
and in what time slot, to maximize viewership?”
Genetic Algorithms
Genetic algorithms are being used to:
• schedule doctors for hospital emergency rooms,
timetables for students;
• return combinations of the optimal materials and
engineering practices required to develop fuelefficient cars;
• generate “artificially creative” content such as puns
and jokes (short phrases).
Activity 3
Q1. Using this LINK, explain how genetic algorithms can assist one of the use
cases* on the previous slide.
Q2. Identify the optimisation requirements and formulate a problem
statement^.
Q3. Outline a methodology or procedure that can be applied to seek out an
optimal solution.
Q4. Discuss how these insights may be applied to create value.
* A use case is an example that highlights the use of an instrument or framework.
^ A problem statement clearly states the problem, the objective and any constraints.
Machine Learning
Machine learning includes software that can ‘learn’ from data and generate
adaptive solutions. It gives computers the ability to compute solutions
without being explicitly programmed along a strict instruction set.
Applications are primarily focused on making predictions based on known
properties learned from sets of ‘training data’.
“What other products would this customer likely
purchase, based on their transaction history?”
Extract Transform Test Validate
Machine Learning
Machine learning is being used to help:
• distinguish between spam and non-spam email
messages;
• learn user preferences and make
recommendations based on this information;
• determine the best content for engaging
prospective customers;
• determine the probability of winning a case, and
setting legal billing rates.
Activity 4
Q1. Think of another machine learning use case*. E.g. Use of large quantities of
data to explore correlations and identify cause-effect relationships.
Q2. Identify a problem where machine learning can be applied.
Q3. Outline the data that need to be collected, the features of that data and
how these features can be used by the algorithm to develop a solution.
Q4. Discuss how these insights may be applied to create value.
* A use case is an example that highlights the use of an instrument or framework.
Regression Analysis
Fundamentally, regression analysis involves manipulating some
independent variable (i.e. pre-existing medical condition) to see how it
influences a dependent variable (i.e. insurance premium estimate). It
describes how the value of a dependent variable changes when the
independent variable is varied. It works with continuous quantitative data
(weight, speed or age) but also with categorical data (marital status,
smoking / non-smoking).
“How would social, biological, demographic and
lifestyle factors affect health insurance premiums?”
Social Biological Demography Validate
Regression Analysis
Regression analysis is being used to determine how:
• levels of customer satisfaction affect customer
loyalty;
• the volume of emergency calls relative to weather
forecast given the previous day;
• neighbourhood and size affect the listing price of
houses;
• to find compatible matches on an online dating
site or chat forum.
Activity 5
Q1. Think of another regression analysis use case*. E.g. consider all the factors
that affect the price of a good or service.
Q2. Identify a problem where regression analysis can be applied.
Q3. Outline the data that need to be collected, the features (independent
variables) in that data and how these features can be used to explain
variations in the dependent (response) variable.
Q4. Discuss how these insights may be applied to create value.
* A use case is an example that highlights the use of an instrument or framework.
Sentiment Analysis
Sentiment analysis is contextual mining of text which identifies and extracts
subjective information in source material, and helping a business to
understand the social sentiment of their brand, product or service while
monitoring online conversations.
Creative use of advanced artificial intelligence techniques can be an
effective tool for doing in-depth research. Insights generated include:
• Key aspects of a brand’s product that customers care about.
• Users’ underlying intentions and reactions concerning those aspects.
“What was the response to our social media
campaign broken down by demographic?”
Sentiment Analysis
Sentiment analysis is being used to help:
• improve service at a hotel chain by analysing
guest comments;
• customize incentives and services to address
what customers really want;
• determine how consumers really feel based on
opinions from social media.
Activity 6
Think of another sentiment analysis use case*.
Identify an area where sentiment analysis can be applied.
Outline the data that need to be collected, the features in that data and how
these features can be used to explain the ebbs and flows of public opinion.
Discuss how these insights may be applied to create value.
* A use case is an example that highlights the use of an instrument or framework.
Social Network Analysis
Social network analysis is a technique that was first used in the
telecommunications industry, and then quickly adopted by sociologists to
study interpersonal relationships. It is now being applied to analyze the
relationships between people across fields, discriplines and commercial
activities. Nodes represent individuals within a network, while links
represent the relationships between the individuals.
# An influencer is a person or entity capable of changing public opinion (e.g. Youtubers)
“How many degrees of separation are there
between viewers and influencers#?”
Social Network Analysis
Visualisation…
High School
Friends
College
Friends
Workplace
Colleagues
Other
Colleagues
Family &
Relatives
Social Network Analysis
Social network analysis is being used to:
• determine how people from different populations
form ties with outsiders;
• find the importance or influence of a particular
individual within a group;
• find the minimum number of direct ties required to
connect two individuals;
• understand the social structure of a customer
base.
Activity 7
Q1. Think of another social network analysis use case*. Use this LINK to assist.
Q2. Identify an area where social network analysis can be applied.
Q3. Outline the data that need to be collected, the features in that data and
how these features can be used to explain the underlying social dynamic and
how they relate back to business objectives.
Q4. Discuss how these insights may be applied to create value.
* A use case is an example that highlights the use of an instrument or framework.
Next Week
Accounting Analytics for Decision-Makers
• Explore the various financial statements in an annual report
and their application.
• Define and discuss the various aspects of income
statement and balance sheet analysis.
• Undertake ratios analysis using a range of conventional
accounting metrics.
• Evaluate different forms of value add in a company and
how they are measured.
• Apply ratios analysis to two listed companies in the airline
industry and make comparisons.

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