top answer: DATA4000 Individual Case Study Page 1 Kaplan Business Scho

  

DATA4000 Individual Case Study

Don't use plagiarized sources. Get Your Custom Essay on
top answer: DATA4000 Individual Case Study Page 1 Kaplan Business Scho
Just from $10/Page
Order Essay

Page 1 Kaplan Business School Assessment Outline

Assessment 2 Information

Subject Code: DATA4000

Subject Name: Introduction to Business Analytics

Assessment Title: Data Management, Analysis and Visualisation Project

Assessment Type: Group Project

Word Count: 1000 Words + Visualisations (+/-10%)

Weighting: 30 %

Total Marks: 30

Submission: In class (In Online workshop)

Due Date: Week 10

Your Task

In groups of 4-5 you are to perform a number of data management, analysis and visualisation
tasks using your BI (Business Intelligence) tool of choice.

Consider the business question and data set below and complete Parts A and B below in a group
in Week 10 class time.

EVERY MEMBER of the group will need to have access to a laptop or a desktop computer in
order to contribute to this task. Student will need to have downloaded and installed a BI tool of
their choice. Online students will complete this task over online workshop and should have
access to a BI tool on their machines.

Page 2 Kaplan Business School Assessment Outline

Assessment Description

Your group will be asked to address the business question below with reference to the data
provided and software of your choice.

• Business Question: Based on the New York Times bestseller list 2011 – 2018, what types
of books and which titles should be stocked by the owner of a bookstore?

• Data: The data file will be provided by your lecturer at the commencement of the class.

• Software: Your team’s choice of Power BI, Tableau or Tibco Spotfire

• This assessment covers learning outcomes 3 and 4

Assessment Instructions

Face-to-Face Students

• Form groups of 4-5 before class

• Each group needs to ensure they bring at least one laptop to the class in Week 10 and have
access to a data visualisation software (free student license)

• Your lecturer will email the books data set to your groups at the start of class.

• Complete the assessment task below in groups:
o You will have 1 hour to do Part A below (Data Management) as a group after

which you will have one group member upload to the assessment portal via TurnItIn
o You will have 1.5 hours to do Part B below (Data Analysis and Recommendation)

as a group after which you will have one group member upload to the assessment
portal via TurnItIn

• This task is open book

Online Students

• Form groups of 4-5 before class

• Each group needs to ensure they attend the online workshop in Week 10 and have access to a
computer and data visualisation software (free student license)

• Your lecturer will email the books data set to your groups at the start of class.

• Complete the assessment task below in groups:
o You will have 1 hour to do Part A below (Data Management) as a group after

which you will have one group member upload to the assessment portal via TurnItIn
o You will have 1.5 hours to do Part B below (Data Analysis and Recommendation)

as a group after which you will have one group member upload to the assessment
portal via TurnItIn

• This task is open book

Page 3 Kaplan Business School Assessment Outline

Assessment Task

Part A: Data Management (10 marks, 200 words) – 1 hour

Instructions:

1. The data file will be provided by your lecturer at the commencement of the class.

2. Pre-cleaning: Open the BI tool and load the file copy, however do not import columns
“book_review_link”, “primary_isbn13”, “sunday_review_link” and “first_chapter_link”. Ensure
that the format of the columns is correct (i.e. dates should be dates and numbers should be
integers or real).

3. Currency conversion:

The price is shown in column G. The currency type for the price is shown in column Q.
Before you carry out your analysis, all prices must be converted to a single currency and this
value entered into ‘converted_price’ in column R. Undertake this conversion using the
vlookup command in Excel.

The currency pairs are as follows:

Your lecturer will advise you at the start of class which base currency to convert all prices to.

4. Data Dictionary: Write a simple data dictionary for the file which outlines the name (top row)
and data type in each of the remaining columns. Explain how you constructed this dictionary
and how it can be applied to data succession management.

5. Data Security: Explain how you will back up the data and keep it secure throughout the data

management process. What challenges are associated with this?

AUD EUR GBP USD

AUD 1 0.62 0.55 0.7

EUR 1.61 1 0.89 1.13

GBP 1.82 1.12 1 1.26

USD 1.43 0.88 0.79 1

How to use this table:

1 unit of the ROW currency is worth [X] units of the COLUMN currency.

E.g. 1 AUD is worth 0.55 GBP; 1 GBP is worth 1.82 AUD

O
N

E
O

F

T
H

E
S

E

…IS WORTH [X] OF THESE…

Page 4 Kaplan Business School Assessment Outline

Part B: Data Analysis and Recommendation (20 marks, 800 words) – 1.5 hours

1. Visualising: Create three different visualisations to show the top ten best-selling books. One
visual must address ‘age group’. Another visual must address ‘genre’. Which one is the most
effective in addressing the business question above? Why?

2. Filtering: Filter out the best five authors for the total time period and provide visualisations
showing each author and their books to see if they have had more than one book (the titles
will appear) on the list and cumulatively the sum of weeks all books were on the list.

3. Summary statistics: Find the average number of weeks any book was on the bestseller list.
How many weeks was the top selling book on the list and what was the title of the book?
Provide any other summary statistics that you think will be useful (e.g. median, mode, range).

4. Sorting: Sort the data by year and determine which book was the best seller in each year.
Make a list/chart of these best-selling book titles. Briefly explain your findings.

5. Quadrant analysis: Consider the data on two dimensions of your choice (e.g.: weeks vs

price) and analyse which books/authors/publishers perform the best on both dimensions.
Discuss any patterns that emerge.

6. Recommendation: Briefly recommend to a bookstore owner which titles they should stock.

Justify you answer with reference to the data analysis you have performed.

Page 5 Kaplan Business School Assessment Outline

Important Study Information

Academic Integrity Policy

KBS values academic integrity. All students must understand the meaning and consequences
of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct
Policy.

What is academic integrity and misconduct?
What are the penalties for academic misconduct?
What are the late penalties?
How can I appeal my grade?

Click here for answers to these questions:
http://www.kbs.edu.au/current-students/student-policies/.

Word Limits for Written Assessments

Submissions that exceed the word limit by more than 10% will cease to be marked from the point
at which that limit is exceeded.

Study Assistance

Students may seek study assistance from their local Academic Learning Advisor or refer to the
resources on the MyKBS Academic Success Centre page. Click here for this information.

Page 6 Kaplan Business School Assessment Outline

Assessment Marking Guide

Section Criteria NN (Fail)
0%-49%

P (Pass)
50%-64%

CR (Credit)
74%-65%

DN (Distinction)
75%-84%

HD (High
Distinction)
85%-100%

Part A: Data
Management

(10 marks)

Construct a data
dictionary by importing,
cleaning and managing
a data set and reflect
on security issues
associated with data
storage

Failure to
demonstrate skills in
data handling,
cleaning and
management and
failure to create a
useful data dictionary.

Incomplete
consideration of
issues associated
with storing and
sourcing large
amounts of data

Basic data handling,
cleaning and
management and
the creation of a
minimal data
dictionary.

Articulation of some
issues associated
with storing and
sourcing large
amounts of data

Competent data
handling, cleaning
and management and
the solid creation of a
data dictionary.

Clear evaluation of
relevant issues
associated with
storing and sourcing
large amounts of data

Efficient data
handling, cleaning
and management
and the creation of a
well-constructed
data dictionary.

Effective evaluation
of relevant issues
associated with
storing and sourcing
large amounts of
data

Sophisticated level of
data handling,
cleaning and
management and the
creation of a
comprehensive data
dictionary.

Comprehensive
evaluation of complex
issues associated with
storing and sourcing
large amounts of data

Part B: Data
Analysis and
Recommendation

(20 marks)

Filter, sort and
visualise data to
extract key statistics
from a data set and
perform quadrant
analysis using data
visualisation software.

Communicate a data-
driven business
recommendation
based on data analysis

Failure to extract key
statistics from data
and to follow
instructions

Visualisations are not
correct or relevant
and no relevant
recommendations are
made

Basic analysis of
data and limited
summary of
statistics conducted.

Creation of partially
accurate data
visualisations which
inform the business
recommendations

Adequate analysis of
data and summary of
main statistics.

Creation of mostly
accurate data
visualisations and
brief business
recommendations
supported by this
data

Effective analysis of
data and solid
articulation of main
statistics.

Creation of accurate
data visualisations in
line with instructions
and informed
business
recommendations
supported by data

Comprehensive
analysis of data and
thorough articulation of
key statistics.

Creation of highly
relevant and accurate
data visualisations in
line with instructions
which underlie the
business
recommendations
made based on that
data

Comments:

Page 7 Kaplan Business School Assessment Outline

Assignment Submission

One student from each group must upload their assessment to the portal via TurnItIn immediately at the end of class time.

  • Assessment 2 Information
  •  This assessment covers learning outcomes 3 and 4
  • Assignment Submission

DATA4000 Introduction to

Business Analytics

What is Business Analytics?

Workshop 1

Copyright Notice

COPYRIGHT
COMMONWEALTH OF AUSTRALIA

Copyright Regulations 1969
WARNING

This material has been reproduced and communicated to you by or on behalf of

Kaplan Higher Education pursuant to Part VB of the Copyright Act 1968 (the Act).
The material in this communication may be subject to copyright under the Act. Any

further reproduction or communication of this material by you may be the subject of

copyright protection under the Act.

Do not remove this notice

This Topic’s Big Idea

Marr, B 2015, ‘Big Data: 20 Mind-Boggling Facts Everyone Must Read’, Forbes, viewed 7 March 2017,

www.forbes.com/sites/bernardmarr/2015/09/30/big -data-20-mind-boggling-facts-everyone-m ust-

read/#6a4477ea17b1

“Artificial intelligence, however you want to

define it, that’s everything. There will be more

changes in the next five to seven years than

we’ve seen in the last 30. It will impact every

business. Data is the new gold. It’s the new

oil. It’s the new plastics.”

Marc Cuban

“By the year 2020, about 1.7 megabytes of

new information will be created every second

for every human being on the planet… By then,

our accumulated digital universe of data will

grow to…around 44 zettabytes, or 44 trillion

gigabytes.”

Bernard Marr

Learning Objectives

In this workshop, we will:

1. Define business analytics and review applications

2. Distinguish between operational and discovery

analytics

3. Review and classify the different levels of

analytics with reference to real-world examples

4. Discuss traditional statistical approaches to

business problem-solving and contrast these to

new analytics, artificial intelligence, and machine

learning methods

General Expectations for DATA4000

• Participation

• Private study

• Discussion of

Assessments

• Housekeeping

Any Questions?

Assessments

• Assessment 1: Individual Case Study (2000

words)

• Assessment 2: Data Management, Analysis

and Visualisation Software Project (1000 words

+ visuals)

• Group Assessment 3: Group Report and

Presentation: My Health Record (10 slides +

1800 words)

What is Analytics?

• Analytics is a broad term covering both statistical analysis,

data mining and data-driven techniques and algorithms

• Analytics takes advantage of the variety and large volume

of data (real-time and past) available today

• The main aim of analytics is to gain useful information

(using analyses and algorithms) and business insights from

the data

This Photo by Unknown Author is
licensed under CC BY

Activity 1: Data Journalism

Watch the video “The Age of Insight: Telling Stories with

Data” and answer the questions.

Discussion questions:

1. How does data journalism differ from traditional journalism?

2. What benefits and costs for journalists and society are associated with

data journalism?

Business Analytics

• Includes many qualitative and quantitative techniques

• Always starts with a business question/problem

• More than statistical analysis and “data-driven” decision making

• Approaches and algorithms applied to the data are flexible and run without

being directed too closely by hypotheses – the data “speaks for itself”

Definition: Using data-based algorithms, analysis and
visualisation to guide business decisions and actions

This Photo by Unknown Author is licensed under CC BY-SA

Activity 2: Analytics and Elections
How the Trump Campaign used Big Data

Cambridge Analytica “harvested private information from the Facebook profiles of

more than 50 million users without their permission, according to former Cambridge

employees, associates and documents, making it one of the largest data leaks in

the social network’s history. The breach allowed the company to exploit the private

social media activity of a huge swath of the American electorate, developing
techniques that underpinned its work on President Trump’s campaign in 2016.”

The demographic information of people who seemed to be pro- Republican

on social media was matched to voter information in the Republican

Party’s databases. Trump’s campaign analysts could then work out exactly

where to target their message, for the minimum expense.

Discussion questions:

1. How were data sources used to gain political advantage?

2. Is this ethical? W hy or why not?

Readings: https://www.nytimes.com/2018/03/17/us/politics/cambridge-analytica-trump-campaign.html

https://www.theguardian.com/uk-news/2018/mar/23/leaked-cambridge-analyticas-blueprint-for-trump-
victory

Business Analytics Cont.

▪ Operational analytics drive day-

to-day operations and decisions

Includes diagnostics, automation

of billing, supply chain tasks,

manufacturing, etc.

▪ Discovery (Innovation) analytics

creatively drive new solutions,

products and services, e.g. new

apps, robots, suggestions for

customer purchases
This Photo by Unknown Author is licensed under CC BY-
NC-SA

Operational Analytics Case Study

In response to day-to-day fluctuations in oil prices (per barrel), a multi-

national company asked Deloitte to estimate how much the company

could save in costs if they used robotic process automation (RPA) rather

than manual operations.

https://www2.deloitte.com/us/en/pages/deloitte-

analytics/articles/business-analytics-case-studies.html

• A Deloitte project team worked out the

requirements for the company.

• Software called UIPath was developed

to carry out the automations.

• This is just the first step and so far has

saved the client 1,700 hours or labour
per year (mainly related to supply chain

tasks).

Robotic process automation in oil and gas

Activity 3: Chatbots Case Study

1. Two Artificial Intelligence (AI) Chatbots talk and argue with

each other

2. Chatbot sitcom

Discussion questions:
1. What type of analytics are these chatbots an example

of and why?

2. Do the characters sound rational?

3. Do you think these chatbots could sit in on a

corporate board and solve a serious problem?
Consider: https://sloanreview.mit.edu/article/ai-in-the-boardroom-the-next-realm-

of-corporate-governance/

Watch the below two videos and answer the questions below:

Different Types of Analytics

Descriptive analytics

Predictive analytics

Prescriptive analytics

Automation

Visualisations and summary statistics on

past data, e.g. last year’s median house

price, average monthly profit, etc.

Model based on past data and used to predict

a future outcome, e.g. Regression model for

predicting heart disease

Simulation, AI or optimisation algorithm which

suggests that one outcome is a better choice

than another, e.g. transport optimisation

Putting actions in the hands of computers or

robots. Smart home devices, e.g. Google

Home

Activity 4: Different Levels of

Analytics Video

Descriptive analytics

Predictive analytics

Prescriptive analytics

Automation

Discussion questions:

1. How do prescriptive analytics

work?

2. How do they differ from

descriptive analytics?

Descriptive Analytics Examples

Visualisations and summary statistics on past data, e.g. median

apartment & house prices for each Melbourne on March 2018

• Does not generally explain why an event happened

• Covers descriptive statistics and basic data mining techniques

https://www.domain.com.au/product/house-price-report-march-2018/

Predictive Analytics Examples

Model based on past data and used to predict a future outcome,

e.g. time series model projections of the average size of a new

solar photovoltaic cells (PV) system during 2018 to 2020.

This Photo by Unknown Author is licensed under
CC BY-SA

http://www.cleanenergyregulator.gov.au/ Docum entAssets/Documents/Modelling%20report%20by%20Jacobs%

20-%20January%202018.pdf

Predicted

average PV

system size

Prescriptive Analytics Examples
Simulation, AI or optimisation algorithm which suggests that

one outcome is a better choice than another, e.g. Deep Neural

networks for YouTube recommendations

Source: https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45530.pdf

This Photo by Unknown Author is
licensed under CC BY-SA

Automation Examples

Putting actions in the hands of computers/robots

Walking robots, which is prescriptive analytics in action

Envisioning the future of robotics

Driverless Cars
Holistic Example of all Types of Analytics

Uses all levels of analytics (research)

➢ Descriptive – to assess the environment

➢ Predictive analytics – used to predict the next step

➢ Prescriptive – what should the car do next

➢ Automation – taking humans out of the equation
See http://dataconomy.com/2015/12/how-data-science-is-dri vi ng-the-dri verless-car/

This Photo by Unknown Author is licensed
under CC BY-NC-ND

Activity 5: Different Levels of

Analytics

Form a group and classify which type of analytics these items belong to:

1. A chart with median income of different professions

2. Driverless car

3. Robotic vacuum

4. Chatbot

5. Finance forecasting equation

6. Machine learning algorithm for customer sentiment analysis

7. Artificial intelligence algorithm for distinguishing weeds from tomatoes

8. Graph of the share price for Qantas over the past year

The Evolution of Analytics

Diagnostic analytics sits between descriptive and

predictive analytics. It examines data to explain why an

event occurred.

There is also alignment

with concepts such as:

* Hindsight

(looking to the past)

* Insight

(deeper understanding)

* Foresight

(Seeing to the future)

Source: www.tdwi.org

• Data driven solutions are becoming a reality

– Large data sets with a number of business variables can be

processed.

– In the past data analysis was limited to estimating parameters of a

known statistical distribution and testing that the solution conformed

(or not) to a hypothesis.

• For example

– They proposed a hypothesis e.g. “Class attendance positively affects

grades”

– They tested this hypothesis by applying a statistical test using

student data

Solving Business Problems

Traditional Statistical Method

General method

• A model, along with very specific hypotheses were created and tested

• Small samples with limited descriptive variables (characteristics) were

used because:

– Computing resources for multiple variables or large samples was

limited – small samples saved time and money

– Fitting multiple variables to a known distribution was intractable.

– The selection of the sample itself would have limited the possible

outcomes to some extent

Today’s Brave New World of

Artificial Intelligence

Why do we now use machine learning

(ML)?

• Availability of computing resources

• ML starts with minimal assumptions

• Can average over a variable

if you want to simplify analysis
– This allows for streaming analytics

Source: https://www.nature.com/articles/nmeth.4642.pdf

Customer Churn – Statistics gives a coarse

solution. ML provides a finer and more nuanced

solution space

Old and New Approaches to Gene

Discovery
Business problem: linking gene patterns to a particular disease

Statistical Method

Output is a test statistic

(p value compared to

test cut-off value)

• Test multiple hypotheses based on

the mean (average) expression of

certain genes

• The assumptions are based on

well known probability distributions

and pre-existing knowledge

• Genes are identified based on
assumptions related to distributions

ML Method

• Try several algorithms and simulations
and evaluate them using a range of

methods
– No knowledge of gene sequencing is

required

• Genes are identified based on a non-

probabilistic method (feature

importance)

• Output is directly related to the gene

expression
Source: https://www.nature.com/articles/nmeth.4642.pdf

Activity 6: Using Data

Food myths (stories about food that are

not true). The sale of food is driven by

many myths rather than big data

analytics. Consider the following food

myth: Consuming several soft drinks
per day is not harmful to adult health

W hat data do you think you would need,

and how would you attempt to

prove/disprove this myth:

1. Using statistics?

2. Using machine learning and big data?

This Photo by Unknown Author is licensed
under CC BY-ND

Final Case Study

Business Problem:

How to deal with

Metabolic Syndrome

This Photo by Unknown Author is licensed under CC BY-NC-ND

Business Problem: How to Deal

With Metabolic Syndrome

Metabolic Syndrome covers a number of disorders with

symptoms ranging from high blood pressure, tendency to

gain weight, cholesterol issues and high glucose levels.

Source: https://gigaom.com/2012/11/20/how-aetna-is-usi ng-big-data-to-improve-patient-health/

Aetna is a healthcare insurance company

Aetna decided to solve the problem of Metabolic Syndrome

amongst its 18.2 million members.

How did Aetna approach this problem?

Short answer: as “scientists”. In business increasingly we will

have to operate as scientists (and “anecdotal” experience in

business will not count as much).

Source: https://gigaom.com/2012/11/20/how-aetna-is-usi ng-big-data-to-improve-patient-health/

Data available from members:

– Conditions treated (or billed for)

– Prescriptions filled

– The types of treatments doctors prescribe

• Aetna launched “Innovation Labs” in 2012.

Business Problem: How to Deal

With Metabolic Syndrome Cont.

Innovation Labs wanted to:

• Improve patient safety – screening for ineffective medication and

poor (harmful) combinations

• Customise patient care – allow doctors access to best practice,

latest treatments, up-to-date technology

• Increase patient engagement – change behaviour to healthy life

styles

Source: https://gigaom.com/2012/11/20/how-aetna-is-usi ng-big-data-to-improve-patient-health/

Aims

Business Problem: How to Deal

With Metabolic Syndrome Cont.

Aetna: Data Analytics Problem
How to Deal With Metabolic Syndrome

Big Data Methods

– 600,000 lab results related to

screening were assessed.

(1.3 terabytes of data)

Analytics was used to:

– Personalise patient risk

– Personalise treatment

Outcomes:

– 90% of patients who didn’t

have a previous visit with their

doctor would benefit from a

screening

– 60% would benefit from

improving their adherence to

their medicine regimen

– Doctors now asked to focus

on intervention programs

Source: https://gigaom.com/2012/11/20/how-aetna-is-usi ng-big-data-to-improve-patient-health/

Next Week:
Making the Move to Analytics

• Emerging job roles

• Individual adaptation in an evolving business world

• Case studies of professionals making the move to analytics

• New concepts to discuss and explore

Place your order
(550 words)

Approximate price: $22

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more
colle writers

Order your essay today and save 30% with the discount code ESSAYSHELP