Exam (elaborations)

Exam (elaborations) BUSINESS ANALYTICS (COIS712023)

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Module
BUSINESS ANALYTICS (COIS712023)

Institution
Staffordshire University

he purpose of this assignment is to provide students with an opportunity to apply the knowledge and skills they have acquired in data analytics techniques to perform business analysis. In this assignment, you will use R OR Python to perform various tasks such as data pre-processing, data visualizat...

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Uploaded on July 19, 2023
Number of pages 5
Written in 2022/2023
Type Exam (elaborations)
Contains Only questions

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Final Assignment for

Business Analytics
(COIS71202,3)

Submission Deadline:
Thursday 18th May 2023 11:59pm

1

, Assignment Description
The purpose of this assignment is to provide students with an opportunity to apply
the knowledge and skills they have acquired in data analytics techniques to perform
business analysis. In this assignment, you will use R OR Python to perform various
tasks such as data pre-processing, data visualization, and model development. You
will also apply machine learning techniques such as regression, classification,
association rules, and clustering to analyse real-world business data.

Objective
The objective of this assignment is to give MSc students hands-on experience in
applying R (Python) and machine learning techniques to real-world business data.
Students will gain an understanding of how these techniques can be used to support
decision-making in a business environment.

Assignment Requirements
Data
The data should be relevant, accurate and up to date. You are required to
analyse a dataset of your choice. You can select a real-world business dataset
from an appropriate source (e.g., Kaggle, UCI Machine Learning Repository,
etc.).

Tasks
1. Data Cleaning and Pre-processing: Students must clean the collected data to
ensure that it is in a suitable format for analysis. This includes removing missing
values, transforming variables, and dealing with outliers.
2. Exploratory Data Analysis: Students must perform exploratory data analysis
(EDA) on the cleaned data. This includes creating descriptive statistics,
visualizing data distributions, and creating correlation and regression plots. The
aim of the EDA is to identify any patterns and relationships in the data.
3. Model Building: Apply one of taught techniques in the lectures such as
regression, classification, association rules, or clustering techniques (You must
select one method based on the dataset you have chosen and the scenario you
have created) to the data to address specific business questions. The models

2

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