Perform data mining. Upload the dataset into business

GIS6008 Analytics and Business Intelligence - Gulf College

Aim(s)

Business intelligence and analytics are the skills, processes, technologies, applications and practices used to support decision making. This module provides historical, current, and predictive views of typical analytical operations. This module covers the common data analytics and business intelligence technologies of OLAP, data mining and predictive analytics, and develops them by incorporating the areas of geographical information systems (GIS) and social network analysis. Students will be presented with the prevalent representations of business, spatial, social and other data, and draw on various methods of presentation, analysis, and applications of these data, using a variety of appropriate software tools, including open-source. Students will focus on the various ways in which different data can be collected, processed (including various file formats) and analysed to solve business and other real world problems.

Learning Outcomes

On successful completion of this module, students should be able to:
• Demonstrate understanding of the key technologies relating to data warehouses, data analysis, data mining, predictive and other analytical technologies (e.g. geospatial, social), and be able to apply them appropriately in real world scenarios.
• Demonstrate understanding of and apply of specialist technologies used to harvest, analyse and visualise various kinds of data.
• Critical appraise analytical techniques to develop models and business intelligence from data.

OLAP
Data analysis and data mining Predictive analytics Geospatial technology
Social networks analysis
The following list is indicative content, and the final delivery will depend on emerging literature. Data analysis
Data collection, measurement, and computer analysis techniques Data preparation (using a wide range of techniques)
Types of inference Hypothesis testing Data mining

The module will be taught presenting various data representations (standard projections, most common formats: Shapefiles, GPX, GML, KML etc) and business objectives (from Sports, Environment, Space, Business/marketing, Crime etc), and will cover emerging research trends such as Computational Movement Analysis and semantic trajectories.

Social network analysis
Students will learn how people are using social computing resources, how to access and deploy these Resources. In Social Computing the data sources we study include digital media such as social media channels, blogs, forums, image sharing sites, video sharing sites, aggregators, classifieds, complaints, Q&A, reviews, Wikipedia and others, but also those from augmented reality, robotics and virtual devices, wearable computers and sensors and artificial intelligence.

Students will learn use of specialist APIs for analyzing social media. They will also be made aware of and
use "mashup" tools for combining different types of data feeds.

Key concepts and principles in network/ graph theory Centrality and centralization
Components, cores and cliques Cohesion, brokerage and ranking Positions, roles and clusters Block modeling
Longitudinal network data
Scale-free and small-world networks Geographical networks

Case Study

In today's modern world of education, educational video games and any other gamification tools have a huge impact to teaching and learning (Nebel et.al, 2020). These educational technologies play a significant role in the delivery of quality lessons to learners. It also allows learners to take active roles in developing their technological and communication skills that are needed in their academic and professional careers.

Mr. Abdulrahman, the sales director of GamerZone located in Muscat has compiled his sales and other unstructured data in a form of a dataset. The dataset given below is a collection of different video games with its entities such as customer Id, customer name, segment, product id, product name, and sales.

Note: Your tutor will provide you the dataset in Moodle which will be used to perform this case study.

You are tasked to prepare a report that would help Mr. Abdulrahman analyse his sales data. Your main task is to predict sales to improve sales performance of the company. Details of the tasks are as follows:

a. Background Information. Write a description about the dataset and its importance to the business (100 words)
b. Perform data mining. Upload the dataset into business intelligence tool - predictive analysis. Perform the relevant data analysis task using your selected data mining techniques (e.g. classification, clustering, etc.). Elaborate your processes with discussions - (200 words)
c. BI reports. Prepare a BI sales report. Make sure you include visual samples of the reports you have produced and explain the content. (500 words)
d. Dashboards. You are required to design a dashboard (using Tableau) to demonstrate the results of your analysis. Make sure you include relevant visualisations you have produced for your dashboard and explain the content. (600 words)
e. Develop a Model. Design the model appropriate for this scenario and demonstrate how this model help improve business decision making. (400 words)
f. Evaluation. Critically evaluate the whole process of business intelligence performed in this case study and include your proposed customer retention programs. (200 words)

Attachment:- Business Intelligence.rar


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