Business Intelligence Technologies Level
1 Develop an understanding of common machine learning and data mining techniques used to manipulate data
2 Critically evaluate, select and apply appropriate techniques to develop applications for a range of purposes
3 Demonstrate awareness of current limitations of various tools used to manipulate data when developing data-intensive business applications
4 Demonstrate an understanding of Big Data technologies and develop business solutions using appropriate Big Data tools
Data Analytics Application DevelopmentTask details You are assigned the task to develop an application to achieve meaningful understanding of dataset of your choice by applying tools and techniques learnt throughout this module. The objective is to experiment with multiple different technologies to perform business intelligence tasks using real-life datasets. It will require use of data mining techniques such supervised and unsupervised learning and ability to use a platform such as Weka or RapidMiner to perform the analysis.
The assessment is comprised of the following tasks:
- Provide an overview of the data mining techniques
- Provide a basic understanding and description of the dataset
- Apply three different data mining techniques to the dataset
- Provide a critical insight into the data analytics performed
- You need to demonstrate your application during the practical session
You are asked to write a technical report to explain the overall process you have followed to conduct this task (1,500 - 2,500 words), which includes
- Provide an overview of the data mining techniques studied during the module.
- Discuss the data mining approaches chosen to be used for the assignment with explanation of the choice.
- Description about the important characteristics of the dataset being used.
- Application of the chosen data mining techniques to the dataset. For each data mining technique, explain the process followed to perform the assignment task. This includes data preparation, specific configuration parameters used, possible fine- tuning of the parameters and results.
- The execution and testing of each of the chosen three data mining approaches. Screenshots of all the steps.
- Explanation on the key steps of the process supported by appropriate screenshots.
- A discussion on the outcome of the analysis highlighting the performance of the different data mining techniques
- Reference list (remember to cite all the references in the main text of your report)
Please note that this is an initial draft and may be subject to changes as a result of moderation/validation.