Data-driven decision making is a guide that enables businesses to make strategic and informed business decisions. Businesses more than ever strive to be more objective and make data-driven decisions not just to have a competitive edge but also for survival. It is therefore important for organisations to build the skills to identify challenges, spot opportunities, and adapt swiftly.
You will use Alteryx to mine actual data for a problem of your interest. The problem could be from your current work, something of interest to the school, or any data-driven problem you wish to solve and can source the data free from the web (e.g. data from UCI Machine Learning Repository - https://archive.ics.uci.edu/ml/index.php, Kaggle.com), etc. There are several types of analytics we’ll discuss in this module, however, for this assessment, you can either focus on Predictive Analytics, Clustering & Segmentation, or Prescriptive Analytics. We will discuss different scenarios of these types of analytics in class.
You will design the data analytics task, mine the data and describe the result. Your analysis of the data and results need not be at par with actual industrial results in your chosen area of interest as the aim of this task is to give you a real hands-on experience for what you have learned in this course.
Assume you have been employed as an analyst by a company that wants to understand the ultra-modern approach for applying and/or using data analytics for the task in question. You need to review what has been done on your problem to date. It will be great if your idea is novel. However, it is more important to have a well-developed idea (within the scope of what’s discussed in class).
The cross-industrial standard process for data mining (CRISP-DM) methodology provides a structured approach and sequence of events for planning a data mining project (this is the fundamental of this course and will be extensively discussed in class). Your research and write-up should be structured using the CRISP-DM process (see the coursework information below). You need to discuss the problem and the problem domain, review related work or existing solutions to the problem if any have been proposed or documented. If for any reason your approach does not linearly progress through the steps, this needs to be reflected in your analysis. You should interact with me as you develop your initial idea up to the write-up stage as a data or business analyst would interact with an organisation during a project. You need to employ your prior experience, skills developed within this course and your imagination to bridge the gap between the materials and data available and what you could potentially find out by interacting with a client organisation.
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