Report format   Eight pages maximum with the information and sections described below. Use 11 or 12 point Times or Arial fonts. Note: not 10 pages as stated in Subject outline.

Submit to   UTS Online assignment submission button.

Please, make sure to call the filename as described above and make sure you put your name and student ID in the report.

In this assignment you need to develop a project proposal that would use data analytics methods to address a problem for a company. Choose one of the areas from the table below. Formulate a specific business problem in that area and make up the specific details of the company yourself.

You will also give a 3-minute pitch for your project proposal. Please submit this as a link to a YouTube video or similar.

This assignment is individual work.

The project proposal is oriented towards a client to provide funding to support such projects in science, business and technology. The funding is issued on a competitive basis, so the aim of your proposal is to convince the client to fund
your project. A good proposal communicates the importance of the problem, makes a strong case that the proposer (i.e. you) knows how to go about solving the problem and leaves the impression that you would be successful if you were given the money.

The project proposal is limited to 8 pages and should include the following:

• Project title

Give a title that describes what the project seeks to do.

• Your name and student ID

Remember to provide these so that I know who to give the marks to!

• Section 1: Aims, objectives and possible outcomes.

Provide a clear statement of the aims and objectives of the data analytics study and the possible outcomes in terms of discovered knowledge and its potential application towards solution of the problem. In this section you need to discuss the business problem.

• Section 2: Background.

In this section you should include the background information to the problem, including the approaches that have been used so far by other researchers. You will need to do some research into how other people have tried to solve the problem. This section should demonstrate to the client that you have a clear picture of what is happening in the field and how similar problems have been approached so far. It is even better if you can point out deficiencies in how others have tried to solve the problem and link that to your proposal. Do not forget to refer to the sources of the information that you have used in your References section.

• Section 3: Data analytics scenario and methodology.

This section should take into account the CRISP-DM methodology. Here you discuss the data analytics problem you have formulated from the business problem. In this section you should:

• formulate the problem as a data mining problem and identify the data analytics tasks;
• formulate the data collection and organisation strategy (what kind of data, how to record it, format(s) in which it is preserved, integration issues, and, if applicable, changes in current data collection and organisation strategies) relevant to the objectives and the possible outcomes of the project;
• briefly discuss some of the data mining method(s) that might be used;
• briefly consider how the results will be evaluated with respect to the project objectives;
• briefly consider how to deploy the results into the business.

Your proposal will benefit if you include examples of data, similar to the one that you plan to collect. Include also examples of the results that the data mining methods produce from these data, illustrating their applicability to
the problem. You may illustrate your proposal with examples of what you can get out of the tools for the type of data that you address – if that is done correctly then it will definitely convince the client that you know what you are talking about.

Assessment

This assignment is assessed as individual work. The assessment criteria are:

• Formulation of the business problem in terms of the specific aims, objectives and potential project outcomes (section 1) — 25%
• The background to the data analytics project in terms of comprehensiveness and understanding (section 2) — 25%;
• Formulation of the data analytics problem and methodology and how well they connect to the aims, objectives and possible outcomes of the project (section 3) — 25%;
• Quality of the 3 minute pitch: was it within time? does it inspire investment? did we understand what you were proposing to do? – 25%