01 May Analytical Report: Learning Outcomes Assessed: K3, S2, A1, V1, V2 : P
Analytical Report: Learning Outcomes Assessed: K3, S2, A1, V1, V2 : Purpose: The purpose of this task is to give students practical experience to work in teams to write a Data Analytical report, which will provide useful insights, patterns and trends from a given dataset. This activity will give students the opportunity to show resourcefulness and creativity by applying Watson Analytics. Enabling them to design useful visualization and predictive solutions for an analytics problem set. Group Presentation: Week 10 (Scheduled Laboratory) Learning Outcomes Assessed: K4, K5, A2, V1, V2 Purpose: The purpose of the oral presentation is to provide an opportunity for students to present the results of Data Analysis and to share this knowledge while practicing their verbal communication skills. Project Details: Your task for this analytical project is to use the Watson Analytics tool to explore, analyze and visualize the given dataset: Solar Cities, power usage. You will need to provide interesting insights, trends and patterns from the data set features. Data set background: The Solar cities project was a project led by the University of Ballarat, (former name of Federation University), which involved the recruitment of households and businesses across the Loddon Mallee and Grampians regions to monitor changes in energy consumption. The project looked at a number of factors that could influence energy consumption. These factors were broken up into sets of features, and measurements were taken for each respective feature. For example, a factor could be related to a dwelling’s construction materials. In which case a feature could be “dwelling construction type” and a measurement would be taken to determine the construction type for each dwelling and stored in the data set. For example “dwelling construction type” could contain the values brick, brick veneer etc… Many of these features are included within the provided Solar Cities data set. The following are sets of features included in the provided data set: • Adoption of solar energy technologies • Geographic characteristics • Physical characteristics of the dwellings, including such things as the dwellings age, size, number of stories, number of lights, insulation etc. The main aim of this project is to understand the drivers of power consumption, and as a large percentage of electrical energy is created by coal fired plants, then conversely the drivers of CO 2 Therefore two main questions that need to be asked as a researcher of the Solar Cities project are: 1. Which combination of features highlight where efficiencies could be made in the reduction in energy consumption? 2. What would you include in a predictive model that would explain the demand on future energy use and CO emissions? 2 i.e. which features and their measurements contribute to either higher or lower energy consumption? (For a predictive model, Watson Analytics does have a rule based engine…) You have been commissioned as a research assistant with the main aim to find the drivers of
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