Assignment Brief
Introduction
This is an individual assignment. Please read this assignment brief carefully in order to do the task. You need to read a case study ‘Happy Cow Ice Cream: Data-Driven Sales Forecasting’ and analyse and visualise two datasets for your report. The assignment is to be carried individually and carries 30% of the unit marks.
Task
Sarah has obtained two datasets from the Happy Cow store located at the University of Hong Kong from the IT support team.
Clean up the dataset and format it into the time series structure. Using Tableau (preferred) – explore and visualise the dataset by answering the following questions:
1. Read the accompanying case study and explore the provided datasets.
2. Explore and visualise the sales performance of the three consumer groups (students, staff and tourists).
3. Both Mary and Prem, the senior sales assistant, believe that different groups of flavours sell better at different times of the year. Do the data back this up? Please propose your groupings and visualise them to generate insights into the ice cream sales. Regarding flavour groups, does grouping give a better level of analysis than individual flavours?
4. What outliers can be identified from the daily sales of Happy Cow? Please define the outliers and explain how to address them.
5. Given the characteristics of the Happy Cow dataset, what are the purposes of the time series analysis (ie predictive versus descriptive)?
6. Submit a written report (no more than 1000 words) providing visuals and accompanying explanation in support of each question.
1. Case Study provided of ‘Happy Cow Ice Cream: Data-Driven Sales Forecasting’.
2. Dataset 1 (DailySales.xls): Daily sales of several ice cream flavours over five months across three types of consumers (students, staff and others / tourists).
• Dataset 2 (HourlySales.xls): hourly sales over seven months (April to October).
This assignment tests whether you have achieved the learning outcomes of the module:
PC 5 Apply systems theory principles in order to differentiate between data and information for decision making and operationalise data / information distinctions in sample case studies.
PC 6 Acquire, analyse and visualise data in order to reflect on its decision making potential, the information generated, its relevance and validity, and its synthetic potential in new situations.
PC 7 Critically evaluate different information visualisation approaches and apply them using software (eg Tableau or other) by using sample datasets provided, in order to interpret business decision potential. Learn how to communicate with information visualisation experts and design pathways to information for decision making.
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