ANLY482 AY2016-17 T2 Group12 : Project Overview / Methodology

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Description

Methodology

Data

The dataset provided by KST Bikers is a Feedback System which consists of feedback lodged by:

• SMS
• Email
• Feedback Form

Tools Used

• Microsoft Excel 2016
• JMP Pro 13
• Tableau 10.0

Methodology

Our team will first understand what KST Bikers is all about through their website, annual reports, social media platforms and by asking our sponsor. Secondly, we will identify potential additional data sources that will help with our analysis. Lastly, we will research to find out what are some techniques or ideas on how to analyse feedback data. The following are some research that we have done and our key findings of each article:

S/N	Title of Article	Summary of Key Findings
1	Top tips on how to analyse feedback	Having a comprehension of how to use present and future state process mapping and the advantages of using data boxes, plus a visual workflow diagram are going to be essential in the most of the cases and will increase value to your data analysis. This provides a clear visual help in seeing where the bottlenecks are in your processing and areas where you have to made the improvements. Other methods include cause and effect diagrams, like the fishbone technique with the 5 whys, which enable you to identify your root causes and will introduce you to your path of resolving your key critical areas. Data analysis in the form of a chart will bring up some important areas for discussion, revisit and future strategy.
2	What is EDA?	Exploratory data analysis (EDA) is not just a collection of techniques. It is a philosophy as to how we breakdown a data set; what to look out for; how we look; and how to interpret. Most EDA techniques are graphical with little quantitative techniques. There is heavy reliance on graphics as the main role of EDA is to open-mindedly explore.
3	Why You’re Not Getting Value from Your Data Science	Business users keep coming up with problems and data analysts cannot keep up as they take much time build sophisticated data models. The most common problem is that data scientists often do not build their work around the final objective which is to derive business value. The following are the best practices: Stick with simple models Explore more business problems: Instead of exploring one business problem with a sophisticated business models. Build a simple model for each problem and assess the value proposition Learn from a sample of data – not all the data Focus on automation: Use algorithms and develop software systems to automate data processing techniques	}