Simplified menu-driven data analysis tool with macro-like automation - Kazembe, Luntha (Rhodes University, Faculty of Science, Department of Computer Science)
This study seeks to improve the data analysis process for individuals and small businesses with limited resources by developing a simplified data analysis software tool that allows users to carry out data analysis effectively and efficiently. Design considerations were identified to address limitations common in such environments, these included making the tool easy-to-use, requiring only a basic understanding of the data analysis process, designing the tool in manner that minimises computing resource requirements and user interaction and implementing it using Python which is open-source, effective and efficient in processing data. We develop a prototype simplified data analysis tool as a proof-of-concept. The tool has two components, namely, core elements which provide functionality for the data anal- ysis process including data collection, transformations, analysis and visualizations, and automation and performance enhancements to improve the data analysis process. The automation enhancements consist of the record and playback macro feature while the performance enhancements include multiprocessing and multi-threading abilities. The data analysis software was developed to analyse various alpha-numeric data formats by using a variety of statistical and mathematical techniques. The record and playback macro feature enhances the data analysis process by saving users time and computing resources when analysing large volumes of data or carrying out repetitive data analysis tasks. The feature has two components namely, the record component that is used to record data analysis steps and the playback component used to execute recorded steps. The simplified data analysis tool has parallelization designed and implemented which allows users to carry out two or more analysis tasks at a time, this improves productivity as users can do other tasks while the tool is processing data using recorded steps in the background. The tool was created and subsequently tested using common analysis scenarios applied to network data, log data and stock data. Results show that decision-making requirements such as accurate information, can be satisfied using this analysis tool. Based on the functionality implemented, similar analysis functionality to that provided by Microsoft Excel is available, but in a simplified manner. Moreover, a more sophisticated macro functionality is provided for the execution of repetitive tasks using the recording feature. Overall, the study found that the simplified data analysis tool is functional, usable, scalable, efficient and can carry out multiple analysis tasks simultaneously.
Keywords: Data analysis; Macro instructions (Electronic computers); Quantitative research -- Software; Python (Computer program language); Scripting languages (Computer science)