Design and Implementation of an Integrated Toolkit for Academic Data Analysis

• Type: Bachelor thesis
• State: open
• Supervisor: Benno Bielmeier

Abstract

In modern research, carefully examining complex data is crucial for gaining scholarly insights. This bachelor's thesis undertakes the task of conceptualising and constructing a comprehensive toolkit and pipeline tailored to the demands of academic data analysis, with a primary focus on data acquired about runtime behaviour and temporal events. This toolkit includes a well-organised system for storing data, a versatile collection of scripts and tools for data transformation, robust stochastic testing, and expressive data visualisations utilising R and ggplot2. The main goal of this project is to streamline the data analysis journey from raw data assimilation to the production of publication-ready assets.

Your Tasks

• Engage in an iterative requirement gathering process with researchers from the laboratory.
• Collect and categorise both internal lab solutions and public available ones.
• Devise a systematic methodology for organising data storage and archival.
• Engineer a resilient and adaptable toolkit capable of gathering, navigating, transform, pre-process, and visualise data.
• Construct a style guide to ensure uniformity in asset generation.
• Compile an exhaustive compendium of best practices to optimise the utilisation of the toolkit.

Depending on the type (i.e., Bachelor's thesis, Master's thesis or HSP), the scope of tasks is adjusted according to the credit points.

Your Goal

• Equip researchers with a dynamic toolkit that accelerates data analysis.
• Provide a systematic scaffold for data storage and an adaptable suite of analytical tools.
• Expedite the transformation of raw data into discernible insights while upholding precision and reproducibility.
• Establish a style guide and repository of best practices to augment research output quality.
• Facilitate seamless incorporation of toolkit-generated assets into scholarly publications

Your Profile

• Solid foundation in software engineering.
• Programming experiences in languages like R, Python, or analogous data analysis-centric languages.
• Prior exposure to research methodologies, including statistical tests and production of publication-grade visuals (optional).

We offer

• Hands-on expertise in data analysis with industry-proven tools and frameworks.
• Level-up your Problem-Solving by developing solutions for real-world research challenges.
• Learn rigorous analysis techniques for impactful research.
• Gain exposure across data domains and practices.
• Boost your own visibility via public contributions to Open-Source projects
• Be part of the team of the Laboratory for Digitalisation
• Work from home or at the laboratory at the informatics building of OTH Regensburg
• The possibility of further work in our team (Bachelor/Master Theses, HSPs, Research Master)
• The opportunity for employment at the Laboratory for Digitalisation
• Free coffee and tea

If you are interested or have further questions, just write an email to benno.bielmeier at oth-regensburg.de