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Browsing by Subject "Data Visualization"

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  • ETL and Dashboards for Student Recruitment & Marketing 
    Zafar, Muhammad Zeeshan (2024)
    In higher education, student recruitment and marketing play a prominent role in the success of educational institutions, maintaining a robust student population and fostering diversity. Institutions compete for the attention of prospective students, and in this data-driven era, a strategic and data-driven approach is required to compete and make informed business decisions. The student recruitment and marketing team of the University of Helsinki possesses various data sources that require storage, transformation, and visualization to get insights from that data. This thesis aims to solve these problems by creating a cloud database using Azure SQL Database, building Extract, Transform, and Load (ETL) pipelines using Azure Data Factory, and developing dashboards in Power BI that allow the student recruitment and marketing team to transform and load their data into a database and visualize the data in Power BI that helps in making better strategic decisions and sharing the dashboards with stakeholders across the institution. The results establish the ability to use Azure services for data management. Results include interactive dashboards in Power BI consisting of various visualizations that meet the requirements of the student recruitment and marketing team by providing Key performance indicators (KPIs). This approach enabled data-driven decision-making for the student recruitment and marketing team.
  • Inference of Bacterial Growth Dynamics via Symbolic Regression 
    Hovhannisyan, Karen (2023)
    Microbial growth dynamics play an important role in virtually any ecosystem. To know the underlying laws of growth would help in understanding how bacteria interact with each other and their environment. In this thesis we try to automate the process of scientific discovery of said dynamics, via symbolic regression. It has historically been implemented with genetic algorithms, and although many of the new implementations have different approaches, we stick with a highly optimized genetic-programming based package. Whatever the approach, the purpose of symbolic regression is to search for a mathematical expression that explains a response variable. We test the highly interpretable machine learning method on several datasets, each generated to mimic certain patterns of growth. Our findings confirm its ability to reverse-engineer theory from data. Even when the generating equations contain the latent nutrient variable, whose dynamics are not observable through the raw data, symbolic regression is able to find an analytically correct reparametrization and exact solution. In this thesis we discuss these results and give an overview of symbolic regression and its applications.

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