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Entry-level Data Science Undergraduate with strong analytical skills and programming proficiency
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Assessing your cultural and operational fit
Analytically driven third year Data Science undergraduate with a strong foundation in programming and the statistical data lifecycle. Proficient in executing data cleaning, exploratory analysis, and predictive workflows using Python and R, complemented by practical knowledge of Object-Oriented Programming (OOP) principles in C#. Adept at processing multi-source datasets and translating statistical evaluations into structured, data-driven insights.
University of Plymouth
BSc (Honours) · Data Science
August 1, 2025 – June 30, 2027
Unknown
G.C.E. A/L Examination · Physical science stream
June 1, 2024 – May 31, 2024
Applying Statistical Techniques and Models to Support Data-Driven Decisions
June 19, 2026 – Present
Utilized statistical methods including hypothesis testing, regression analysis, and time series forecasting on real-world datasets. Applied techniques such as Chi-Square testing, Mann-Whitney U testing, ARIMA, and SARIMA models for analytical evaluation. Generated visual representations including residual plots, scatter plots, ACF/PACF charts, and forecasting graphs to support data interpretation. Assessed model effectiveness and interpreted analytical outcomes to derive meaningful data-driven insights.
Developing Predictive Models and Analytical Frameworks to Optimize Valuation Strategies | Data Programming in R (PUSL 2076)
June 19, 2026 – Present
Cleaned and preprocessed market datasets using R programming to ensure data integrity. Applied exploratory analysis, regression modeling, and clustering techniques to identify key variables. Generated distribution profiles, correlation matrices, and diagnostic charts to guide feature selection. Evaluated model accuracy and predictive metrics to interpret structured, data-driven insights.
Cultural Fit Analysis
The candidate is an undergraduate with academic projects, indicating a foundational interest in Data Science. Involvement in extracurricular activities like IEEE Student branch and AIESEC suggests an inclination towards collaboration and community engagement, which could contribute positively to cultural fit. However, the lack of professional experience or diverse project types beyond academic settings limits the assessment of broader cultural adaptability and real-world problem-solving approaches.
Soft Skills & Operational Fit
The candidate's resume highlights critical thinking, teamwork, technical communication, and logical problem-solving as soft skills. These are essential for a Data Science role, indicating a potential for good operational fit within a data-driven team. However, without practical work experience, the application of these skills in a professional setting is unproven.