Skills

Python: Leveraging expertise in Python to build scalable, efficient solutions for data analysis, machine learning, and web development. Skilled in using popular libraries like Pandas, NumPy, and Scikit-learn to extract insights and solve real-world problems.
R: Adept at statistical computing and crafting visually compelling graphics using R. Proficient in creating reproducible research pipelines and leveraging packages such as ggplot2 and dplyr for advanced data analysis.
SQL: Experienced in managing relational databases, optimising complex queries, and performing advanced data manipulations to support business intelligence and reporting.
Java: Gained foundational knowledge and practical experience during computer science studies, with an understanding of object-oriented principles and algorithm design.
C: Acquired a solid background in low-level programming, memory management, and system development through academic coursework.
C++: Built a foundational understanding of high-performance computing and object-oriented programming paradigms during computer science education.
JavaScript: Familiar with creating interactive web applications and gaining practical knowledge in modern libraries and frameworks through academic projects.

Google Sheets: Proficient in leveraging Google Sheets for collaborative data analysis, creating automated workflows with scripts, and generating shareable reports.
Power BI: Proficient in developing interactive dashboards and custom visualisations that drive data-informed decisions and strategic planning.
Tableau: Demonstrated expertise in transforming raw data into engaging, actionable insights through sophisticated Tableau visualisations.
MS-Excel: Advanced proficiency in Excel for data analysis, including the use of pivot tables, advanced formulas, and VBA for task automation.

Data Visualisation: Skilled in presenting complex datasets in a concise and intuitive manner, empowering stakeholders to make informed decisions.
Statistical Analysis: Leveraging statistical methodologies to extract trends, test hypotheses, and provide actionable recommendations.
Predictive Modelling: Skilled in creating predictive models that accurately forecast outcomes using tools like Python, R, and machine learning techniques.
Data Cleaning: Deep understanding of data preprocessing techniques to ensure accuracy and integrity in analysis.
Automation: Proficient in automating workflows and repetitive tasks, improving efficiency and minimising errors.

Team Collaboration: Proven ability to collaborate across departments, fostering a team-oriented approach to achieve shared objectives.
Problem-Solving: Leveraging analytical acumen to diagnose issues, evaluate options, and implement effective solutions in challenging scenarios.
Data Storytelling: Adept at translating data-driven insights into compelling stories and presentations that resonate with diverse audiences.
Effective Communication: Strong interpersonal and technical communication skills, ensuring alignment between technical and non-technical stakeholders.
Leadership: Experience leading projects to successful completion by motivating teams, setting clear goals, and fostering professional growth.

Markdown: Skilled in writing clean and structured documentation using Markdown syntax to create professional and shareable documents.
Quarto: Skilled in crafting dynamic, interactive documents and reproducible reports tailored to diverse audiences, from technical teams to business stakeholders.
Jupyter: Proficient in creating and managing interactive computational notebooks to document and share workflows, analyses, and visualisations effectively.
Git: Skilled in version control practices, including branching strategies and collaborative workflows, to ensure seamless team integration and project continuity.
Microsoft Word: Proficient in creating polished, professional reports and templates with attention to detail and clarity.