I’m Jabulente, a dedicated Data Analyst and Research Specialist with a strong foundation in Crop Science. I bring together domain expertise in agriculture with modern data science practices to deliver insights that support sustainable agriculture, food systems improvement, and evidence-based policymaking. My approach involves exploring data patterns, extracting insights, and communicating findings clearly to support real-world solutions.
My journey began with a degree in Crop Production and Management, where I developed a strong foundation in agricultural science and a deep understanding of ecological systems. Over time, my passion for problem-solving sparked a growing curiosity for technology. I immersed myself in learning—spending countless hours watching tutorials, reading books, and experimenting with data—to teach myself programming, data analysis, and machine learning. This pursuit of knowledge allowed me to build a multidisciplinary career that bridges science, research, and technology. Today, I combine my agricultural expertise with advanced analytical skills to drive measurable impact in data-driven agricultural solutions.
- Data Exploration & Visualization: I interpret complex datasets and present them in intuitive visual formats to help teams understand key trends and make informed decisions.
- Research Analysis: I support and execute research by applying statistical methods to assess data accuracy, uncover insights, and produce evidence-backed conclusions.
- Statistical Modeling: I use various modeling techniques to evaluate patterns, assess relationships, and forecast outcomes based on reliable data.
- Data Management & Automation: I streamline repetitive tasks through automation and maintain clean, organized, and accessible data pipelines.
- Geospatial Analysis: I work with spatial data to map and analyze environmental or agricultural variables, supporting geographical insights and planning.
- Languages: Python, R, SQL, JavaScript (basic)
- Libraries: pandas, NumPy, matplotlib, seaborn, scikit-learn, statsmodels, tidyverse
- Data Tools: Power BI, Excel, Google Sheets
- Geospatial: QGIS, GeoPandas, Folium
- Others: Git, LaTeX, Markdown, Jupyter Notebooks
- Agri-tech and smart farming
- Sustainable development and food security
- Climate and environmental analytics
- Soil health and nutrient cycling
- Data-driven decision-making for rural development
I'm always open to new ideas, collaborations, and meaningful discussions in data science, research, or agriculture. Let’s connect:
