Brian Kipsang | Data Scientist

Technical Skills

Data Analytics

Expert in data analysis, machine learning algorithms, and statistical modeling. Proficient in deriving meaningful insights from complex datasets using Python, R, and SQL. Specialized in predictive analytics, data visualization, and creating interactive dashboards with Power BI and Tableau.

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Geospatial Analytics & GIS

Advanced expertise in Geographic Information Systems (GIS) and spatial data analysis. Skilled in creating interactive maps, spatial modeling, and location-based insights for urban planning, environmental science, and logistics optimization using QGIS, ArcGIS, and Python geospatial libraries.

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Machine Learning & AI

Comprehensive knowledge of machine learning algorithms, deep learning, and artificial intelligence. Experience in building and deploying predictive models, neural networks, and AI solutions. Proficient in TensorFlow, PyTorch, Scikit-learn, and MLOps practices for production-ready ML systems.

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Certifications

BSc. Geospatial Information Science and Remote Sensing

Dedan Kimathi University of Technology (2024)

Machine Learning

Stanford Online – Andrew Ng (2023)

IBM Data Science Professional Certificate

Coursera (2023)

My Work

Building Security Analytics System

Advanced security analytics platform combining IoT sensor data with machine learning algorithms to predict and prevent security threats. Features real-time monitoring, anomaly detection, and automated alert systems using Python, TensorFlow, and real-time data processing frameworks.

Hospitality Data Analytics Platform

Comprehensive data analytics solution for hospitality industry with predictive pricing models, occupancy forecasting, and customer behavior analysis. Built with Python Flask, featuring interactive dashboards, MySQL integration, and machine learning-powered recommendations.

Data Processing Shell Environment

Custom command-line interface optimized for data science workflows. Features automated data pipeline execution, batch processing capabilities, and integration with popular data science tools. Supports complex data transformations and multi-threaded processing operations.

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Hackathons

Amini Uwazi Citizen Science Challenge

Ranked 7^th in a pan-African competition collecting agricultural and forestry data for AI-driven sustainability tools. Contributed datasets on crops and forests across Africa to build predictive models and support food security initiatives.

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Côte d’Ivoire Byte-Sized Agriculture Challenge

develop a pixel-level classification model that can distinguish between cocoa, rubber, and oil palm plantations using satellite imagery, using open-source Sentinel-2 data from the Copernicus program. (Ongoing)