Technology Skills: Excel, Power B, Matlab, scikit-learn, NLP, TensorFlow, Tableau, Jupyter, GitHub
Languages: R, Python, SQL
Lynda is a proven data science professional with over three years experience in data analysis, research, machine learning, and network analysis.
- Master’s Degree in Data Science from University of Padova
- Bachelor of Science in Statistics from University of Nairobi
- Currently a Data Science Research Assistant at DTU (Technical University of Denmark) involved in research, data collection, cleaning, analyzing, archiving, and modeling epidemiological data, and using statistical techniques and machine learning models to analyze trends and forecasts and recommend improvements.
- Previously worked as a Junior Data Analyst at University of Padova – Office of Public Engagement, and as a Data Analyst & Quality Assurance at CapaBuil Ltd
In our Lab, Lynda has been heavily involved in the data analysis portion as well as building models for the attrition problem. She’s put together a number of data reports that have helped uncover key features.
- Performing predictive modelling using XgBoost, Boosted Regression Trees and Random forest in R and Python.
- Using statistical techniques and machine learning models to analyze trends and forecasts and recommend improvements.
- Retrieving Data, performing big data analysis, and visualization using SQL, R, Matlab, Python, tableau dashboards and Gephi.