Minerva S

Imperial College London

As a dedicated and experienced data science educator and mentor, I am committed to empowering students and guiding them towards success in the field of data science. With a strong academic background and extensive industry experience, I bring knowledge and skills to my teaching and mentoring endeavours. I completed my PhD at the University of Cambridge, UK 2017, focusing on developing machine-learning-based geospatial data analysis pipelines. Additionally, I hold an MPhil from the School of Geography and Environment and an MSc from the Department of Engineering at Oxford University. From 2018 to 2022, I served as a Research Fellow at Imperial College London, working on machine learning-based social good projects. Additionally, I am a well-loved machine learning instructor on Udemy with 95,000+ students to my credit (Udemy is the world's largest MOOC platform).

I have developed a strong command of programming languages, including SQL, R, and Python, and I am proficient in utilizing libraries such as NumPy, Pandas, scikit-learn, TensorFlow, and Keras. My expertise extends to visualization tools like Excel, Tableau, and Python packages such as matplotlib, seaborn, and Plotly. These technical skills have enabled me to apply various numerical and categorical modelling techniques, including supervised and unsupervised machine learning methods such as logistic regression, KNN, SVM, decision trees/random forest, clustering, cluster analysis, dimensionality reduction, and neural networks. I have successfully employed these techniques in my research endeavours and while teaching data science to postgraduate students at esteemed institutions like Imperial College London and Oxford University. Through my extensive experience mentoring data science students at Imperial College London, I have witnessed their growth and development, resulting in published research papers and valuable internships and PhD placements. I am dedicated to providing a supportive and engaging learning environment where students can thrive and reach their full potential in data science.