Kimia E Semester II

Kimia develops data analytics solutions using Python, SQL, R, Tableau, Excel, and Power BI, with a focus on data cleaning, visualization, and advanced analytics implementations. Her technical portfolio features customer segmentation projects utilizing RFM analysis and K-means clustering methodologies. Her machine learning work emphasizes logistic regression applications for predictive modeling. As a content creator in the chemistry education space, Kimia produces instructional materials covering core chemical principles and laws. Her educational content explores fundamental concepts including Lavoisier's Law of Conservation of Mass, Proust's Law of Definite Proportions, and Dalton's Law of Multiple Proportions. This work bridges the gap between technical data analysis and chemistry education. Her dual expertise spans both commercial data science applications and chemistry education initiatives. The intersection of these domains enables her to serve both enterprise analytics needs and educational resource development. Her work demonstrates the practical application of quantitative methods across multiple technical disciplines.