Hey, I’m Zola.
I am deeply passionate about transforming data into valuable insights, fueling data-driven decisions that drive real business impact.
My Story
I’m a never-ending explorer, always seeking growth and new challenges. As a mom, wife and a good friend, I value balance, empathy, and perseverance in all aspects of life.
During my time in marketing, I always aimed to base my strategies on data analysis, market research, and surveys to ensure marketing effectiveness. Since marketing costs are so high it was often challenging to justify my plans to management without solid back up. That’s how I started and fell in love with data analysis—the joy of discovering patterns in numbers was incredibly fulfilling.
This passion led me to pursue a Master of Business Analytics at Macquarie University. Now, as I deepen my understanding, I’m realizing how data analytics and information systems not only drive smarter decisions but also positively impact both businesses and society as a whole.
Database
design & query
Designed and implemented a relational database for Pro App by identifying key entities and their relationships, creating an ER diagram, and writing SQL queries to address business questions, generating insights to improve operations and customer satisfaction.
My projects
Big data
I successfully completed a big data project where I applied efficient data extraction and processing techniques using MongoDB, pymongo, and MapReduce. By extracting only the necessary data from a large dataset of song documents, I was able to significantly reduce computation time and costs.
Sales Data Analysis & Predictive Modeling
This project involved comprehensive sales data analysis for Dibs, an organization looking to optimize their business decisions based on historical sales data. Using R programming language, the goal was to clean and analyze the data, identify key trends, and build predictive models to forecast future sales.
Bank client behavior prediction
The goal of this analysis is to compare the performance of two machine learning models, Logistic Regression and Random Forest, in predicting whether a client will have overdue payments. By using features from client application and credit records, the analysis aims to assess which model can better predict a client's likelihood of having overdue payments, based on accuracy metrics.
Customer segmentation
Analyzes a dataset of 2,000 customers using Python for data processing, exploratory data analysis, and visualization, applying machine learning techniques such as K-Means++ and Agglomerative Clustering to segment customers based on demographic and behavioral attributes, uncovering patterns that inform targeted marketing strategies tailored to each group.