Hello, 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 lifelong explorer who is driven by curiosity, growth, and a desire to take on new challenges. I value balance, empathy, perseverance and strive to reflect these values in both my personal and professional life.
My journey began in marketing, where I quickly learned that creativity alone wasn’t enough. In a world of tight budgets and high stakes, I relied heavily on data through market research, surveys, and analysis to craft strategies that could stand up to scrutiny. The process of uncovering insights and making sense of patterns in numbers sparked something in me. I found joy in data analysis, as it gave meaning to the decisions I made and allowed me to tell compelling, evidence-based stories.
That joy evolved into a deeper passion and led me to pursue a Master of Business Analytics at Macquarie University. As I continue to expand my knowledge, I see more clearly how data and information systems empower better decision-making, create business value, and contribute to broader impact.
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.