Academic Projects and Skill Applications
Through the following academic projects, I have gained extensive experience in utilizing data analysis to solve complex business challenges. These projects have helped me develop a versatile and applicable understanding of how data analysis can be applied in practical, real-world scenarios.
Market Intelligence: Customer Profiling
Conducted a logistic regression analysis to identify the top 1,000 customers for a new platform. Analyzed the firmographic characteristics of the identified customers by interpreting the model coefficients. Created a correlation matrix to assess multicollinearity. Offered justification for the significance or lack of significance of certain coefficients, supported by evidence.
Structured Data Modeling: SQL Database
Identified a real-world situation that required a database system. Developed a solution using an entity-relation diagram (ERD) and performed a mapping of the ERD's relational schema. Created a SQL database from scratch using the ERD and schema. Anticipated future business questions and created helpful queries to provide answers. Used Tableau to create comprehensive visualizations.
Fundamentals of Data Analytics: Cleaning Messy Datasets
In cleaning a large dataset using Python, the Pandas library is utilized first to efficiently handle and preprocess the data, applying functions to filter, sort, and remove duplicates. NumPy was then employed for more complex numerical operations, dealing with missing values, and normalizing the data for consistency Python's data visualization tools like Matplotlib and Seaborn were leveraged to identify outliers and trends, ensuring the data's integrity and readiness for analysis.
Advanced Analytics: Life Expectancy Models & Visualizations
Applied regression and classification methods to study life expectancy in major US cities. Utilized linear and logistic regression, decision trees, random forests, and KNN to identify correlations between cities.
