Data Science Portfolio
Welcome to my Data Science Portfolio! Within this page, you'll find a compilation of projects that embody my journey as an aspiring entry-level Data Analyst. Each project serves as a testament to my growing proficiency in data science, machine learning, and statistical analysis.
System Analyst
In this project, my team and I leveraged IBM RSA Architect software to conduct a thorough analysis of Uber's software requirements and subsequently created comprehensive and precise models to design their system architecture. The use of this advanced tool allowed for meticulous exploration of the business needs, resulting in a well-structured and efficient system design that aligns perfectly with the organization's objectives.
Data Analyst
As the Data Analyst for the project, i played a pivotal role in successfully developing and deploying the Greenspace app to foster collaboration among students at Georgia State University. I contributed to the project by developing comprehensive business requirements, ensuring alignment with the organization's objectives and needs. Leveraging my expertise in data analysis tools such as RStudio and Tableau, I meticulously analyzed and interpreted diverse statistics and data sets, providing valuable insights that informed the crucial decision-making processes. Throughout the project, I honed a range of digital, professional, collaborative, management, and leadership skills, enhancing my overall effectiveness in this dynamic work environment.
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Personal Project
For this project I built a comprehensive HR dashboard. This project involved importing data and creating an employee demographics page to provide an overview of the organization's demographic outlook. Additionally, I utilized pie charts and doughnut charts to visually represent gender and racial diversity. To enhance the interactivity and usefulness of the dashboard, I incorporated buttons, themes, slicers, and filters. By the end of this project, I gained the confidence to create organized HR dashboards suitable for personal or organizational purposes.
Personal Project
This project involves analyzing and interpreting the extensive dataset generated by users on the Spotify platform. By leveraging this data, the project seeks to uncover valuable insights into user preferences, music consumption patterns, and trends. Through advanced data analytics and machine learning techniques, the project will identify correlations between listening habits, genres, and demographics, contributing to a deeper understanding of user behavior. The ultimate goal is to enhance personalized recommendations, refine content curation, and improve the overall user experience within the Spotify ecosystem.
Business Analyst
This project allowed me to spearhead a comprehensive profit and operations analysis for a prominent business, skillfully employing advanced Microsoft Excel functions to construct meticulous financial cost analysis forecasts leveraging available data. This in-depth analysis proved instrumental in identifying lucrative opportunities for enhanced profitability, empowering strategic decision-making that laid a solid foundation for future success.
Personal Project
Throughout this project, I have honed my skills in utilizing essential Python libraries like Pandas, NumPy, Matplotlib, and Seaborn to conduct comprehensive univariate and bivariate analyses, as well as correlation analysis. This project also allowed me to effectively identifying and managing duplicate and missing data, ensuring data integrity. Demonstrating my practical knowledge, I have applied these techniques to this tabular dataset, crafting insightful data visualizations with Seaborn and Matplotlib.
Certification Project
This capstone aimed to predict Falcon 9 first-stage landing success, crucial for evaluating SpaceX mission cost-effectiveness. Data collection used RESTful API and web scraping, transformed into a dataframe with data wrangling. A dynamic Plotly Dash dashboard and Folium map provided interactive launch record analysis and site proximity examination. Machine learning (SVM, Classification Trees, Logistic Regression) on split data identified the best method for predicting success. The project concluded by consolidating all components into comprehensive data-driven insights, offering valuable information for companies competing with SpaceX in rocket launch bids.
