This is
Raiyan Rashid

Welcome to my data science portfolio.
See also my personal website and GitHub.

Welcome to the digital home of Raiyan Rashid, a passionate and dedicated Chemistry major, Biology minor at Howard University. My research interests are solving biological problems using tools at the nexus of chemistry, biology, and data science. In addition to STEM, I'm also passionate about science communication. Outside of the classroom, I'm an avid fan of foreign languages, video game design, and nature photography..

In this website, you'll find a collection of meticulously crafted portfolios showcasing my journey, projects, and insights in the world of technology and software development. As an aspiring computational biologist, I am committed to exploring the nexus of computer science and the life sciences, embracing challenges, and contributing to meaningful innovations. Through my portfolios, I invite you to delve into the projects I've undertaken, the skills I've acquired, and the insights I've gleaned along the way. From data analysis and machine learning to web development and beyond, each portfolio is a testament to my curiosity, creativity, and commitment to excellence. Whether you're a fellow enthusiast, a potential collaborator, or simply curious about the fascinating world of technology, I hope you find inspiration and value in exploring my portfolios. Thank you for visiting, and I invite you to embark on this journey with me as we navigate the ever-evolving landscape of computer science and beyond....

Unlocking Insights from the Stack Overflow Annual Developer Survey 2019

Project Description:

The Stack Overflow Annual Developer Survey serves as a treasure trove of information for understanding the dynamics of the developer ecosystem worldwide. With this project, I embarked on a journey to extract meaningful insights from this extensive dataset. The project was structured around several key objectives:

  1. Data Cleaning: I started by meticulously cleaning the dataset, handling missing values, removing duplicates, and ensuring data consistency to prepare a robust foundation for analysis.
  2. Visualization: Utilizing Python libraries such as Matplotlib and Seaborn, I created a series of visualizations to illustrate trends and patterns within the developer community. These visualizations ranged from simple bar charts to complex heatmaps, providing intuitive representations of the data.
  3. Analysis: With clean data and compelling visualizations in hand, I delved into the analysis phase, exploring various aspects such as average developer salaries per country, the prevalence of Python usage, preferred resources for problem-solving, and the correlation between developer salaries and age/gender.
  4. GitHub Repository: As a final step, I documented my code, findings, and visualizations in a GitHub repository, making it accessible to the wider community. Leveraging Git/GitHub commands, I ensured version control and collaborative development throughout the project lifecycle.

Visualizing Sales Trends: BigMart's 2011 Global Retail Analysis

Project Description:

For the purposes of this exercise, I visualized the sales trends for BigMart, a fictional retail company, which is based in Europe and has operations across multiple countries around the globe. I worked with BigMart's Invoice-wise and Stock-Keeping Unit (SKU)-wise sales data for the year 2011. In this simulated scenario, I was a data analyst in BigMart's Information Technology (IT) team, and my goal was to prepare meaningful charts to showcase the various sales trends for 2011 to the company's top management. I displayed data for key issues pictorially to capture the top management's attention and bring these issues to their consideration. I accomplished this in several steps:

  1. Data Cleaning: I started by meticulously cleaning the dataset, handling missing values, removing duplicates, and ensuring data consistency to prepare a robust foundation for analysis.
  2. Visualization: Utilizing Python libraries such as Matplotlib and Seaborn, I created a series of visualizations to illustrate trends and patterns within the sales data. These visualizations ranged from simple line graphs to complex bar charts and pie charts, providing intuitive representations of the data.
  3. Analysis: With clean data and compelling visualizations in hand, I delved into the analysis phase, exploring various aspects such as the top ten items sold in particular countries such as the United States and the United Kingdom, the highest- and lowest-performing months in terms of sales, and which countries contributed the most to BigMart's sales.
  4. GitHub Repository: As a final step, I documented my code, findings, and visualizations in a GitHub repository, making it accessible to the wider community. Leveraging Git/GitHub commands, I ensured version control and collaborative development throughout the project lifecycle.