This project consists of using the New York Times Article Search API to examine how the volume and framing of soccer coverage in the New York Times has evolved since the modern Major League Soccer (MLS) expansion era began in 2005.In fact d,API provides rich article-level metadata such as headline, publication date, section name, news desk, word count, and multimedia flags since 1851, making it well suited for detecting decade-long window in editorial attention and story framing by querying the keyword “soccer”

This assignment demonstrates how to manually create structured data files in HTML and JSON formats, then load them into R data frames, and rigorously compare whether both sources yield identical data. In order to implement it, i chose to investigate the evolution of striker performance metrics in the Premier League since 2020 through a comparative analysis of 3 selected sports analytics literature.

This assignment demonstrates how to manually create structured data files in HTML and JSON formats, then load them into R data frames, and rigorously compare whether both sources yield identical data.In order to implement it, i chose to investigate the evolution of striker performance metrics in the Premier League since 2020 through a comparative analysis of 3 selected sports analytics literature. By integrating data from both formats, my goal is to determine how modern finishing efficiency influences a club’s overall league standing and point acquisition.

This assignment aims to analyze the attacking performance of five elite Premier League players: Haaland, Salah, Palmer, Son, and Saka using the statistics covering goals, assists, and shots split by home and away fixtures at a given period of the season. We seek to identify meaningful patterns in finishing efficiency and goal contribution across different venues (Home/Away).

This project consists of analyzing the 2026 US Top 50 Universities dataset to evaluate the relationship between institutional characteristics, specifically focusing on research impact and employment outcomes. By analyzing research impact and post-graduation employment rates, we aim to identify performance trends across public and private institutions for the 2026 academic outlook.

This project consists of analyzing the 2026 US Top 50 Universities dataset to evaluate the relationship between institutional characteristics, specifically focusing on research impact and employment outcomes.By analyzing research impact and post-graduation employment rates, we aim to identify performance trends across public and private institutions for the 2026 academic outlook.

This assignment aims to analyze the attacking performance of five elite Premier League players: Haaland, Salah, Palmer, Son, and Saka using the statistics covering goals, assists, and shots split by home and away fixtures at a given period of the season. We seek to identify meaningful patterns in finishing efficiency and goal contribution across different venues (Home/Away).

This assignment analyses the Sleep Health & Lifestyle Dataset to understand how lifestyle factors such as occupational role and psychological stress could shape two core sleep outcomes: sleep duration and self-rated sleep quality. Understanding these relationships is relevant for us as individual and corporations when creating workplace wellness programs to improve employee's performance

The goal of this assignment is to clean and transform a "wide" dataset representing arrival delays for ALASKA and AM WEST airlines across five different cities into a "long” format suitable for comparative analysis. I will be using functions in the Tidyverse R-package to accomplish this goal.

The goal is to compare the chess player’s actual score with their expected score calculated using the ELO rating system’s probability formula. This analysis will then facilitate to classify players who most over performed and those who most underperformed of course relative to their expected score.

In this project, we will manipulate a structured chess tournament file that contains the result of 64 players. Our goal is to extract some meaningful players data including player's name, state, total tournament points, pre-tournament rating, the average pre-tournament rating of all opponents they faced and then export into a clean, relational CSV file.