The purpose of this analysis is to examine candidates’ academic performance in order to identify patterns, compare results across faculties, and highlight the factors that could influence competitiveness in ASE master’s programs.

Chess Tournament Data Analysis Project This project converts unstructured chess tournament data from a fixed-width, pipe-delimited text file containing 64 players into a clean, analyzable CSV dataset through systematic parsing and data extraction. The raw data presents multiple challenges including player information spanning two lines, inconsistent spacing, embedded separators, and mixed alphanumeric content requiring careful line-by-line processing using R's string manipulation functions and regular expressions. Key extracted fields include player identification (name, state, USCF ID), rating information (pre- and post-tournament ratings), tournament performance (total points, round-by-round results, opponent numbers, colors played), and calculated metrics such as average opponent rating for strength-of-schedule analysis. The technical approach employs R programming with core libraries including `stringr` for text processing, `dplyr` for data manipulation, and `readr` for file I/O operations, implementing robust data cleaning to remove separator lines and headers, type conversion from text to numeric values, cross-referencing to match opponent numbers, and quality validation to check for missing values and data consistency. Project deliverables include a structured CSV file with complete player and tournament data, a comprehensive HTML report documenting the conversion process using R Markdown for reproducible analysis, data quality assessments highlighting limitations, and summary statistics with interactive data tables using the DT package, ultimately transforming complex human-readable tournament records into machine-readable format suitable for statistical analysis, database storage, or integration with tournament management systems.

Analysis of World Bank data on global poverty rates and regional differences from 1980 to 2020.

Intro to Biostatistics (BIOL 327) September 21, 2025 Fall 2025 William & Mary

Materi praktikum Pemrograman Statistika yang membahas konsep randomness, distribusi peluang, serta simulasi distribusi menggunakan R. Topik mencakup fungsi distribusi bawaan (d-, p-, q-, r-), metode inverse transform, acceptance–rejection, direct transformation, hingga distribusi normal multivariat. Disertai penjelasan teori, kode R, dan visualisasi dengan ggplot2.

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