Projects

A collection of data analytics projects completed during my Master of Science in Data Analytics at Western Governors University (Oct 2022 – Oct 2023), plus ongoing work at the intersection of clinical laboratory science and data.

MS Data Analytics — WGU Projects

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D206: Data Cleaning

Tools: Python, Pandas
Real-world datasets are messy. This project focused on identifying and handling missing values, outliers, and inconsistencies to prepare data for downstream analysis. Built a repeatable cleaning pipeline that documented every transformation decision.
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D207: Exploratory Data Analysis

Tools: Python, Pandas, Matplotlib, Seaborn
Explored a healthcare dataset to uncover patterns, distributions, and relationships between variables before any modeling. Used statistical summaries and visualizations to generate hypotheses and guide further analysis.
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D208: Predictive Modeling

Tools: Python, Scikit-learn
Built and evaluated regression and classification models to predict outcomes from structured data. Covered feature selection, model tuning, cross-validation, and performance metrics including ROC-AUC and confusion matrices.
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D209: Data Mining I

Tools: Python, Scikit-learn
Applied unsupervised and supervised machine learning techniques including k-means clustering and decision trees to identify hidden structure in data. Focused on pattern recognition and actionable insight extraction.
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D210: Representation & Reporting

Tools: Tableau
Designed interactive dashboards and data visualizations to communicate analytical findings to non-technical audiences. Emphasized storytelling with data — translating complex results into clear, decision-ready visuals.
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D211: Advanced Data Acquisition

Tools: SQL, PostgreSQL
Wrote complex SQL queries to extract, join, and aggregate data from relational databases. Covered subqueries, CTEs, window functions, and database design principles for efficient data retrieval at scale.
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D212: Data Mining II

Tools: Python, TensorFlow, Scikit-learn
Advanced machine learning methods including neural networks, ensemble methods, and dimensionality reduction. Evaluated model performance and interpretability trade-offs on complex datasets.
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D213: Advanced Data Analysis

Tools: Python, Statsmodels
Applied time series analysis and NLP techniques to structured and unstructured data. Explored ARIMA modeling, sentiment analysis, and text mining to extract insights from temporal and language-based datasets.
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D214: MS Data Analytics Capstone

Tools: Python, SQL, Tableau
Culminating project integrating skills across the entire program. Defined a research question, acquired and cleaned data, performed analysis, built visualizations, and delivered a full written report and presentation.
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Bioinformatics — Python Projects

A collection of Python scripts developed during early bioinformatics coursework, focused on DNA sequence analysis algorithms built from scratch — emphasizing an understanding of the underlying biology and computation.

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DNA Sequence Analysis Toolkit

Tools: Python, BioPython
A suite of command-line tools for analyzing DNA sequences, including nucleotide counting, k-mer frequency analysis, and pattern matching. Scripts accept raw sequence input and return counts, positions, or frequency arrays — replicating core operations used in genome analysis pipelines.
Scripts: side_project.py, seq.py, frequent_words.py, array_frequency.py, pattern.py, position.py

Approximate Pattern Matching with Hamming Distance

Tools: Python, BioPython
Implements approximate string matching on DNA sequences using Hamming distance — identifying where a target pattern appears in a genome even when allowing for a set number of mismatches. Directly applicable to mutation detection and primer design in genomics workflows.
Scripts: Approx_Pat_Match.py, hamming_dist.py, test_code.py

k-mer Neighborhood & Mismatch Analysis

Tools: Python
Generates all k-mer neighbors within a given Hamming distance and identifies the most frequent k-mers accounting for mismatches and reverse complements. Useful for identifying transcription factor binding sites and replication origin candidates in genomic sequences.
Scripts: d_neighbor.py, fr_words_mis.py, fr_mis_rev.py

Genome Skew & Replication Origin Analysis

Tools: Python
Computes the GC skew across a genome and identifies minimum skew positions — a technique used to predict the origin of replication (oriC) in bacterial genomes. Also includes clump finding to locate regions of high k-mer density.
Scripts: min_skew.py, clump.py

RNA-seq Differential Expression Data Processing

Tools: Python, NumPy, JSON
Parses and cross-references DESeq2 differential expression output with sequence annotation files, merging datasets by gene identifier and filtering out NA log-fold change values. Outputs a clean structured table ready for downstream visualization or statistical analysis — bridging raw pipeline output and interpretable results.
Scripts: converttodict.py, table2.py

GC Content Analysis from FASTA Files

Tools: Python, BioPython
Reads multi-sequence FASTA files, computes GC content for each record, and identifies the sequence with the highest GC percentage — a standard quality metric in genomics and a foundational Rosalind bioinformatics problem.
Scripts: transcribe.py