Data Analyst | Machine Learning | Generative AI
Data professional with a background in machine learning and academia, focused on transforming complex datasets into meaningful insights through Analytics, Natural Language Processing, Predictive Modeling, and Generative AI workflows.
I'm Nickmoon Mware, a data and AI-focused technologist with a background in computer science, analytics, and applied research. I earned my B.S. in Computer Science from Benedict College and my master's in Computer Science from the University of Nebraska-Lincoln. I am currently pursuing an M.S. in Information Systems & Business Analytics at Park University.
My graduate research centered on natural language processing and voice user interfaces, with a particular interest in how accents affect speech-driven systems and user experience.
Today, I'm focused on machine learning, generative AI, and data analytics, using data to uncover patterns, build useful models, and communicate findings clearly for both technical and non-technical audiences.
Selected work across NLP research, analytics, machine learning, and generative AI workflows, with an emphasis on translating technical work into clear insights.
Built machine learning models to predict average monthly spending and eBook subscription likelihood from a customer dataset with 16,519 records and 25 variables.
Conversational AI focused on educational support. I leveraged my experience as an adjunct instructor and the IEEE educational chatbot reference to design intelligent learning assistants that bridge the gap between pedadogy and technology.
Graduate NLP and speech research on automatic speech recognition, focused on translating spoken audio to text while improving inclusivity for speakers with different accents.
Problem
ASR systems often struggle with speaker variability, producing higher transcription error rates for different accents and speaking styles.
Methods
Processed WAV audio from Wikimedia Commons, generated mel spectrograms, trained a CNN-based speech workflow with Python libraries such as Librosa, NumPy, and PyTorch, and evaluated performance using WER.
Findings
The trained model improved transcription accuracy from roughly 27% to about 77%, while the paper outlines future work on accent robustness, MFCC features, and broader speech datasets.
Built Python and SQL analysis workflows on education and healthcare data, including exploratory analysis of a healthcare readmission dataset with 12,980 observations and 28 variables.
Developed regression, classification, and time-series models for school performance, customer spend, patient readmission, subscription adoption, and 30-day incident forecasting.
Designed dashboards to visualize trends, predictions, comparisons, and KPI-style summaries so non-technical audiences could interpret model outputs quickly.
IBM
Covers practical applications of generative AI in data science workflows, including analysis support, experimentation, and model-assisted productivity.
Dallas Data Science Academy
Focuses on integrating generative AI into the full data science lifecycle, from problem framing and analysis to modeling and communication.
Coursera
Supports business-facing communication, requirements thinking, and the ability to connect analytical outputs to decision-making contexts.
LinkedIn Learning
Reinforces practical SQL querying skills used across exploration, reporting, and data validation workflows.
A place for analysis decks, downloadable reports, and future dashboard embeds that showcase how data stories are communicated.
Presentation-ready PDF summarizing customer segmentation, subscription prediction, monthly spending forecasts, and model-selection decisions.
Exploratory analysis of a healthcare readmission dataset covering 12,980 records, variable distributions, key clinical and cost features, and next-step modeling considerations.
External IEEE reference connected to chatbot-assisted learning and educational support, useful for framing future conversational AI portfolio work.
Central hub for interactive dashboards and public visual analytics work across portfolio projects and data storytelling examples.
Research paper and artifact covering the ASR workflow, model design, word error rate framing, and findings from the speech-to-text study.
Generative AI
A future-facing note on how conversational AI can support student learning, course assistance, and engagement when paired with strong UX and domain grounding.
Read moreNLP Research
A reflection on dataset limits, WER evaluation, spectrogram-based training, and how speech research connects to fairness and usability in voice interfaces.
Read moreMachine Learning
A practical look at how feature engineering, model selection, and business framing turn customer data into usable decisions for targeting and planning.
Read moreAnalytics
A quick-study view of how profiling distributions, imbalance, costs, visits, and chronic-condition signals helps prepare healthcare data for downstream modeling.
Read moreTools and methods I use across analytics, machine learning, NLP, visualization, and production-ready data workflows.
Benedict College, Columbia, SC
Completed a computer science degree with a finance minor, building a foundation in programming, problem-solving, and applied technical communication.
2016 - 2020University of Nebraska-Lincoln, Lincoln, NE
Focused on machine learning, NLP, and speech research, including graduate work on automatic speech recognition, transcription accuracy, and accent-related bias in voice systems.
2020 - 2022Park University
Currently expanding skills in analytics, business decision support, and data-driven storytelling to complement an engineering and ML background.
2024 - CurrentUniversity of Nebraska-Lincoln (BOSR & OASIS)
Analyzed educational and survey datasets using Python and SQL, built summaries and visual reports for non-technical stakeholders, and improved data processing and validation workflows.
2020 - 2022Ameritas Life Insurance
Supported secure data ingestion, access controls, governance processes, and reporting-oriented operational data workflows across enterprise systems.
2021 - 2022Thales Group, New York, NY
Maintained SQL- and Python-supported data pipelines for transportation systems, diagnosed system issues, and documented operational workflows for high-availability environments.
2023 - 2024Benedict College
Taught Python fundamentals, mentored students on applied analytics concepts, and supported LearnBot work centered on chatbot analysis and user experience insights.
2024 - CurrentDallas Data Science Academy, Dallas
Built EDA pipelines, regression and classification models, Prophet forecasts, Tableau dashboards, and NLP sentiment workflows on education and healthcare datasets.
2025 - CurrentI'm open to data analytics, machine learning, NLP, and generative AI opportunities. Reach me directly at nickmoonmware@gmail.com or send a message below.