- Academic Transcript
- Academic Portfolio (for recruiters)
- Recommendation Letters
- Statement of Intent
Folders above are descriptions of each course taken and links to repositories of projects completed.
Program goal (Graduate Bulletin link):
The Master of Decision Analytics provides students with knowledge of the statistical, mathematical and scientific skills and experience necessary to utilize advanced methods of data analysis for business decision-making. Student learning goals Students will be able to examine a situation/problem to determine a relevant data-driven analysis to provide valuable information for decision makers and apply advanced analytical and quantitative skills to the decision problems of businesses, organizations and society. Students will be able to communicate analysis information and recommended decisions in a clear, ethical and transparent manner.
- Database structures and query: Students will have an understanding of basic database structures, be able to query databases and organize data for analysis.
- Quantitative skills: Students will be able to identify appropriate data analysis approaches to address real-world problems. They will be able to perform the analysis using commercial software.
- Problem formulation: Students will have the knowledge, skills and practice to take nonquantitative and perhaps ill-formed problems and issues and determine ways objective analysis can bring organization and insight to them. They will be able to determine data requirements and query available databases.
- Analytics applications: Students will experience various applications of analytics in real situations.
- Technical communications and teamwork: Students will be able to communicate analytical analysis and results effectively to nonquantitative audiences, and will develop skills in organizing, interacting and analyzing real problems as members of a team.
For Projects Completed, See Repositories
The Honor Society of Phi Kappa Phi, the nation’s oldest, most selective, and most prestigious all-discipline honor society. Standards for election to Phi Kappa Phi are extremely high. Membership is by invitation only to VCU’s top 7.5 percent of second term juniors and the top 10 percent of seniors and graduate students. Virginia Commonwealth University chapter member.
Projects tagged with ⭐ are my personal favorites, do check them out!
(Machine Learning: Deterministic and Probabilistic Modeling, Optimization, Simulation)
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⭐ Federal Reserve Bank of Richmond Workspace Allocation Optimization Won Optimization model class competition issued by Dr. Brooks (M.D.A. department chair and professor). Proposed Python, Pyomo and GLPK network optimization model approach with binary variables and logical constraints to simulate reorganization of 1700 workspaces across 17 floors, while allocating for changing project teams and requirements. Provided report to IT Vice President, Christine Holzem at the Federal Reserve Bank of Richmond.
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Optimization and Simulation Proposal for SCC Bureau of Insurance Proposed optimization and simulation framework to benefit helpdesk request distribution and simulate future request volume.
(Modeling via Prediction, Classification and Clustering: Machine Learning, Classification Trees, Regression, Random Forests, Support Vector Machines, etc.)
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⭐ Alchemy Broker Analysis Project: Performed segmentation analysis and predictive modeling on insurance broker performance to conclude a random forest model (highest AUC of 73%) predicted whether 2020 Gross Written Premium will increase or decrease from 2019 with a misclassification rate of 35%. Four classification models (classification trees, logistic regression, random forests, and support vector machines) were built, evaluated, and then tuned for prescriptive measures to analyze broker performance. Explored, visualized, and described five groups of brokers using principal component analysis.
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⭐Lending Club Cluster and PCA Repository - Using loan data, performed a k-means cluster analysis to identify 7 groups or clusters and two PCAs of borrowers with multiple variables. Explained all preprocessing steps. Performed PCA to identify characteristics of each cluster. Evaluated how clusters compare to assigning applicants to clusters by loan grade. Supported comparison with visuals.
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⭐Lending Club Classification Analysis Repository - Built a logistic regression and classification tree models for predicting the final status of a loan based on multiple variables available. Confusion matrix and misclassification rate for each model for a test dataset. Advised variables that appear to be important for predicting outcome. Plotted and described the ROC curves and AUC for the four models to provide my recommendation.
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⭐ Statistical Analysis and Modeling Course Repository Statistical analysis and modeling for decision analytics via R and XLSTAT. Topics covered had an applied focus and include logistic regression, bootstrap estimation, permutation tests, categorical data analysis, model selection, sparse methods and Bayesian methods. Repository includes topics covered per module.
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Whiskey Analysis Logistic regression model to predict the best and worst whiskeys using Confusion Matrix with training and validation samples. Correlation matrix, goodness of fit statistics, Hosmer-Lemeshow test, chi-squared, confusion matrix, Scatter plots, box plots etc.
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⭐ Richmond Bank Total Core Deposit Forecasts Using MS Excel and R, accurately forecasted total core deposit data from a Richmond Bank. The Holt’s Linear Exponential Smoothing had the overall lowest “Quick and Dirty” MAPE (1.2%), the lowest overall Maximum MAPE (3.49%), and consistently more accurate projections for each of the forecast horizons. Overall, the Unaided, Holts Linear Exponential Smoothing, and both regressions overestimated while the Naïve, 12 Month (M) Center Moving Average (CMA), 3M Moving Average (MA), 6M MA, Damped Trend Exponential Smoothing, and Simple Exponential Smoothing underestimated.
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Richmond Casino Proposal Analysis Research analysis completed on the proposal of a Richmond, VA Casino presentation including history/background, economic/revenue impact, frustrations, process etc.
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New Product Forecasting New product forecasting concepts (such as Delphi method, Assumptions Based, ATAR model, and Sensitivity Analysis) used to forecast and present an innovative smart sticky note printer.
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Forecasting with R Forecasting described from the perspective of using R and R studio software.
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KJ Manufacturing Company Case Scenario
- Discussed the forecasting process at KJ Manufacturing, any relevant factors about the company and industry that are pertinent to the new forecast and Ken’s forecast.
- Forecasted monthly revenues for KJ Manufacturing for the coming year. Used a variety of methods and graphically displayed them. Explained and supported the new forecasting approach as well as the choice of models and the rational for parameters selected.
- Prepared a report to owner explaining/supporting the forecast.
- ⭐ Project Proposal Decision Tree Objectives, uncertainties, influence diagram, assumptions, decision tree model, probability and cumulative comparison node chart (supporting documents – proposal, excel analysis and presentation)
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⭐ Automated Systems DBMS Completed and proposed an Automated Systems Database to Manager - also created a MS Power BI Version. Centralized Relational SQL Database to help produce the appropriate roles for a position, creating consistency throughout departments and job titles (with the exception of optional roles for additional access) and reduce the number of access roles that are kept when changing positions. The DBMS unifies and consolidates system access to improve data security.
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Taylor’s Clothing DBMS Business rules, user requirements, ER diagram, entity relationships etc. (Oracle APEX)
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- Industry Analysis
- Environmental Assessment
- Strategic Review
- Growth through Acquisition
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Uber’s Failing Brand and Culture Analysis Uber Technologies Inc. brand analysis research report describing brand culture and change strategy. Historical strategic context of the brand, the role culture played in the performance decline of the brand, how the culture impacted financial performance, and how they are attempting to transform and renew the culture.
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Lowe’s Case Assessment Lowe’s industry analysis for the market space, brand positioning, environmental assessment, and strategic opportunities/dilemmas.
- Time Series Apple Watch Workout Analysis Time series health workout data was extracted from my Apple watch to analyze workout variables. A Scope, descriptive statistics, pivot tables, C-Chart and scatter plots were created to check workouts outside of control. Tableau work was used to display correlations.
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Opioid Prescribing Rates Analysis Semester long project working with Virginia Department of Social Services to assist in data centric reengineer useful data into VA’s major FAACT database. Tableau dashboard analysis and presentation created using data from 2016 to 2019 on Medicare Prescribing rates.
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Children’s Bureau Race and Ethnicity Analysis General statistics on the race and ethnicity of children in foster care analyzing statistics on variables such as Child Maltreatment, Children Waiting for Adoption, children adopted etc.
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Car Loan Negotiation Used Excel Goal seek to negotiate a car purchased with variables such as Price, APR, Years, Payment/month.
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Norton Engines Optimization analysis solved using Excel solver, sensitivity analysis, and slack. To optimally maximize profits, Norton Engines should produce 1000 Type A engines and 500 Type B engines for a total profit of $6,600,000.
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Music Sales Music sales displayed in a Tableau dashboard with a variety of graphs
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Airbnb A couple is deciding where to rent at an Airbnb in New York. Our team helped evaluate factors we thought would help them choose the best location using a Tableau dashboard story.
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IRIS Flower Data K-Means cluster analysis conducted using KNIME and Tableau
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Census Clustering US Census Bureau data K-Means cluster analysis and Logistic Regression conducted using KNIME and Tableau
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Lending Club Loan Analysis We are a group of investors, looking for the target group of people to give out a personal loan with expectations that it will be fully paid off. Used KNIME logistic regression and MS Excel data table to conclude our target group and focus factors.
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Netflix Student Competition Netflix data, such as Average user rating score and average rating description, visualized via Tableau dashboard