School CoursesComplete list of graduate school courses
Data Mining for Business Applications
Throughout this course, we learned fundamentals of data mining and the CRISP-DM process. We used R programming to clean data, visualize data, and use machine learning techniques such as feature engineering, linear regression, logistic regression, discriminant analysis, and tree-based models. This class went over various forms of supervised and unsupervised machine learning as well as understanding results of model performance. Two projects were completed in this course designed to solve business problems and offer recommendations, one with an analytical focus and one with a machine learning focus. (see Project Work for more details)
Big Data to Information
This course offered a wide range of topics and covered many big data tools like SQL, R, Python, machine learning, natural language processing, web scraping, and AWS tools. In addition to getting exposure to a wide range of tools, we studied the fundamentals of big data, data cleaning, and data ethics. This course ended with the completion of an individual data analysis project requiring us to use R, Python, SQL, and AWS. (see Project Work for more details)
Principles of Data Management and Mining
In this course, we learned techniques to store, manage and use data including databases, relational models, schemas, queries, and transactions. We covered theory of relational and NoSQL data bases, Hadoop, and Spark. This course offered a lot of knowledge sharing through thoughtful discussion posts on various topics such as data warehousing, EDA, CRISP-DM, clustering algorithms, and more. Throughout the course, we completed a group project using public data working with Agile methodologies to complete weekly sprints.
Applied Statistics and Visualization for Analytics
This course covered a variety of modeling techniques such as regression and random forests in addition to visualization techniques. R programming was the primary language used for both statistical analysis and visualization. We studies data access, variable selection, and model diagnostics. The course concluded up with a personal project on any chosen dataset utilizing modeling algorithms and visualization tools discussed throughout the course. With an interest in interactive visualization, I created an R-shiny dashboard and model using the world happiness data set collected by Gallup (see Project Work for more details)
Operations Research Analytics and Modeling
Course focused on prescriptive and predictive analytics with topics covering operations research techniques and their application to decision making such as mathematical optimization, networks modeling, stochastic modeling, and multi-objective modeling. We also studied Monte Carlo simulation, decision analysis using decision trees and quantitative value functions, and heuristic methods for non-smooth models. The techniques learned were practiced through small weekly case studies focused on modeling application and business meaning and impact. The modeling techniques were primarily conducted using Excel software.
Marketing Research
Throughout this course we developed skills to plan and implement effective marketing research studies. Topics included research design, data collection, statistical analysis, and use of database systems. Tableau and excel were the primary analytics tools used in addition to Qualtrics for survey creation. It offered perspective on how managers can use market data to develop successful product or service strategies. During the course, as teams, we defined a marketing problem, developed hypothesis, created and deployed a product survey, analyzed survey data, and developed business strategies and recommendations based on findings. (see Project Work for more details)
Marketing Analytics
This course covered several marketing analytics concepts to drive effective marketing decision making. In addition to data cleaning and visualization techniques, we covered several regression models, conjoint analysis, decision trees for customer segmentation and prediction, targeted marketing strategies, and customer management. The course concluded with a team marketing analysis project defining the chosen company’s business problem, data analysis and modeling, and business suggestions. (see Project Work for more details)
Advanced Data Mining for Business Analytics
Similar to the Data Mining for Business Analytics course, this course covered business analytics using advanced data mining methods for the purposes of developing predictive models and forecasting . The course covered the concept of feature selection to identify what dimensions to best use for constructing decision making models.
Data Analytics Capstone
This was the final course in the Data Analytics Engineering program where my team was assigned a project with the Puerto Rico Science Technology and Research Trust. Throughout this course we used Agile scrum methodologies to develop a product for rapid flood detection. As the Product owner, I lead weekly meetings with the client and lead product development through a shared repository. Using pre- and post-flood satellite images my team and I developed an algorithm to detect changes in pixel values and identify potential flood areas. The identified flood pixels were then merged to form shape files to identify flooded areas using GIS software. Upon the completion of the project our team delivered the product meeting all client objectives and presented on our findings in a course showcase. (see Project Work for more details)
Additional Courses and certifications
Data Analyst Associate Certificate - Data Camp