An overview of my education background and relevant coursework
Background
I am currently pursuing an M.S. in Data Science at the University of Virginia. Prior to enrollment, I took prerequisite courses in Mathematics and Computer Science at Bergen Community College. Below are the descriptions for my graduate courses.
Spring 2022
DS 5110 - Big Data Systems
The focus of the course is learning Spark, an open-source, general-purpose compuƟng framework that is scalable and blazingly fast. The fundamental data types and concepts will be covered (e.g., resilient distributed datasets, DataFrames). You will learn how to use Spark for large-scale analyƟcs and machine learning, among other topics. Tools for data storage and retrieval will be covered, including AWS and the Hadoop ecosystem.
DS 5001 - Exploratory Text Analytics
This course provides an introduction to text analytics with a focus on pattern discovery in long-form documents, such as reviews, news articles, and novels. Students convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and narrative structures. The focus is on unsupervised methods to explore cognitive and social patterns in texts. Code work employs the Python stack, including NumPy, Pandas, SciKit Learn, NLTK, and other packages.
Fall 2021
DS 6030 - Statistical Learning
Statistical learning describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, statistical learning uses inductive approaches to discover the appropriate models. These models describe a relationship between a system's response and a set of factors or predictor variables. Statistical learning in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from statistical learning for systems with both discrete and continuous valued responses. It covers both regression and classification and explores both practical and theoretical aspects of statistical learning.
CS 5012 - Foundations of Computer Science
In this course students gain a better understanding of the underpinnings of the Data Science tools they use from the perspective of a computer scientist. Students will learn to leverage this understanding to improve the effectiveness and efficiency of their code and analytical tools. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public policy, marketing, and more).
Summer 2021
DS 5100 - Programming for Data Science
An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization.
STAT 6021 - Linear Models for Data Science
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components. The primary software is R.