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Hello!
My name is Trisha and I am a third year student at the University of Washington studying Informatics with a concentration in Data Science. I firmly believe in the power of data to tell stories, drive decision-making and optimize business functions. In my time at UW, I've strengthened my coding skills along with developing my leadership, collaborative, and interpersonal skills. I'm proficient in multiple programming languages like Java, Python, SQL, and R. I'm also well-versed in common data visualization tools like Tableau and PowerBI. I've applied these skills not only in my rigorous coursework in the Information School, but in my personal projects as well. I'm always looking for new opportunities where I can gain hands-on experience and further develop my skills!
Skills
Java
Python
R
SQL
Data wrangling + modeling
Visualization tools
Statistical + Machine Learning Modeling
Excel
Relevant Coursework
Computer Programming I and II
CSE 142 + 143
Introduction to basic programming in Java. Learned about procedural programming, recursion, file processing, arrays, and data structures like stacks, queues, linked lists, and binary trees.
Data Structures and Algorithms
CSE 373
Covered fundamental algorithms and data structures necessary for implementation. Learned about common techniques for solving problems by programming. Dove deeper into linked lists, stacks, queues, directed graphs, trees (representations, traversals), and searching (hashing, binary search trees, multiway trees).
Research Methods
INFO 300
Introduction to research methods used to understand people's interactions with information, information technology, and information systems. Learned how to conduct qualitative and quantitative research studies, and applied design methods for answering questions in both research and practical settings.
Databases and Data Modeling
INFO 330
Introduction to database systems and the SQL language. Learned about industry-best practices in SQL like stored procedures, computed columns, business rules, and complex queries. Covered the relational model, entity-relationship modeling, three-tier architectures, implementation of database applications, and non-relational databases. Briefly went over data visualization tools -- Tableau and PowerBI.
Client-side Development
INFO 340
Introduction to client-side development on the internet, including markup, programming languages, protocols, libraries, and frameworks for creating and maintaining usable and accessible, interactive applications.
Core Methods in Data Science
INFO 370
Surveys the major topics within data science, including data ingestion, cloud computing, statistical inference, machine learning, information visualization, and data ethics. Used Python libraries like Pandas, NumPy, Scikit-Learn, and TensorFlow
Elements Of Statistical Methods
STAT 311
Covered descriptive statistics including correlation and regression. Introductory concepts of probability and sampling; binomial and normal distributions. Learned about the basic concepts of hypothesis testing, estimation, and confidence intervals; t-tests and chi-square tests. Conducted statistical analyses in R.
Matrix Algebra With Applications
MATH 308
Learned about systems of linear equations, vector spaces, matrices, subspaces, orthogonality, least squares, eigenvalues, eigenvectors, applications.