My name is Sita Robinson. I am a Senior Data Science major at Drexel University with a minor in Computer Science. I am expected to graduate with my Bachelors degree in 2020.
I am the president of Drexel Women in Computing Society (WiCS). Previously I was treasurer of WiCS for two years. My last job was in a data science co-op (6 month internship) at ELAP Services in Chesterbrook, Pennsylvania. In the summer of 2017, I was a Software Engineering Intern at Comcast in Philadelphia. Some of my hobbies include programming, photography, playing the clarinet, playing Scrabble and traveling around the world. I am originally a Northern Virginia/Metro D.C. native.
Data Science Course Assistant
April 6, 2020-June 11 2020
Data Science Co-op at ELAP Services
September 24, 2018-March 20, 2019
Software Engineering Intern at Comcast
June 19, 2017-August 25, 2017
Beginner: R, Node.js, ArcGIS (ArcMap, ArcGIS Online), LaTeX, Java, Unix, Tableau, JQuery Mobile, JQuery, Flask, Ruby on Rails, ReactJS, Hadoop, Apache Spark, Google Cloud
INFO 212,213 Data Science Programming I, II (Python)
INFO 250 Information Visualization (Tableau,RawGraphs, VOSviewer)
INFO 323 Cloud Computing & Big Data (Hadoop, Apache Spark, Google Cloud)
INFO 332 Exploratory Data Analytics (R)
INFO 371 Data Mining Applications (Weka, RapidMiner)
INFO 365, 366 Database Administration 1, II (Google Cloud, MariaDB)
INFO 400 Data Science Projects (Python)
INFO 432 Advanced Analytics (R)
INFO 440 Social Media Data Analysis (Python)
STAT 201,202 Business Statistics
CS 171,172 Computer Programming I,II (C++)
CS 260 Data Structures
CS 265 Advanced Programming Tools and Techniques
CS 283 Systems Programming (C)
SE 310 Software Architecture I (Java, Software Design Patterns)
Toast is an automated financial planning application for financial advisors and their clients. The application will be developed using Django and ReactJS.
Using the Python Scikit-Learn library to train and test the dataset from Kaggle to predict whether a transaction is fradulent or not using classification algorithms.
Developed a web application with backend to match the skills and qualifications needed for a job. Created a web scraper using the Beautiful Soup library in Python to scrape job data from Indeed.com. Leveraged text analytics to match employee skills to job postings. Used Flask which is a framework to run Python as a web server.
Built machine learning models using random forest to predict what factors are most important for student performance in exams using the "Student Performance in Exams" dataset from Kaggle. Performed pre-processing and exploratory data analysis. This project was completed using the R language.
Built a machine learning model using the linear regression, random forest, and gradient boosting algorithms to analyze what factors are most important for graduate admissions. The model was generated using the "Graduate Admissions 2" dataset from Kaggle. This project was completed using R.
Performed exploratory analysis of Black Friday data. In addition, used matrix factorization to study consumer behavior and provide recommendations for potential customers. We also explored the process of vectorization and the process of creating embeddings, using the results to find similar customers, find consumer groups using clustering, and predict what a given customer will buy.
Ⓒ Sita Robinson 2017