For the past 15 years, I have been teaching Computer Science in New York City Public Schools and building teachers' capacity for providing Computer Science Education through my work with CS4ALL . I am passionate about social justice and equity and use my data science expertise to uncover patterns in data to identify trends that demand our attention. I tell stories about what I find in the data to elevate the public’s awareness. I also empower others by teaching them how they too can work with data to develop their own insights.
My teaching experience includes over ten years of service in New York City Public Schools. I specialized in Physics Education, studying how misconceptions about the world affect students' understanding of science concepts. I quickly turned my eye to Computer Science education and received 5 years of training in curriculum development and CS pedagogy from CS4ALL, UpperlineCode (Giant Machines), CUNY Graduate Center, and others.
For over 5 years, I have been focusing on expanding CS education offerings in NYC Public Schools by writing and refining curriculum and developing and facilitating professional learning sessions for NYC public school teachers. I amplified my impact by training hundreds of teachers, thereby making CS education more accessible to NYC students. Trainings emphasized topics related to equity and inclusion, so that students understand how CS can empower them to drive social change within their communities.
With my background CS and Physics, Data Science was a natural next step. In the last 3 years I have been developing my expertise in Data Science through my coursework in CUNY Graduate School. I translated that expertise into a Data Science Fellowship program for NYC Public School teachers who are interested in learning more about Data Science. This fellowship empowers teachers to teach Data Science concepts and skills to their students. Some fun projects I have worked on include Sentiment Analysis using Natural Language Processing and Image Recogition using Convolutional Neural Networks.
Data analysis, data wrangling, data modeling, data preparation and pre-processing, statistics, data visualization, programming, quantitative analysis, machine learning, machine-learning models, data mining, debugging, hypothesis testing, A/B tests, regression, dashboard and application development
Python, R, SQL, HTML, CSS, JavaScript, Julia, Java, Git
Utlizing pedagogical methods to convey information to a broad audience, communication and collaboration with colleagues and partners, developing creative and progressive ideas to solve problems, adaptability to the latest technologies and trends, apply critical thinking to objectively approach data sets and minimize bias, ability to develop content-area knowledge to help support decision making.
This project explores image classification via a Convolutional Neural Network (CNN) which has become the gold standard
for solving image classification problems. A CNN is a class of deep learning neural
networks that uses a series of filters to extract features from a particular data set, while keeping parameters relatively low.
In this project, the keras package is used to contruct the model. keras is a high level deep learning library
that allows the use of a fully connected neural network to train a model to recognitize images that fall into one of
three categories. Specifically, the functions used in this project utilizes the Tensorflow library.
TensorFlow utilizes vectors as tensors to create
the sturcture of the neural network that was trained.
Women have been historically underrepresented in higher education, particularly in STEM fields. In recent years, headlines have appeared indicating that female college graduates are surpassing their male counterparts. In this project a statistical analysis of PhD students graduated in various STEM fields was undertaken to answer the question: Is the gender gap descreasing in PhD programs in which women have been historically underrepresented? The study found that although more men and women are earning degrees in STEM fields but, while men's participation is growing exponentially, women's increased participation is more linear. As a result, there is not a significant reduction in the gender gap.
This study focuses on the sentiment analysis of posts from the popular social media site Twitter.
Python was used to extract tweets using the Twitter API sub-library Tweepy. Keywords that were studied
include: remote learning, remote learning COVID, remote learning mental health, remote learning wellness, remote
learning teachers, remote learning students, and remote learning school closure. Posts were cleaned using the pandas
library, sentiment scores were assigned to each tweet using the TextBlob library, and word
importance was
calculated using the WordCloud library. Each sentiment category (positive, negative and neutral) was passed
through the LeXmo model to determine the prominent emotions found in each group.
This project utilizes NYC Open Data to create an application with the Dash library in Python to present data in a way that anyone can understand. It leverages the interactive power of the Dash framework to enable users to select variables of interest to visualize. With this app, users can choose a specific borough in New York City and a specific year, to access visualization on student enrollment in NYC Public Schools. Data was then aggregated by gender and ethnicity to gain more insight into attendance trends for sub-groups. This app serves as a model of an interactive visualization that uses open data to engage the public in conversations around trends and insights. It also serves as an exemplar for students and teachers who are building similar applications.