This dashboard offers a multidimensional analysis of Boston using publicly available data from the U.S. Census Bureau and the City of Boston Open Data Portal. Drawing from the 2020 American Community Survey (ACS), the 2020 Decennial Census, and 311 service request records, the visualizations provide insights into the city’s socioeconomic landscape, racial composition, and patterns of civic engagement. The first section visualizes median household income across Boston’s census tracts using 5-year estimates from the ACS. This choropleth map displays estimated median income by geographic unit, highlighting spatial disparities in wealth and economic opportunity within the city. Tracts with unavailable or suppressed data are shown in gray to maintain transparency around data coverage. The second section presents a racial breakdown of Boston’s population using the 2020 Decennial Census. It incorporates population counts from key race variables as well as the total population. These values are visualized in a bar chart that includes both absolute counts and proportional shares of the total population, emphasizing the city’s racial diversity and demographic complexity. The final two sections examine resident interaction with local government through the lens of 311 service requests. One line chart displays the daily number of requests submitted in 2020 across the top five reporting neighborhoods, based on the cleaned neighborhood field and grouped by submission date. The second visualization, a summary table, categorizes all requests by submission source (e.g., Citizens Connect App, Constituent Call, Self Service) and case status (e.g., Open, Closed), revealing how residents used different platforms to report issues and how those requests were addressed. Together, these visualizations provide a layered view of Boston—through income, race, and civic participation—offering a richer understanding of the city’s social and structural dynamics.

Modelado El objetivo es construir tu primer modelo simple para la relación entre palabras. Este es el primer paso para crear una aplicación de minería de texto predictiva. Explorarás modelos simples y descubrirás técnicas de modelado más complejas. Tareas a realizar Construya un modelo básico de n-gramas: utilizando el análisis exploratorio que realizó, construya un modelo básico de n-gramas. modelo de n-gramas para predecir la siguiente palabra basándose en las 1, 2 o 3 palabras anteriores. Construya un modelo para gestionar n-gramas no observados. En algunos casos, los usuarios querrán escribir una combinación de palabras que no aparece en los corpus. Construya un modelo para gestionar casos en los que un n-grama en particular no se observa.

The Iris Species Explorer presentation provides an interactive overview of the classic Iris dataset, which is widely used in machine learning and statistics for classification tasks. The dataset contains measurements of sepal length, sepal width, petal length, and petal width for 150 iris flowers belonging to three species: Setosa, Versicolor, and Virginica. This presentation includes: A summary of the dataset’s structure and species distribution. Visualizations such as boxplots comparing sepal and petal lengths across species. A scatter plot exploring the relationship between petal length and sepal length, color-coded by species. The goal is to help users understand key differences between iris species through clear data summaries and graphical insights.

The Iris Species Explorer presentation provides an interactive overview of the classic Iris dataset, which is widely used in machine learning and statistics for classification tasks. The dataset contains measurements of sepal length, sepal width, petal length, and petal width for 150 iris flowers belonging to three species: Setosa, Versicolor, and Virginica. This presentation includes: A summary of the dataset’s structure and species distribution. Visualizations such as boxplots comparing sepal and petal lengths across species. A scatter plot exploring the relationship between petal length and sepal length, color-coded by species. The goal is to help users understand key differences between iris species through clear data summaries and graphical insights.