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Showing 1–25 of 79 items
Learn how to apply AI in healthcare diagnostics while understanding ethical, clinical, and technical considerations in medical decision-making.
Learn to ensure high data quality during collection and design processes to improve the integrity of research and analytics.
Use R and ggplot2 to explore public datasets and create insightful, equity-focused visualizations for planning and reporting.
Apply Python and scientific libraries like NumPy and SciPy to solve statistical problems and explore probability, distributions, and relationships in data.
Use built-in Python data structures to perform increasingly complex data analysis and elevate your programming skills.
Discover how to use unsupervised learning techniques to find patterns in data, including clustering, topic modeling, and dimensionality reduction.
Create relational database models using PostgreSQL, practicing data modeling, SQL commands, and schema creation through hands-on exercises.
Use SQL and Python to gather, store, and visualize data, including building web crawlers and working with databases.
Apply your Python skills in a final project to retrieve, process, and visualize data using real-world datasets.
Gain essential knowledge of generative AI applications, benefits, and risks, from societal impacts to legal and ethical concerns.
Explore governance strategies and global regulatory frameworks to ensure generative AI is deployed ethically and transparently in organizations.
Learn to apply data analysis tools and statistical methods to evaluate public policies and inform decisions using R.
Explore generative AI essentials, ethical use, authorship, and regulation through expert insights on AI’s impact on society and work.
Learn statistical fundamentals, including study design, data visualization, and inference, while using Python tools like Pandas and Matplotlib to analyze data.
Explore networks and connections using Python’s NetworkX library to measure centrality, evolution, and structure in social systems.
Learn how to assess and improve data quality before analysis using the Total Data Quality framework.
Learn R programming and core data analytics skills by analyzing real public sector datasets using tidyverse and dplyr libraries.
Explore how PostgreSQL handles full-text search, JSON data, and inverted indexing for advanced natural language processing.
Apply machine learning techniques in Python to build, validate, and optimize predictive models using scikit-learn.
Use Python to analyze baseball performance data and explore the evolution of Moneyball-era statistics through hands-on coding.
Learn to create meaningful data visualizations in Python using matplotlib and design principles for clarity and insight.
Learn Python basics and explore data manipulation using Pandas to clean, analyze, and visualize tabular data.
This Teach-Out introduces learners to artificial intelligence and explains how large language models and chatbots like ChatGPT work. You will better understand the ethical use of artificial intelligen
Learn how to evaluate and measure data quality at each stage of the data lifecycle using metrics, tools, and real-world applications.
Learn to use AI tools like ChatGPT to analyze, visualize, and interpret data for real-world applications and projects.
