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Learn Predictive Modeling, earn certificates with free online courses from Stanford, MIT, University of Pennsylvania, University of Michigan and other top universities around the world. Read reviews to decide if a class is right for you.
Develop in-demand data science skills, from databases to machine learning, using Python and essential tools. Build a project portfolio and prepare for a high-growth career in data analysis and insights.
Learn to leverage data science techniques for informed decision-making and valuable insights. Covers data wrangling, statistical analysis, visualization, and predictive modeling using Python and machine learning algorithms.
Explore Generative AI's impact on data science, learning its technical workings and practical applications. Enhance analytical precision and foster innovation in your data-driven projects.
In this course, we introduce the characteristics of medical data and associated data mining challenges on dealing with such data. We cover various algorithms and systems.
Explore sports analytics through diverse projects, data scraping, and statistical analyses. Learn to predict outcomes, simulate games, and gain insights into various sports using data science techniques.
Learn powerful statistical tools: linear regression and models. Predict continuous values using various data types. Covers least squares, multiple regression, t-tests, ANOVA, and R implementation.
Learn to predict discrete outcomes using logistic regression, covering coefficients, maximum likelihood, R-squared, p-values, deviance, and practical implementation in R.
Learn to implement XGBoost for regression and classification, explore its mathematical foundations, and master advanced optimizations in Python.
Explore complex systems through parables, examining prediction models, chaos theory, and market dynamics to gain insights applicable to everyday life and decision-making.
Explore how Reservoir Computing and AI can predict chaotic systems like weather and brain dynamics, pushing beyond traditional forecasting limits and combining knowledge-free and knowledge-based approaches.
Explore fairness, accountability, and transparency in predictive modeling for criminal justice, with examples highlighting their importance in real-world applications.
Explore computational approaches for driving self-assembly of complex structures, unlocking new possibilities in materials science and nanotechnology.
Explore how network science aids in predicting outcomes, from flu vaccine efficacy to citation patterns, using mathematical models and computer simulations for informed decision-making.
Explore how network analysis reveals community leaders, predicts voting patterns, and improves transportation efficiency. Discover insights from network science across various domains.
Explore early warning signals for network dynamics, uncovering predictive indicators and their applications in complex systems analysis.
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