Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference. The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of [...]
Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is [...]
Understand machine learning’s role in data-driven modeling, prediction, and decision-making. Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?
Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets. In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access [...]
Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such [...]