Use modern machine learning tools and python libraries. Explain how to deal with linearly-inseparable data. Compare logistic regression’s strengths and weaknesses. Explain what decision tree is & how ...

Linguists, chemists, business analysts, social scientists, and essentially everyone needs computational approaches to structure, analyze and present their data. However, non-experts are often ...

You don't have to spend a fortune and study for years to start working with big data, analytics, and artificial intelligence. Demand for "armchair data scientists" – those without formal ...

This course aims to cover various tools in the process of data science for obtaining, cleaning, visualizing, modeling, and interpreting data. Most of the tools introduced in this course will be based ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

The purpose of the course is to introduce the statistical methods that are critical in the performance analysis and selection of information systems and networks. It includes fundamental topics as ...

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This introductory ethics module for data science courses includes a reading, homework assignments, and case studies, all designed to spark a conversation about ethical issues that students will face ...