Data Science

• Development
• Data Science
• Free
• Johns Hopkins University (jhu)
• Coursera

This is a focused course that will quickly bring you up to speed in the field of data science. Our goal was to make this as easy for you as possible while not sacrificing any essential content. We've left out the technical details so you can focus on managing and moving your team forward.

• Development
• Data Science
• Data Science
• Free
• Universidad Nacional Autónoma de México
• Coursera

Introducción a Data Science: Programación Estadística con R from Universidad Nacional Autónoma de México. Este curso te proporcionará las bases del lenguaje de programación estadística R, la lengua franca de la estadística, el cual te permitirá ...

• Development
• Data Science
• Data Science
• Free
• Udemy

Learn Data science / Machine Learning using Python (Scikit Learn)

• Development
• Data Analysis
• Free
• Udemy

FREE Python and R Programming Course on Data Science, Machine Learning, Data Analysis, and Data Visualization

• Development
• Data Science
• Data Science
• Free
• Duke University
• Coursera

Data Science Math Skills from Duke University. Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was ...

• Development
• Data Science
• Data Science
• Free
• Coursera

Practical Time Series Analysis from The State University of New York.

• Development
• Data Science
• Free
• Johns Hopkins University (jhu)
• Coursera

This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it.

• Development
• Data Science
• Free
• Udemy

Construct an ANN Regression model to forecast the electrical energy output of a Combined Cycle Power Plant.

• Development
• Data Science
• Data Science
• Free
• Eindhoven University of Technology
• Coursera

Process Mining: Data science in Action from Eindhoven University of Technology.

• Development
• Data Science
• Free
• University of Pennsylvania
• Coursera

Learn how to define causal effects, what assumptions to make about your data and models, and how to implement and interpret some popular statistical methods. Learners will be able to apply these methods to example data in R. (free statistical software environment).