5 Free Online Data Science Courses By Stanford University – News18

Last Updated:January 22, 2025, 08:34 IST

Stanford University offers several free online courses to help you master the skills required for a career in data science.

One of the leading providers of online education is Stanford University, offering a variety of courses aimed at enhancing professional knowledge and skills. With years of experience in the field, Stanford University is renowned for delivering excellent online training.

Data science is one of the most sought-after fields today, and the best part? You do not need to pay for expensive degrees to get started.

Data science involves studying data to uncover business-relevant insights. This multidisciplinary field utilises mathematics, statistics, computer engineering, and artificial intelligence to analyse large datasets.

To help you acquire the necessary data science skills, we have compiled this list of free courses offered by Stanford University:

R Programming Fundamentals

This course covers the basics of R, a free software environment and programming language used for statistical computing and graphics. R is widely utilised by statistical researchers, data scientists, and data analysts globally. The course provides an overview of R, from installation to basic statistical operations. You will learn to build functions, work with variables and external datasets, and hear from Robert Gentleman, one of the co-creators of the R language.

Statistical Learning with R

This introductory course in supervised learning focuses on regression and classification techniques. The University aims to explain these techniques without relying heavily on formulas and complex mathematics. The course covers linear and polynomial regression, logistic regression, and linear discriminant analysis; cross-validation and bootstrapping, model selection and regularisation methods (ridge and lasso); nonlinear models, splines, and generalised additive models; tree-based methods, random forests, and boosting; and support vector machines. It also discusses unsupervised learning approaches, including principal components and clustering (k-means and hierarchical).

Performance Assessment in the NGSS Classroom: Course 1

This course, part of the Stanford NGSS Assessment Project (SNAP), helps participants explore how performance assessment can support students in meeting the objectives of the Next Generation Science Standards. Participants will learn SNAP’s methods for evaluating assessments, analysing multifaceted student data, and making instructional decisions based on evidence of students’ progress and additional needs. Throughout the course, participants will practise applying these concepts using student data and sample brief performance assessments (20-minute activities).

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Automata Theory

This course covers the theory of automata and languages, including topics such as the equivalence of different language-defining systems, regular expressions, and deterministic and non-deterministic automata. A sufficient level of “mathematical sophistication” is required, meaning you should be comfortable with proofs and mathematics.

Reservoir Geomechanics

This interdisciplinary course addresses a variety of geomechanical issues encountered during the extraction of oil and gas reservoirs, integrating structural geology, rock mechanics, earthquake seismology, and petroleum engineering. It is designed for research scientists interested in stress measurements and their relation to faulting and fluid flow in the crust, as well as geoscientists and engineers in the petroleum and geothermal industries.