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Learn Linear Model online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.
Harvard University
Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.
Harvard University
A focus on several techniques that are widely used in the analysis of high-dimensional data.
Harvard University
The structure, annotation, normalization, and interpretation of genome scale assays.
Harvard University
A focus on the techniques commonly used to perform statistical inference on high throughput data.
Harvard University
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
Harvard University
Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.
Harvard University
Learn to use R programming to apply linear models to analyze data in life sciences.
Johns Hopkins University
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra…
MIT OpenCourseWare
Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms…
Johns Hopkins University
Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multiva…
Johns Hopkins University
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares a…
Delft University of Technology
Learn the fundamentals of machine learning to help you correctly apply various classification and regression machine learning algorithms to real-life problems using the Python toolbox scikit-learn.
MIT OpenCourseWare
A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis distribution through permutation, useful parametric distributions, the generalized linear model, and model-based analyses more generally. Familia…
Dartmouth College
With Dartmouth Engineering, learn to transform data into actionable strategies in Prescriptive Analytics for Digital Transformation. Use Python to build and solve optimization models, tackle complex decisions, and leverage prescriptive tools to drive efficient, data-driven innovations.
Wageningen University & Research
Join the transition towards a circular economy considering economic, supply chain, social, technical, managerial and environmental aspects.
Université catholique de Louvain
À partir d’études de cas en économie et finance, apprenez à construire des modèles pour améliorer vos facultés d’analyse et de prévision.
Johns Hopkins University
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to unde…
MITx
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.
University of Canterbury
Now that you know the basics of Bayesian inference, dive deeper to explore its richness and flexibility more fully. Let’s take a closer look at modeling latent variables, Bayesian model averaging, generalised linear models, and MCMC methods
University of Colorado Boulder
Build Your Statistical Skills for Data Science
