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Introduction to Linear Models and Matrix Algebra. Learn to use R programming to apply linear models to analyze data in life sciences. Fat Chance: Probability from the Ground Up.
Increase your quantitative reasoning skills through a deeper understanding of probability and statistics. Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal Data Science: Probability.
Learn probability theory — essential for a data scientist — using a case study on the financial crisis of — Data Science: Inference and Modeling. Learn inference and modeling: two of the most widely used statistical tools in data analysis. Learn statistics, data analysis, business analytics and other in-demand subjects with courses from top universities and institutions around the world on edX. Statistics is an area of mathematics that deals with the study of data. Data sets can include population data with machine learning, sampling distributions, survey results, data analysis, normal distribution, hypothesis testing, data collected from experiments and much more.
Various statistical models can be applied in order to analyze and interpret the results of a set of data or to find relationships between different data sets.
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Text Generation with Markov Chains in Python. Getting Started with Tidyverse. Data-Driven Decision Making Certificate. Data Science Math Skills. Linear Regression for Business Statistics. An Intuitive Introduction to Probability. University of Zurich. Improving Your Statistical Questions. Experimental Design Basics. Arizona State University. Factorial and Fractional Factorial Designs. University of Pennsylvania. Response Surfaces, Mixtures, and Model Building. Probability and Statistics: To p or not to p?
University of London. University of Florida. Fitting Statistical Models to Data with Python. Regression Modeling in Practice. Wesleyan University. Other topics to explore. Arts and Humanities. Computer Science. Information Technology.
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