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Learn Hypothesis Testing, earn certificates with free online courses from Stanford, MIT, University of Pennsylvania, University of Michigan and other top universities around the world. Read reviews to decide if a class is right for you.
Get ready to analyze, visualize, and interpret data! Thought-provoking examples and chances to combine statistics and programming will keep you engaged and challenged.
Learn to draw conclusions from data using statistical inference techniques, covering probability, distributions, hypothesis testing, and advanced methods for robust analysis.
Intro to Inferential Statistics will teach you how to test your hypotheses and begin to make predictions based on statistical results drawn from data!
We live in a time of unprecedented access to information. You'll learn how to use statistics to interpret that information and make decisions.
This course will be exclusively quantitative and an application to business/management-related problems. It is connected with problem sets and real life cases to know the relevance of a particular problem and the decision-making thereof.
Design and implement a personalized positive intervention to enhance well-being using character strengths, psychological theories, and empirical methods.
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Comprehensive biostatistics primer for healthcare professionals, equipping learners to interpret scientific literature and participate in research teams across public health and related fields.
Explore inferential statistics using Python, covering confidence intervals and hypothesis testing for various population parameters. Apply concepts to real-world case studies using statistical libraries.
Explore statistical learning techniques for machine learning, including linear regression and classification. Apply concepts through Python coding assignments and practical problem-solving.
Learn statistical analysis using Python, from data collection and visualization to advanced modeling, enhancing your ability to answer research questions with data-driven insights.
Learn the statistical concepts and techniques you need to conduct rigorous inferential analysis and draw accurate conclusions from data sets.
Master statistical concepts through hands-on practice with R, covering descriptive statistics, probability distributions, data visualization, and hypothesis testing for real-world data analysis.
Master statistical decision-making in R through hands-on practice with t-tests, ANOVA, chi-square, and non-parametric tests. Apply these methods to real-world data analysis scenarios.
Master essential probability and statistics concepts crucial for machine learning, from basic probability and descriptive statistics to hypothesis testing and linear regression analysis.
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