statistics

  1. R

    Study Buddy Study Buddy

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    Statistics/TimeSeries/ML/AI Study Group

    This group is intended for serious and committed MOOC learners who would like to follow online courses in ML/AI/TimeSeries together. The idea is to further explore the courses together and even do some complementary material. The group is intended to understand the methodologies and foundations...
  3. Coursera

    Coursera Understanding and Visualizing Data with Python

    Overview In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for...
  4. Coursera

    Coursera Inferential Statistical Analysis with Python

    Overview In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to...
  5. Coursera

    Coursera Fitting Statistical Models to Data with Python

    Overview In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting...
  6. Coursera

    Coursera Understanding Clinical Research: Behind the Statistics

    Overview If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or...
  7. Coursera

    Coursera Bayesian Statistics: From Concept to Data Analysis

    Overview This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian...
  8. Coursera

    Coursera Bayesian Statistics: Techniques and Models

    Overview This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more...
  9. Coursera

    Coursera Improving your statistical inferences

    Overview This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might...
  10. Coursera

    Coursera Probability and Statistics: To p or not to p?

    Overview We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact events. To study, or not to study? To invest, or not to invest? To...
  11. Coursera

    Coursera Statistical Inference

    Overview Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore...
  12. Coursera

    Coursera Regression Models

    Overview 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...
  13. Coursera

    Coursera Basic Statistics

    Overview Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the...
  14. Coursera

    Coursera Inferential Statistics

    Overview This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using...
  15. Coursera

    Coursera Business Applications of Hypothesis Testing and Confidence Interval Estimation

    Overview Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help enhance your business decision making and allow you to understand and measure the extent of ‘risk’ or ‘uncertainty’ in various business processes...
  16. Coursera

    Coursera Linear Regression for Business Statistics

    Overview Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. This is the fourth course in the specialization...
  17. Coursera

    Coursera Introduction to Probability and Data

    Overview This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be...
  18. Coursera

    Coursera Inferential Statistics

    Overview This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using...
  19. Coursera

    Coursera Linear Regression and Modeling

    Overview This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student...
  20. Coursera

    Coursera Bayesian Statistics

    Overview This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and...
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