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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... more
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.... more
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... more
In Elicitation: Artifact and Stakeholder Driven Analysis, you will learn to use both recorded and presently unrecorded knowledge in your elicitation techniques. As you get started in finding out... more
In Requirements Goal Development and Language Analysis, we move from the spoken word to precise writing. A first step in this is writing goals. We will talk about goals used in requirements... more
The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You\'ll master essential spreadsheet... more
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... more
What does it mean to be influential? How does one persuade others to pursue a unified goal? How does one leverage power? In this course, you\'ll learn how to develop influence and to become more... more
The lectures of this course are based on the first 11 chapters of Prof. Raymond Yeung\'s textbook entitled Information Theory and Network Coding (Springer 2008). This book and its predecessor, A... more
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to... more
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... more
The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the... more
Identify interesting questions, analyze data sets, and correctly interpret results to make solid, evidence-based decisions. This Specialization covers research methods, design and statistical... more
We have all heard the phrase \"correlation does not equal causation.\" What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn... more
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... more
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... more
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... more
The objectives of this MOOC are to enable you to learn from applying concepts, exercises and learning from the four other MOOCs in this specialization. Learners involved in our programs over the... more
The final course aims to build learners\' confidence and adaptability when communicating in cross-cultural environments as they assume leadership responsibility to communicate influence in... more
This course offers a rigorous mathematical survey of causal inference at the Master\'s level. Inferences about causation are of great importance in science, medicine, policy, and business. This... more