Data Literacy Specialization

This specialization is intended for professionals seeking to develop a skill set for interpreting statistical results. Through four courses and a capstone project, you will cover descriptive statistics, data visualization, measurement, regression modeling, probability and uncertainty which will prepare you to interpret and critically evaluate a quantitative analysis.

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Data Literacy Capstone – Evaluating Research (Coursera)

This is the final course in the Data Literacy Specialization. In this capstone course, you'll apply the skills and knowledge you have acquired in the specialization to the critical evaluation of an original quantitative analysis. The project will first require you to identify and read a piece [...]
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What are the Chances? Probability and Uncertainty in Statistics (Coursera)

May 24th 2021
What are the Chances? Probability and Uncertainty in Statistics (Coursera)
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This course focuses on how analysts can measure and describe the confidence they have in their findings. The course begins with an overview of the key probability rules and concepts that govern the calculation of uncertainty measures. We’ll then apply these ideas to variables (which are the [...]
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Quantifying Relationships with Regression Models (Coursera)

This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We’ll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable. [...]
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Measurement – Turning Concepts into Data (Coursera)

This course provides a framework for how analysts can create and evaluate quantitative measures. Consider the many tricky concepts that are often of interest to analysts, such as health, educational attainment and trust in government. This course will explore various approaches for quantifying these concepts. The [...]
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Data – What It Is, What We Can Do With It (Coursera)

This course introduces students to data and statistics. By the end of the course, students should be able to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights. The course first introduces a framework for thinking about the various purposes of statistical analysis. We’ll talk [...]
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