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Data Literacy

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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What's inside

Five courses

Data – What It Is, What We Can Do With It

(0 hours)

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.

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Measurement – Turning Concepts into Data

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This course provides a framework for analysts to create and evaluate quantitative measures. It explores approaches for quantifying concepts like health, educational attainment, and trust in government. Topics include measurement levels, variable transformations, measurement model construction, surveys, and measure quality assessment.

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Quantifying Relationships with Regression Models

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This course introduces the linear regression model, a powerful tool for measuring relationships between multiple variables. We'll explore bivariate and multivariate models, binary dependent variable models, and interactive models. We'll also consider incorporating categorical and dummy variables. By the end, you'll be able to interpret and critically evaluate multivariate regression analyses.

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What are the Chances? Probability and Uncertainty in Statistics

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This course focuses on how analysts can measure and describe the confidence they have in their findings. We'll discuss probability rules, concepts, variables, probability distributions, hypothesis testing, test statistics, confidence intervals, and the role of hypothesis testing in regression.

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Data Literacy Capstone – Evaluating Research

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This capstone course applies skills and knowledge acquired in the Data Literacy Specialization to the critical evaluation of original quantitative analysis. Students will identify and read a piece of high-quality, original, quantitative research on a topic of their choosing, interpret and evaluate the findings as well as the methodological approach, and review other students' submissions.

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Time

0 hours

Level

Introductory

Via

Coursera

Institution

Johns Hopkins University

Instructor

Jennifer Bachner, PhD

Level

Introductory

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