- visualization
- Measurement Uncertainty
- Probability
- Statistical Hypothesis Testing
- Statistics
- Causal Inference
- Data Visualization (DataViz)
- Empirical Evidence
- Cross-Sectional Analysis
- Basic Descriptive Statistics
- Survey Design
- Statistical Analysis
Spécialisation Data Literacy
Become a Data-driven Leader. Master the Fundamentals of Interpreting Data
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Compétences que vous acquerrez
À propos de ce Spécialisation
Projet d'apprentissage appliqué
Learners will develop expertise in calculating and interpreting statistical quantities, such as causal effects and measures of uncertainty. Learners will apply their knowledge to evaluating quantitative results and solving statistical problems. For the capstone project, learners will select and critically evaluate a piece of published, quantitative research.
An interest in learning how to interpret data in an applied manner
An interest in learning how to interpret data in an applied manner
Comment fonctionne la Spécialisation
Suivez les cours
Une Spécialisation Coursera est une série de cours axés sur la maîtrise d'une compétence. Pour commencer, inscrivez-vous directement à la Spécialisation ou passez en revue ses cours et choisissez celui par lequel vous souhaitez commencer. Lorsque vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Il est possible de terminer seulement un cours : vous pouvez suspendre votre formation ou résilier votre abonnement à tout moment. Rendez-vous sur votre tableau de bord d'étudiant pour suivre vos inscriptions aux cours et vos progrès.
Projet pratique
Chaque Spécialisation inclut un projet pratique. Vous devez réussir le(s) projet(s) pour terminer la Spécialisation et obtenir votre Certificat. Si la Spécialisation inclut un cours dédié au projet pratique, vous devrez terminer tous les autres cours avant de pouvoir le commencer.
Obtenir un Certificat
Lorsque vous aurez terminé tous les cours et le projet pratique, vous obtiendrez un Certificat que vous pourrez partager avec des employeurs éventuels et votre réseau professionnel.

Cette Spécialisation compte 5 cours
Data – What It Is, What We Can Do With It
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.
Measurement – Turning Concepts into Data
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 course begins with an overview of the different levels of measurement and ways to transform variables. We’ll then discuss how to construct and build a measurement model. We’ll next examine surveys, as they are one of the most frequently used measurement tools. As part of this discussion, we’ll cover survey sampling, design and evaluation. Lastly, we’ll consider different ways to judge the quality of a measure, such as by its level of reliability or validity. By the end of this course, you should be able to develop and critically assess measures for concepts worth study. After all, a good analysis is built on good measures.
Quantifying Relationships with Regression Models
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. Building on this foundation, we’ll then discuss how to create and interpret a multivariate model, binary dependent variable model and interactive model. We’ll also consider how different types of variables, such as categorical and dummy variables, can be appropriately incorporated into a model. Overall, we’ll discuss some of the many different ways a regression model can be used for both descriptive and causal inference, as well as the limitations of this analytical tool. By the end of the course, you should be able to interpret and critically evaluate a multivariate regression analysis.
What are the Chances? Probability and Uncertainty in Statistics
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 building blocks of statistics) and their associated probability distributions. The second half of the course will delve into the computation and interpretation of uncertainty. We’ll discuss how to conduct a hypothesis test using both test statistics and confidence intervals. Finally, we’ll consider the role of hypothesis testing in a regression context, including what we can and cannot learn from the statistical significance of a coefficient. By the end of the course, you should be able to discuss statistical findings in probabilistic terms and interpret the uncertainty of a particular estimate.
Offert par

Université Johns-Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Foire Aux Questions
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