Data Literacy

Johns Hopkins University via Coursera Specialization

Go to class Write review

Details

Go to class

Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

22 weeks, 3 hours a week
Level

Beginner

Found in

Overview

Class Central Tips

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.

Syllabus

Course 1: Data – What It Is, What We Can Do With It
- Offered by Johns Hopkins University. This course introduces students to data and statistics. By the end of the course, students should be ... Enroll for free.

Course 2: Measurement – Turning Concepts into Data
- Offered by Johns Hopkins University. This course provides a framework for how analysts can create and evaluate quantitative measures. ... Enroll for free.

Course 3: Quantifying Relationships with Regression Models
- Offered by Johns Hopkins University. This course will introduce you to the linear regression model, which is a powerful tool that ... Enroll for free.

Course 4: What are the Chances? Probability and Uncertainty in Statistics
- Offered by Johns Hopkins University. This course focuses on how analysts can measure and describe the confidence they have in their ... Enroll for free.

Course 5: Data Literacy Capstone – Evaluating Research
- Offered by Johns Hopkins University. This is the final course in the Data Literacy Specialization. In this capstone course, you'll apply ... Enroll for free.

Courses

0 reviews
11 hours 4 minutes
View details

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.
0 reviews
5 hours 51 minutes
View details

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 of high-quality, original, quantitative research on a topic of your choosing. You’ll then interpret and evaluate the findings as well as the methodological approach. As part of the project, you’ll also review other students’ submissions. By the end of the project, you should be empowered to be a critical consumer and user of quantitative research.
0 reviews
11 hours 28 minutes
View details

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.
0 reviews
10 hours 31 minutes
View details

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.
0 reviews
11 hours 7 minutes
View details

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 about how analysts use data for descriptive, causal and predictive inference. We’ll then cover how to develop a research study for causal analysis, compute and interpret descriptive statistics and design effective visualizations. The course will help you to become a thoughtful and critical consumer of analytics. If you are in a field that increasingly relies on data-driven decision making, but you feel unequipped to interpret and evaluate data, this course will help you develop these fundamental tools of data literacy.

Taught by

Jennifer Bachner, PhD

Reviews

Start your review of Data Literacy

Go to class

BloomTech’s Downfall: A Long Time Coming

Most common

Popular subjects

Popular courses

Data Literacy

Overview

Syllabus

Courses

Measurement – Turning Concepts into Data

Data Literacy Capstone – Evaluating Research

Quantifying Relationships with Regression Models

What are the Chances? Probability and Uncertainty in Statistics

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

Taught by

Tags

Reviews

BloomTech’s Downfall: A Long Time Coming

Measurement – Turning Concepts into Data

Data Literacy Capstone – Evaluating Research

Quantifying Relationships with Regression Models

What are the Chances? Probability and Uncertainty in Statistics

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

Taught by

Tags

Data Science: Statistics and Machine Learning

Advanced Statistics for Data Science

Business Statistics and Analysis

Data Science

What are the Chances? Probability and Uncertainty in Statistics

Genomic Data Science

1700 Coursera Courses That Are Still Completely FREE

250 Top FREE Coursera Courses of All Time

Massive List of MOOC-based Microcredentials

Never Stop Learning.