Johns Hopkins University
Data – What It Is, What We Can Do With It
Johns Hopkins University

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

This course is part of Data Literacy Specialization

Taught in English

Some content may not be translated

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Course

Gain insight into a topic and learn the fundamentals

4.6

(151 reviews)

Beginner level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

13 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(151 reviews)

Beginner level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Data Literacy Specialization
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There are 4 modules in this course

When most people think about using data, they quickly jump to considering the best way to analyze it with statistical methods. A good analysis, however, begins with a strong theoretical framework. A good theory will guide the collection of data, selection of appropriate statistical methods and interpretation of the results. Further, the theory will determine what kind of research design is needed, such as an observational study or experiment. This module will focus on the development of high-quality theories that can be used to guide descriptive, causal and predictive inference.

What's included

4 videos3 readings1 quiz1 discussion prompt

Establishing causality is frequently the primary motivation for research. Policymakers often want to understand how the implementation of a new program or other policy tool will affect an outcome of interest. Will smaller class sizes increase student learning? Will the implementation of stricter background checks for gun buyers reduce gun violence? Biomedical researchers often want to understand whether a new medicine will improve a disease outcome. Will taking a drug improve life expectancy, or even cure the disease under study? To answer these and similar questions, analysts must develop research designs that are appropriate for causal inference. Estimating a causal effect is challenging, yet it is essential to understand the impacts of a policy, medicine or any other kind of intervention.

What's included

4 videos3 readings4 quizzes

Over the next four lessons we'll begin to make sense of raw data. Staring at raw data, such as a spreadsheet, does not reveal much of anything about the key takeaway points. Consider a variable such as a survey question that asks about the level of discrimination in the U.S. (where the answer choices are "a lot," "some," "only a little," "none at all," and "don't know"). Reading the raw data does not tell you about the average respondent or the distribution of responses among the possible answer choices. To better understand the shape of the distribution, we can calculate measures of central tendency, measures of spread and characterize the data's dispersion. These summary statistics allow a researcher to draw some simple yet powerful initial conclusions about what the data tell us in a real-world sense.

What's included

4 videos5 readings4 quizzes

Edward Tufte, a world-renowned expert of data visualization, once said, "There is no such thing as information overload. There is only bad design." When communicating the results of an analysis, and particularly when trying to persuade an audience, a picture is truly worth a thousand words. A well-designed graph can leverage either a small or large amount of data to make a convincing argument. Data visualizations highlight specific points about the underlying information and enable the viewer to draw insights that are nearly invisible when staring at the numbers alone. In short, to be a good at communicating with data, you must become skilled at visualizing data.

What's included

3 videos4 readings4 quizzes

Instructor

Instructor ratings
4.6 (70 ratings)
Jennifer Bachner, PhD
Johns Hopkins University
5 Courses11,647 learners

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Recommended if you're interested in Probability and Statistics

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