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

8,685 already enrolled

Gain insight into a topic and learn the fundamentals.
4.6

(156 reviews)

Beginner level

Recommended experience

11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.6

(156 reviews)

Beginner level

Recommended experience

11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Data Literacy Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

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 assignment1 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 assignments

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 assignments

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 assignments

Instructor

Instructor ratings
4.6 (72 ratings)
Jennifer Bachner, PhD
Johns Hopkins University
5 Courses13,112 learners

Offered by

Recommended if you're interested in Probability and Statistics

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 156

4.6

156 reviews

  • 5 stars

    71.87%

  • 4 stars

    21.25%

  • 3 stars

    4.37%

  • 2 stars

    1.25%

  • 1 star

    1.25%

NA
5

Reviewed on Apr 4, 2021

RK
4

Reviewed on Jan 18, 2022

HK
5

Reviewed on Jul 9, 2023

New to Probability and Statistics? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions