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.
Ce cours fait partie de la Spécialisation Culture des données
Offert par
À propos de ce cours
An interest in learning how to make sense of data
Compétences que vous acquerrez
- Causal Inference
- Data Visualization (DataViz)
- Empirical Evidence
- Cross-Sectional Analysis
- Basic Descriptive Statistics
An interest in learning how to make sense of data
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.
Programme de cours : ce que vous apprendrez dans ce cours
Data and Theories
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.
The Causality Framework
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.
Descriptive Statistics
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.
Visualizations
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.
Avis
- 5 stars73,52Â %
- 4 stars17,64Â %
- 3 stars5,88Â %
- 2 stars0,98Â %
- 1 star1,96Â %
Meilleurs avis pour DATA – WHAT IT IS, WHAT WE CAN DO WITH IT
Good general overview of data description, different ways of reading/presenting data sets.
Some of the quiz questions feel a bit unfair. Answers that are "Accurate, but..." do not feel appropriate for these kinds of lessons.
An excellent introductory course for data understanding and analysis, I would advice to simplify the related readings to be more clear.
The course was streamed objectively. The reading site and materials covered were broad and fulfilling the objectives.
À propos du Spécialisation Culture des données
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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