Data ' What It Is, What We Can Do With It
- Offered byCoursera
- Public/Government Institute
Data ' What It Is, What We Can Do With It at Coursera Overview
Duration | 11 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Data ' What It Is, What We Can Do With It at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 5 in the Data Literacy Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level An interest in learning how to make sense of data
- Approx. 11 hours to complete
- English Subtitles: English
Data ' What It Is, What We Can Do With It at Coursera Course 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
Data ' What It Is, What We Can Do With It at Coursera Curriculum
Data and Theories
Welcome Video
Statistical Inference
Components of Scientific Research
Scientific Theories
Big Data is Not About the Data!
We're All Social Scientists Now
Theories in Scientific Research
Final Quiz
The Causality Framework
Causal Effects and the Counterfactual
Randomized Controlled Trials
Observational Studies: Overview
Observational Studies: Strategies for Estimating Causal Effects
Causal Inference Based on Counterfactuals
A Simplified Guide to Randomized Controlled Trials
Difference-in-Difference Estimation
Practice Problem
Practice Problems
Practice Problems
Final Quiz
Descriptive Statistics
Why do we need descriptive statistics?
Measures of Central Tendency
Measures of Spread
Dispersion
Descriptive Statistics: Introduction
Measures of the Center of the Data
Measures of the Location of the Data
Measures of the Spread of the Data
Skewness and the Mean, Median and Mode
Practice Problems
Practice Problems
Practice Problems
Final Quiz
Visualizations
Elements of Good Visualizations
Bar Plots, Histograms and Box Plots
Scatter Plots, Line Graphs and Side-by-Side Bar Graphs
Publication, Publication
A Complete Guide To Bar Charts
Comparing Box Plots and Histograms
A Complete Guide to Scatter Plots
Practice Problems
Practice Problems
Practice Problems
Final Quiz
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