Data Analysis for Social Scientists (edX)

Data Analysis for Social Scientists (edX)
Free Course
Categories
Effort
Certification
Languages
No prior preparation in probability and statistics is required, but familiarity with algebra and calculus is assumed.
Misc

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Data Analysis for Social Scientists (edX)
Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest. This statistics and data analysis course will introduce you to the essential notions of probability and statistics.

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We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively.




What you'll learn:

- Intuition behind probability and statistical analysis

- How to summarize and describe data

- A basic understanding of various methods of evaluating social programs

- How to present results in a compelling and truthful way

-Skills and tools for using R for data analysis


Course Syllabus


Data Analysis for Social Scientists

Week One: Introduction

Week Two: Fundamentals of Probability, Random Variables, Joint Distributions and Collecting Data

Week Three: Describing Data, Joint and Conditional Distributions of Random Variables

Week Four: Functions and Moments of a Random Variables & Intro to Regressions

Week Five: Special Distributions, the Sample Mean, the Central Limit Theorem

Week Six: Assessing and Deriving Estimators - Confidence Intervals, and Hypothesis Testing

Week Seven: Causality, Analyzing Randomized Experiments, & Nonparametric Regression

Week Eight: Single and Multivariate Linear Models

Week Nine: Practical Issues in Running Regressions, and Omitted Variable Bias

Week Ten: Endogeneity, Instrumental Variables, and Experimental Design

Week Eleven: Intro to Machine Learning and Data Visualization

Optional: Writing an Empirical Paper



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Average: 3 ( 2 votes )

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Free Course
No prior preparation in probability and statistics is required, but familiarity with algebra and calculus is assumed.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.