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Data Science: Inference and Modeling

Provided by Harvard University (HarvardX)
Introductory
See prerequisites
1–2 hours
per week, for 8 weeks
Free

$49 USD for graded exams and assignments, plus a certificate

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

Before you start

Data Science: Probability or a basic knowledge of probability theory.

Choose your pace

Self-Paced courses contain assignments without due dates. You can progress at your own speed.

Steady Learners
80% complete in less than 13 weeks
Accelerated Learners
51% complete in less than 5 weeks
Course opens: Jul 16, 2019
Course ends: Jan 3, 2020

What you will learn

  • The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
  • How to use models to aggregate data from different sources
  • The very basics of Bayesian statistics and predictive modeling

Overview

Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.

This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast.

Once you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modeling. Finally, at the end of the course, we will put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.

Meet your instructors

Rafael Irizarry
Professor of Biostatistics
Harvard University

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This course is part of:

Earn a Professional Certificate in 2-4 months if courses are taken one at a time.

View the program
  1. 8–16 hours of effort

    Build a foundation in R and learn how to wrangle, analyze, and visualize data.

  2. 8–16 hours of effort

    Learn basic data visualization principles and how to apply them using ggplot2.

  3. 8–16 hours of effort

    Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

  4. Data Science: Inference and Modeling
  5. 8–16 hours of effort

    Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

  6. 8–16 hours of effort

    Learn to process and convert raw data into formats needed for analysis.

  7. 8–16 hours of effort

    Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

  8. 16–32 hours of effort

    Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

  9. 30–40 hours of effort

    Show what you’ve learned from the Professional Certificate Program in Data Science.

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