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Data Science: Capstone

Provided by Harvard University (HarvardX)
Introductory
15–20 hours
per week, for 2 weeks
Free

$99 USD for a certificate of completion

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

Before you start

  • No prerequisites.
Course opens: Jul 16, 2019
Course ends: Jan 17, 2020

What you will learn

  • How to apply the knowledge base and skills learned throughout the series to a real-world problem
  • How to independently work on a data analysis project

Overview

To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Unlike the rest of our Professional Certificate Program in Data Science, in this course, you will receive much less guidance from the instructors. When you complete the project you will have a data product to show off to potential employers or educational programs, a strong indicator of your expertise in the field of data science.

Meet your instructors

Rafael Irizarry
Professor of Biostatistics
Harvard University

Frequently asked questions

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View Courses
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. 8–16 hours of effort

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

  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. Data Science: Capstone

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