Learn critical concepts and practical methods to support research data planning, collection, storage and dissemination.
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
In this first week of the course, we look at finding data and reading different file types.
Graded: Week 1 Quiz
This week the primary goal is to introduce you to the most common data storage systems and the appropriate tools to extract data from web or from databases like MySQL.
Graded: Week 2 Quiz
This week the lectures will focus on organizing, merging and managing the data you have collected using the lectures from Weeks 1 and 2.
Graded: Week 3 Quiz
This week we finish up with lectures on text and date manipulation in R. In this final week we will also focus on peer grading of Course Projects.
Graded: Week 4 Quiz
Graded: Getting and Cleaning Data Course Project - Peer Review