DATA SOURCING // FOUNDATIONS OF DATA SCIENCE, PART 2

Data science can’t happen without data. That means the first task in any project is source – that is, to get – the raw materials that you will need. This short course discusses some of the more familiar methods of gathering data and some of the less familiar that are specific to data science. Data Sourcing is the second of five courses designed to outline the principles and practices of applied data science.

Course Information

  • Introductory level

  • 14 streaming video tutorials

  • 53 minutes of instruction

  • Created by Barton Poulson

  • Released 15 July 2016

Data science can't happen without the raw material of data. This course provides a concise overview of the range of methods available for gathering existing data and creating new data for data science projects.

Note. To navigate the videos, click on the menu icon (☰) at the top right of the video.

Table of Contents

INTRODUCTION

  • Welcome {0:51}

MEASUREMENT

  • Metrics {6:14}
  • Accuracy {3:52}
  • Social context of measurement {3:37}

GETTING DATA

  • Existing data {7:09}
  • APIs {6:24}
  • Scraping {5:20}

MAKING DATA

  • New data {2:16}
  • Interviews {2:53}
  • Surveys {3:23}
  • Card sorting {3:38}
  • Laboratory experiments {3:47}
  • A/B testing {3:00}

CONCLUSION

  • Next steps {0:35}