Questions? Read our FAQs

Introduction to Data Science


Aditya Mukerjee
Hacker-in-Residence, Quotidian Ventures

Questions? Read our FAQs

About This Class

We live in a world with seemingly infinite data, and if you can learn the right balance of computer science, statistics, and information theory, there are lucrative opportunities available to you. This class is designed to give newcomers some clarity into how data scientists add value to any organization. Discover case studies about how data science is uniquely positioned to solve major problems for a variety of companies. By the time you finish this class, you’ll have a better idea of what the data science field is, and more importantly, how you can help yourself get what the Harvard Business Review calls the “sexiest job of the 21st century”.


  • Introduction 1:24
  • What is Data Science? 2:40
  • Tools of the Trade 6:54
  • Why Use Bayesian Techniques? 10:36
  • Limitations of Averages 5:04
  • Inference 2:59
  • Bias and Consistency 11:54
  • Visualizations 5:40
  • Further Resources 3:49
  • Q: Examples of projects you worked on? 2:48
  • Q: What should you look for in a data science hire? 4:34
  • Q: What's workflow like? 4:12
  • Q: What makes a good AB test? 3:00
  • Q: Can SQL analyze large datasets? 1:16
  • Q: Are there open source projects to get experience with R? 1:29
  • Q: At what point should you bring on a data scientist? 2:35


  • Introduction to a standard workflow for anyone conducting data analysis.
    • Gain a basic understanding of Bayesian scientific reasoning.
    • Acquire experience and sample R code from a specific data related case study.
    • Discover the tools and resources to help you find answers to questions that may come up in future analyses.

About the Instructor(s)


Aditya Mukerjee
Quotidian Ventures

Aditya Mukerjee is the Hacker-in-Residence at Quotidian Ventures, a seed to early-stage investment firm in New York. He is a graduate of Columbia University with degrees in Computer Science and Economics-Statistics, as well as a HackNY fellow and mentor. While on the server engineering team at Foursquare he worked on their explore recommendation engine, and as a data scientist at OkCupid, he provided the data work behind OkTrends posts such as “The Real ‘Stuff White People Like’” and “The Big Lies People Tell in Online Dating”.

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