Foundations of Data Science: a 5-Part SEries
This collection of five courses provides a gentle introduction to the field of data science from a conceptual, non-technical level.
Data Science: An Introduction / Foundations of Data Science, Part 1
Data science sits at the intersection of statistics, computer programming, and domain expertise. This non-technical overview introduces the basic elements of data science and how it is relevant to work in the real world.
Data Sourcing / Foundations of Data Science, Part 2
Data science can’t happen without data. That means the first task in any project is to 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.
Coding / Foundations of Data Science, Part 3
Data science professionals rely on a range of tools, from basic spreadsheets to advanced languages like R and Python. In these videos, we’ll cover the basic elements of the most important tools for data science.
Mathematics / Foundations of Data Science, Part 4
Data science relies on several important aspects of mathematics. In this course, you’ll learn what forms of mathematics are most useful for data science, and see some worked examples of how math can solve important data science problems.
Statistics / Foundations of Data Science, Part 5
Statistics is distinct from - but critical to - data science. In this non-technical, conceptual overview, you can learn how statistical practices such as data exploration, estimation, and feature selection give data science its power and insight.