Columbia University edX Course DS101X Statistical Thinking for Data Science and Analytics
In this first course of the Data Science for Executives Series, we will start with an introduction to data science. A team of experts with backgrounds in statistics, computer science, applied mathematics, finance, public health, medicine, engineering and journalism will help define the field of data science, and discuss its role in driving innovation across a multitude of domains. They will also talk about the importance of data visualization in data science approaches, and the type of skills data-scientists, as well as non data-scientists, need in a world that is literally exploding with data. You will also hear from the team where they think the field of data science and analytics is heading over the next few years.
Following the introductory module, the remainder of this course will provide the statistical foundation that underpins data science and analytics. Statistics plays a central role in the data science approach. In almost all data-driven solutions, data scientists exercise statistical thinking in designing data collection strategies, deriving insights from data visualization methods, obtaining supporting evidence for decision-making, and constructing models for predicting future trends.
In the four Statistical Thinking modules, we will discuss the data collection, analysis and inference processes, as well as the statistical methods used for association analysis, which are critical to understanding predictive analytics. We will then cover the principles and practices used in exploratory data analysis and visualization, drawing upon several real-world examples from the social sciences, health-care as well as sports. The final module will introduce the philosophy of Bayesian inference and Bayesian modeling techniques using illustrative case studies.