# Introduction to Probability

DSA ADS Course - 2021

Probability Theory, Applied Probability, Probabilistic Causation, Causal Inference

Understanding applied probability and accurate assessment of real-world risk is a critical part of high value data science.

Applied data scientists require high-level understanding of probability theory and information theory. Understanding uncertainty and complexity requires combining simpler parts yet understanding both holistic dynamics in addition to sub-set property dynamics of the whole. This is extremely difficult and takes both deep knowledge of theory and many years of real-world practice to master.

Probability theory provides the glue whereby the parts are combined, ensuring that the system as a whole is consistent, and providing ways humans can model highly-interacting sets of variables as well as a data structure that lends itself naturally to the design of efficient general-purpose algorithms.