Stochastic Analysis has been a key discipline in modern applied mathematics because of its numerous practical applications. With the emergence of large data sets, often times not limited to numerical databases, data science considerations have blended with the traditional approaches, from the valuation of derivatives to modern portfolio theory, a wide variety of topics are based upon concepts such as Stochastic Integration, Stochastic Differential Equations, and Ito calculus. The course establishes basics in Probability Theory and Stochastic Processes and moves on to models build exclusively on data inputs, through a set of lectures that mix theoretic results and practical applications.