This course is an introduction to probability and statistical concepts and their applications in solving real-world problems, as well as an introduction to coding in Python. This introductory course provides a solid background in the application of probability and statistics that will form the basis for advanced AI methods. Statistical concepts, probability theory, random and multivariate variables, data and sampling distributions, descriptive statistics, and hypothesis testing will be covered. In addition, the use of Python for the performance of basic statistics will be covered in this course. Covered topics include the numerical and graphical description of data, elements of probability, sampling distributions, probability distribution functions, estimation of population parameters, and hypothesis tests. This course will combine the learnings from texts, case studies, and standard organizational processes with practical problem-solving skills to present, structure, and plan the problem as it would be presented in large enterprises and execute the steps in a structured analytics process. Team collaboration, professional presenting, and academic writing will be covered as well through a final team project.