This introductory course focuses on petroleum data types (such as field data, lab data, and simulated data) and data analytics tools available today in the context of these data types. During 2 days of the course participants will learn about AI algorithms that have been successfully applied to a variety of petroleum engineering problems, machine learning models and their hybridization with fuzzy logic, and some information theory techniques that you can adopt for your engineering, characterization or decision-making problems. This two-day course provides broad and yet sufficiently detailed introduction into petroleum data analytics and puts all tools in the context of data that we routinely deal with in petroleum engineering. The course covers several Artificial Intelligence algorithms (evolutionary programming and swarm intelligence), Machine Learning techniques (various types of neural networks, SVM), introduction to Fuzzy Logic and its uses in combination with ML techniques, and information theory approaches (such as Akaike Information Criterion). All algorithms are presented from the conceptual standpoint and illustrated with multiple examples from the petroleum E&P industry.