The book describes the theoretical principles of nonstatistical methods of data analysis but without going deep into complex mathematics. The emphasis is laid on presentation of solved examples of real data either from authors' laboratories or from open literature. The examples cover wide range of applications such as quality assurance and quality control, critical analysis of experimental data, comparison of data samples from various sources, robust linear and nonlinear regression as well as various tasks from financial analysis. The examples are useful primarily for chemical engineers including analytical/quality laboratories in industry, designers of chemical and biological processes.Features: Exclusive title on Mathematical Gnostics with multidisciplinary applications, and specific focus on chemical engineering. Clarifies the role of data space metrics including the right way of aggregation of uncertain data. Brings a new look on the data probability, information, entropy and thermodynamics of data uncertainty. Enables design of probability distributions for all real data samples including smaller ones. Includes data for examples with solutions with exercises in R or Python. The book is aimed for Senior Undergraduate Students, Researchers, and Professionals in Chemical/Process Engineering, Engineering Physics, Stats, Mathematics, Materials, Geotechnical, Civil Engineering, Mining, Sales, Marketing and Service, and Finance.