Introduction to Data Science

The Data Science course delivers the fundamentals of data sets analysis arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. The content of this course introduces theories and practices of data science concepts based on mathematical and statistical concepts. This course offers a multitude of topics relevant to the analysis of complex data sets accompanying programming and code algorithms in R that underpinning data science. This course is ideal for students and practitioners without a strong background in data science. The students will also learn analyses of foundational theoretical subjects, including the history of data science, matrix algebra, and random vectors, and multivariate analysis; a comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity; introductions to the R programming languages, including basic data types and sample manipulations; an exploration of algorithms, including how to write one and how to perform an asymptotic analysis; and, a comprehensive discussion of several techniques for analyzing and predicting complex data sets. Towards the end of the class, students will develop a case study by gathering data to apply and practice the learned concepts in a large-scale project.