The aggressive rate of data growth has outpaced our ability to manually understand what data represents. Data is typically stored in database and files, and represented in different formats (structured, semi-structured, or no structure). Data analytics is the science of applying quantitative techniques to analyze data with the objective of discovering hidden knowledge and identifying interesting patterns. This course surveys a number of data preprocessing and sampling methods, data distributions and uncertainty, statistics, regression, time-series analysis, predictions and clustering. It introduces the characteristics and analytic challenges on dealing with clinical data from electronic health records. The course also covers emerging trends in Data Analytics and the applications of information technology in the healthcare. Statistical analyses and data mining techniques will be discussed along with methods for deploying these techniques using the open source tools.