Because this is an emerging field, tools and technologies discussed will include suggestions for those new to data analysis or programming as well as those with advanced skill, with a common-sense approach. To demonstrate the high applicability and marketable nature of data science skills, examples related to benchmarking and code profiling as well as select market research questions will be given. Topic points will generally align with the structure of a data analysis project, from start to finish.

First, common sources of data and data types along with the technologies used to process them will be introduced. A brief review of the normal distribution curve with a simple explanation on how statistical estimates are derived will be provided. The talk will distinguish between predictive and descriptive statistics, as well as higher level data modeling including machine learning (e.g. classification, regression, and dimensionality reduction techniques). Finally, data visualization types and tools will be presented, with characteristics of poor visualizations or poor data strategy to be aware of in considering analysis from others or in your own work.