Introduction to Data Science

Data science is the study of extrapolating insights out of data and information. It leverages tools and techniques from different academic fields such as mathematics, computer science, information science, and domain knowledge to analyze data and create data-driven observations, hypotheses, and conclusions. These hypotheses, or models, attempt to represent the underlying laws that govern the patterns we see. Using data science, we can now attempt to understand the data at a deeper level and even attempt to identify root causes that created the observed and collected data. Training and continuously updating the data models with different observations fine-tunes these models and can help to better approximate our understanding of the recorded phenomena. Thus, models can consequently be used to classify, infer, and predict new data points that were not part of the original training data. Since data is often representative of sensitive, private, or confidential information, its accumulation, processing, and insights are sometimes private as well. As such, data scientists are usually asked to adhere to various privacy regulations that restrict and regulate data science methodologies. This chapter provides a basic introduction to data science, data privacy concerns, and an overview of privacy-preserving techniques that attempt to address these concerns.