IEEE Potentials, November/December 2020: Big Data and Data Science

Data is more than a technical resource in this November/December 2020 issue of IEEE Potentials: it is the starting point for questions about engineering, business, healthcare, and personal privacy. The issue’s theme, Big Data and Data Science, brings together accessible discussions of the systems that handle data and the methods used to find meaning in it.

From raw data to practical insight

Feature articles explore data’s growing influence, the role of sound data architecture, and the development of data science platforms. The issue also considers big-data applications in wireless sensor networks and the use of machine-learning algorithms to predict gestational diabetes. Together, these subjects show how data work moves from infrastructure and analysis into real-world applications.

Machine learning, in practice

Readers will also find articles on machine-learning pipelines—from research to production—and on working with time-series data. These topics bring attention to the practical steps and different forms of data involved in building data-driven systems, rather than treating data science as a single tool or technique.

Engineering with responsibility

The opening editorial places the technical theme alongside concerns about privacy and ethical data use. That perspective gives the issue a useful human dimension: data systems are engineered, but their collection and application affect people and organizations.

For students and early-career technologists

IEEE Potentials is aimed at undergraduate and graduate students and young professionals. This themed issue will interest readers building familiarity with big data, data science, analytics, and machine learning, as well as those looking for approachable connections between technical ideas and their applications.

Explore a snapshot of data-focused engineering topics in this issue of IEEE Potentials.