Data Sciences for Decision-Making

Data Sciences for Decision Making provides a foundation for learners to apply advanced analytics skills to complex data analysis and modes. Learners build an understanding of design, data analytics tools, and advanced analytics translators to communicate complex data-related requirements between data engineers, business, and IT stakeholders. Learners examine four major areas. First, there are momentum gains in the data and analytics revolution. Advances in data collection, machine learning, and computational power have fueled progress due to an exponential growth in the volume of data, continual advances in algorithms, and greater computational power and storage. Second, there are five elements of successful data analytics transformation: cases/sources of value, data ecosystems, modeling insights, workflow integration, and adoption. Third, the mapping value in data ecosystems includes data generation and collection, data aggregation, and data analysis. Fourth, models of distribution are fueled by big data analytics as business models are enabled by orthogonal data, hyper scale, real-time matching, radical personalization, massive data integration capabilities, data-driven discovery, and enhanced decision making.