There are 2 graduate courses next semester I want to take for fun or skills for future projects. If you had to pick one, which one would you choose and why?
Class descriptions:
Big data Analytics
Current issues in storage, retrieval, and analysis of large volumes of data (Big Data), in order to support business decisions. Big Data are stored in a variety of formats, including Web log, Internet clickstream data, as well as unstructured data, such as industry reports and customer comments. Big Data analytics utilize data sources that may be left untapped by conventional business intelligence solutions. Topics include conventional data warehousing, retrieval of large data sets that are stored across clustered systems, natural language processing, topic extraction in textual data, machine learning and artificial intelligence, and predictive analytics for unstructured data. A semester project in Big Data Analytics relevant to a functional area of business is an important component of the course.
Predictive Modeling
This course covers major topics used in developing predictive modeling and applied statistical forecasting models that are of major interest to the business, government, and academia. These include exploring the calibration of models, the estimation of seasonal indices, and the selection of variables to generate operational business forecasts. Topics in this course can assist business professionals in utilizing historical patterns to build a more constructive view of their future. The course also examines how these topics can be used in combination with data capture, integration and information deployment capabilities, to ensure more productive decisions and more accurate planning. Modern forecasting techniques for the evaluation of sophisticated business models used to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, facilities management and strategic planning, are covered.
Class descriptions:
Big data Analytics
Current issues in storage, retrieval, and analysis of large volumes of data (Big Data), in order to support business decisions. Big Data are stored in a variety of formats, including Web log, Internet clickstream data, as well as unstructured data, such as industry reports and customer comments. Big Data analytics utilize data sources that may be left untapped by conventional business intelligence solutions. Topics include conventional data warehousing, retrieval of large data sets that are stored across clustered systems, natural language processing, topic extraction in textual data, machine learning and artificial intelligence, and predictive analytics for unstructured data. A semester project in Big Data Analytics relevant to a functional area of business is an important component of the course.
Predictive Modeling
This course covers major topics used in developing predictive modeling and applied statistical forecasting models that are of major interest to the business, government, and academia. These include exploring the calibration of models, the estimation of seasonal indices, and the selection of variables to generate operational business forecasts. Topics in this course can assist business professionals in utilizing historical patterns to build a more constructive view of their future. The course also examines how these topics can be used in combination with data capture, integration and information deployment capabilities, to ensure more productive decisions and more accurate planning. Modern forecasting techniques for the evaluation of sophisticated business models used to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, facilities management and strategic planning, are covered.
if you had to choose..big data or pred. modeling?