TEXT MINING AND GENERATION FOR BUSINESS ANALYTICS

GENBUS 207

Enables you to understand and apply useful approaches to analyzing and presenting data to support business decision making. Emphasis on applications of predictive and prescriptive analytics. Predictive approaches use historical data to infer relationships and forecast future outcomes. Prescriptive methods formulate decision models to identify choices that are optimal with respect to a desired, measurable outcome. Provides experience integrating diverse data sources, modelling uncertainty, and visualizing key insights.

GENBUS 760

Focuses on various technologies needed to perform data analytics. Techniques of extracting structured and unstructured data from databases, applications, or social networks. Transform and combine data with other relevant information and load into targeted systems. How to use programming languages to collect data from the web and leverage libraries for other, more, advanced data analysis.

GENBUS 840

Covers emerging and current topics in the industry through applied learning experiences - case studies, industry meetings, and exercises or workshops.

GENBUS 657

An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.

INFOSYS 723

An introduction to text mining and natural language processing for business applications. Provides an overview of text data and steps to make it usable and approaches for making text data useful in descriptive and predictive analytics applications. Topics include representation approaches, topic modeling, and an overview of key applications of natural language processing, such as chatbots and recommender systems.