![]() ![]() Coverage includes various options to lower operational costs and improve responsiveness to customers' needs, including operating system design, product & service design, capacity analysis & buffering, waiting line optimization, and process quality analysis using statistical approaches. This course will provide students with the analytical tools to analyze, manage, and improve manufacturing, service, and business processes. ![]() The outcome of this course will be a focused survey of Python and SQL topics designed to equip analytics professionals rather than a deep focus on technical programming topics. This course introduces both languages to equip students pursuing an analytics education with the skills necessary to succeed in the analytics and data visualization field. Even with advances in database technologies and languages for handling heterogeneous data types, SQL remains the core skill for interacting with data. Employers have indicated that knowledge of SQL is one of the most important skills for new graduates entering the workforce. Structured Query Language (SQL) is the most common language globally for interacting with relational databases. This course introduces students to the Python environment and teaches a solid foundation in the basic syntax and structure. One of the most popular programming languages, its use has steadily increased across a large number of industries. Prerequisite: PY100 (Intro to Python) Python is a modern, high-level programming language. DATA MINING FOR BUSINESS ANALYTICS SOFTWARER, SQL, and Power BI software are used in this course. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. DATA MINING FOR BUSINESS ANALYTICS HOW TOStudents will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). Prereq: AD100 Pre-Analytics Laboratory and ADR100 Introduction to R This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. ![]()
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