Business Analysis & Analytics   

This course introduces statistical techniques to evaluate quality and analyze processes and products. The course covers fundamentals of statistics, descriptive statistics, probability, binomial probability distribution, normal probability distribution, inferential statistics, and linear regression analysis. Students will be able to practice mathematical formulae as they relate to statistical methods as well as apply Excel to solve statistical problems. The course will develop skills of statistical analysis and decision-making. No Textbook Required.

Software Required: MS Excel

Students examine the fundamental processes, roles and responsibilities of the business analyst. The course curriculum is consistent with version 3 of the Business Analysis Body of Knowledge (BABOK) from the IIBA International Institute of Business Analysis. Students explore the use of tools and techniques used by business analysts within the broader context of organizations and their projects. They review methodologies used to equip the business analyst to better perform as a communication link between all business stakeholders and help them deliver on their key role in project and organizational success. The role of the business analyst as an agent of change is also highlighted. Students learn to plan, structure, and control the requirements elicitation and communication process, in order to maximize success and optimize results. These techniques can be utilized on a wide variety of activities, initiatives and projects and are an important component for success. Additionally studies include the effective use of requirements and life cycle management techniques. Through the combination of lectures, discussions and exercises, the course provides insights into the interaction between the business analyst and his or her surrounding environment and presents methodologies to identify, analyze and resolve business problems. Textbook required. NOTE: This course has mandatory chats and mandatory group work.

The student will be introduced to the computerization of statistical process control functions such as histograms, control charts and data collection. This course uses spreadsheets, word processing and other quality assurance related software to demonstrate how computers can be used to effectively manage a quality system. Prerequisite: Working knowledge of Windows, Microsoft Word and Excel or equivalent. No textbook required. Software Requirements: Word processing and Spreadsheet

Students are introduced to different scripting language tools such as SQL, NOSQL, Apache, Java and Python that support data analysis on large volumes of data. They also analyze the strengths and limitations of current tools used today. Students review and recommend which tools best support data analysis, data quality, problem solving, analysis, analytics and business decision-making for different functions and industries.

NOTE: This course has mandatory chats. No Textbook Required

This course provides and introduction to quality assurance and control programs within a business organization. Topics include the nature and history of quality, factors which affect quality, quality inspection and verification. The course will also deal with collection, and analysis and interpretation of data, histograms and frequency distributions, probability and its applications, normal and binomial curves, control charts for variables and attributes and statistical sampling. Prerequisite: Level 4 Mathematics or equivalent. Textbook required.

Students are introduced to data analysis principles, practices, and approaches used in research, Big Data, data science, and artificial intelligence. They will analyze the algorithms and statistical models used to support analytics and business decision-making for different industries and functions. Textbook required.

Computer Requirements:Hardware; Intel I7 or AMD A10 processor or better with chipset that must support virtualization, 16 GB of RAM, 1 TB hard drive, Ethernet Network Card, Wireless Network Card, One USB 3.0 port (two preferred) Software; Windows 10 Professional Edition

Students are introduced to best practices, approaches, and tools for managing and delivering analytics, predictive analyses, ETL, and data projects. They will assess approaches around collaboration, estimation, scoping, planning, data cleaning, data migration, data quality, and risk mitigation. Students will recommend how to best communicate their assessments to business stakeholders. Textbook required.