2021-2022 Catalog [ARCHIVED CATALOG]
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INFM 219 - Business Intelligence and Reporting
PREREQUISITES: INFM 109 - Informatics Fundamentals and DBMS 110 - Introduction to Data Analytics
PROGRAM: Informatics
CREDIT HOURS: 3
LECTURE HOURS: 3
DATE OF LAST REVISION: Spring, 2020
Introduces students to technology and techniques used to effectively consolidate, arrange, and analyze vast amounts of data. Business intelligence has become a highly sought after skill in many industries. Students will be introduced to business intelligence concepts and best practices to help organize data to make data-driven decisions. Emphasis is placed on learning how to mine large amounts of data using common business intelligence and decision support tools. Students will be introduced to data warehousing, business intelligence, and related topics such as decision support systems, data mining, web mining, and customer relationship management. Emphasis is placed on learning how to derive business value from large amounts of data.
MAJOR COURSE LEARNING OBJECTIVES: Upon successful completion of this course the student will be expected to:
- Identify key concepts and techniques related to business intelligence.
- Employ common business intelligence tools to extract, transform, and load data to aid in data-driven decision making.
- Analyze data to generate information for decision making.
- Discuss and explain the societal impacts and ethical dimensions of business intelligence and data analysis.
- Identify and examine data in order to ensure consistency and quality.
- Identify bad data and formatting for consistency following established data governance methodologies.
- Create reports utilizing common data visualization techniques to support business decisions.
- Explain the value of business intelligence and decision support systems and technologies.
- Explain how to use data diversity and transparency to improve inclusion, belonging, and equity of business decision making.
- Analyze structured and unstructured data.
COURSE CONTENT: Topical areas of study include -
Knowledge management
Relational databases
Big data and unstructured data
Report writing
Data governance
Data quality
Extract, Transform, Load (ETL) processes
Data Warehouse/Data Mart
Information/Data Security
Data visualization
Query selection criteria
Data maintenance
Decision making
Tools used for querying and reporting
Ethical use of data
Data diversity and transparency
Data cleansing
Data Mining
Course Addendum - Syllabus (Click to expand)
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