BAS-DAT7711 - SQL Business Analytics

Course Description

Understanding the design and manipulation of relational databases is a fundamental step in the field of data management and analytics. This comprehensive course covers core database concepts, database design and lifecycle, business intelligence, data warehousing, database administration and security, and advanced business analytics techniques. Students will learn to identify relational database operators, manipulate table contents using SQL, integrate data to meet business requirements and apply data analytics to support decision-making. The course also emphasizes the importance of data quality, security, and the role of business intelligence in organizational success. 

Course Outline

Introduction to Database Systems 

• Overview of database systems 
• Database management systems (DBMS) 

Relational Database Concepts 

• Fundamentals of relational databases 
• Tables, rows, columns, and keys 

Entity Relationships and Normalization 

• Entity-relationship diagrams (ERDs) 
• Principles of normalization (1NF, 2NF, 3NF) 

SQL Querying Basics 

• Writing SELECT statements 
• Aggregation functions (COUNT, SUM, AVG, MAX, MIN) 

Advanced SQL Querying 

• Joining tables (INNER JOIN, LEFT JOIN, RIGHT JOIN) 
• Writing subqueries 

SQL Sets and Functions 

• Using set operators (UNION, INTERSECT, EXCEPT) 
• Utilizing built-in SQL functions 

Data Manipulation Language (DML) 

• Inserting, updating, and deleting data 

Database Design and Life Cycle: 

• Apply the phases of the Systems Development Life Cycle (SDLC) and Database Life Cycle (DBLC) to design effective databases. 
• Compare design strategies (top-down vs. bottom-up) and conceptual models (centralized vs. decentralized). 

Business Intelligence and Data Warehousing: 

• Define business intelligence (BI) and its role in supporting decision-making through operational and analytical data. 
• Design data warehouse structures (star and snowflake schemas) to meet business requirements. 

Data Analytics and Data Mining: 

• Explain key functions of data analytics and mining in extracting business insights. 
• Identify common tools and techniques used for data analysis. 

Database Administration and Security: 

• Describe the roles of database administrators in ensuring data quality, security, and decision support. 
• Apply security frameworks to protect data assets and maintain database integrity. 

Introduction to Business Analytics: 

• Differentiate between descriptive, predictive, and prescriptive analytics and their business applications. 
• Recognize the impact of big data, cloud computing, and AI, while considering legal and ethical issues. 

Descriptive Statistics: 

• Classify data types and analyze distributions using Excel (e.g., frequency tables, histograms, measures of variability). 
• Explore relationships between variables through measures of association like correlation and scatterplots. 

Data Visualization and Wrangling: 

• Apply design principles to create effective visualizations and dashboards using tools like Tableau and Power BI. 
• Clean, structure, and enrich datasets to prepare them for analysis and presentation. 

Learner Outcomes

This course is designed to provide a solid foundation in data business analytics, equipping students with the essential knowledge and skills to excel in data-driven roles. By the end of this course, students will be able to: 

• Develop Proficiency in SQL for Business Analytics: Write and optimize SQL queries to retrieve, manipulate, and analyze business data, supporting decision-making and insights. 
• Understand Core Database Concepts: Explain the purpose of databases, design entity-relationship models, apply normalization, and structure data effectively to support SQL operations. 
• Design and Manage Databases: Apply database development life cycles (SDLC, DBLC), compare design approaches, and understand key concepts in database administration, security, and data quality. 
• Apply Business Intelligence and Data Warehousing Principles: Use SQL within the context of business intelligence, design data warehouse schemas, and understand how data mining and analytics drive business value. 
• Integrate and Visualize Data for Business Insights: Combine datasets using SQL and ETL processes, and create reports and dashboards to communicate insights using tools like Tableau, Power BI, and Excel. 
• Explore Business Analytics Concepts: Understand decision-making processes, distinguish between types of analytics (descriptive, predictive, prescriptive), and gain awareness of big data, AI, and ethical considerations. 

Notes

Target Audience:

This class is intended for data business professionals who want to understand the different types of data analysis, tools, and reports available to leverage data for better decision-making. The class may cater to a diverse range of backgrounds and experience levels, offering foundational knowledge and more advanced concepts to accommodate different learners.

Occupational Outlook:

The U.S. Bureau of Labor Statistics (BLS) reported that employment of data workers is projected to grow 36 percent from 2021 to 2031, much faster than the average for all occupations. About 13,500 openings for data workers are projected each year, on average, over the decade. The recommended education level is a Bachelor's degree or higher.

Industry Certification: 

While not required, students completing this course may be prepared for: 

• Microsoft Certified: Azure Data Fundamentals (DP-900) 
• Oracle Database SQL Certification 

These certifications validate SQL knowledge and improve employability. 

Information Technology Continuing Education Certificate: 

A Certificate of Completion will be awarded to participants who successfully meet the following requirements: 

• Maintain a minimum of 80% attendance, with the understanding that documented, meaningful progress in Cengage MindTap may be considered as an equivalent substitute for limited absences, at the instructor’s discretion. 
• Actively participate in class discussions, group work, and problem-solving activities. 
• Complete all required hands-on labs, skill practices, and course projects to an acceptable standard. 
• Demonstrate consistent progress in Cengage MindTap to support skill development and mastery of course objectives. 

Instruction Method: 

This course follows a blended learning model, combining instructor-led lectures, demonstrations, discussions, hands-on exercises, and problem-solving activities. To support individual learning needs, the course incorporates Cengage MindTap, an interactive, self-paced platform designed to help students catch up or accelerate their learning. 

To maximize success, students are strongly encouraged to dedicate at least 5 hours per week to self-guided training and practice in Cengage MindTap, in addition to scheduled class time. 

Evaluation: 

Student progress will be evaluated through: 

• Active participation in all class activities and discussions. 
• Completion of assigned skill practices, hands-on labs, and projects. 
• Regular, measurable progress in Cengage MindTap. 
• Consistent attendance and engagement in both in-person and self-paced learning components. 

Recommendations

IMPORTANT: Make sure to have your network login, Microsoft authenticator, and student email before the first day of class.

Testimonials

"Taking the SQL class significantly improved my ability to work with databases, making data analysis faster and more efficient. I gained hands-on experience writing queries, joining tables, and organizing data in meaningful ways. I’d recommend it to anyone looking to boost their data skills—it’s a practical, powerful tool in any analyst’s toolkit."
— Lisa Major (Spring 2025)

Accrediting Associations

• Computing Technology Industry Association