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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 

  • Five phases of the Systems Development Life Cycle (SDLC) 
  • Six phases of the Database Life Cycle (DBLC) 
  • Top-down vs. bottom-up design approaches 
  • Centralized vs. decentralized conceptual database design 

Business Intelligence and Data Warehouses 

  • Understanding business requirements 
  • Role of business intelligence in decision support 
  • Architecture and benefits of business intelligence 
  • Operational data vs. decision support data 
  • Data warehouse components and design (star and snowflake schemas) 

Data Analytics and Data Mining 

  • Functions and roles of data analytics and data mining 
  • Tools and techniques for data analysis 

Database Administration and Security 

  • Impact of data quality on business assets 
  • Supporting decision-making with databases 
  • Managerial and technical roles of database administrators 
  • Information security frameworks and database security goals 

Introduction to Business Analytics 

  • Decision-making in business analytics 
  • Categorization of analytical methods: descriptive, predictive, prescriptive 
  • Big Data, the Cloud, and AI: Volume, Velocity, Variety, Veracity 
  • Business analytics applications: accounting, finance, HR, marketing, healthcare, supply chain, government, sports, web 
  • Legal and ethical issues in data and analytics 

Descriptive Statistics 

  • Types of data: population, sample, quantitative, categorical, cross-sectional, time series 
  • Exploring data in Excel: sorting, filtering, conditional formatting 
  • Creating distributions: frequency distributions, histograms, cumulative distributions 
  • Measures of location and variability: mean, median, mode, range, variance, standard deviation 
  • Analyzing distributions: percentiles, quartiles, z-scores, identifying outliers, boxplots 
  • Measures of association: scatter charts, covariance, correlation coefficient 

Data Visualization and Wrangling 

Overview of data visualization principles 

  • Table and chart design principles 
  • Specialized data visualizations: heat maps, treemaps, waterfall charts, stock charts, parallel-coordinates chart 
  • Visualizing geospatial data: choropleth maps, cartograms 
  • Data dashboards: principles, applications 
  • Data visualization in Tableau or Power BI

Data discovery: accessing and understanding raw data 

  • Data structuring: formatting, arrangement, splitting, combining fields 
  • Data cleaning: handling missing data, identifying erroneous values and duplicates 
  • Data enriching: subsetting, supplementing, enhancing data 
  • Data validating and publishing 

 

 

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: 

1. Core Database Concepts 

  •    Describe the importance of databases and database design 
  •    Identify and construct entity-relationship models 
  •    Explain normalization and its significance in database design 
  •    Perform SQL queries for data retrieval and manipulation 

2. Database Design and Life Cycle 

  •    Design databases using the SDLC and DBLC frameworks 
  •    Differentiate between top-down and bottom-up design approaches 
  •    Contrast centralized and decentralized conceptual database design 

3. Business Intelligence and Data Warehousing 

  •    Understand the role of business intelligence in decision support 
  •    Identify the components and characteristics of a data warehouse 
  •    Develop star and snowflake schemas for data warehousing 
  •    Explain the roles of data analytics and data mining in business 

4. Database Administration and Security 

  •    Assess the impact of data quality on business competitiveness 
  •    Explain the roles and responsibilities of database administrators 
  •    Implement information security measures to protect database integrity 
  •    Utilize standards, strategies, and tools for effective database administration 

5. Data Integration and Analytics 

  • Integrate data to meet business requirements 
  • Apply data visualization techniques to communicate insights 
  • Use ETL tools for data integration and analysis 
  • Develop reports and dashboards for business decision-making 

6. Introduction to Business Analytics 

  •  Understand decision-making processes in business analytics 
  •  Differentiate between descriptive, predictive, and prescriptive analytics 
  •  Apply big data concepts and utilize cloud and AI technologies 
  • Address legal and ethical issues in data and analytics 

7. Descriptive Statistics 

  •  Identify and work with different types of data 
  •  Explore and analyze data using Excel
  •  Calculate measures of location and variability 
  •  Analyze distributions and measure associations between variables 

8. Data Visualization and Wrangling 

  •    Design effective tables and charts 
  •    Create specialized data visualizations and geospatial data representations 
  •    Develop and apply data dashboards 
  •    Use Tableau/Power BI for data visualization 
  •    Manage and clean data for analysis 
  •    Structure, format, and enrich data 
  •    Validate and publish cleaned data 

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.

Information Technology Continuing Education Certificate:

A certificate of completion for the class will be issued to participants with at least 80% attendance, class participation, and completion of hands-on practice and projects.

Instruction Method:

Lecture with demonstration, discussion, hands-on exercises, problem-solving, and outside assignments.

Evaluation:

Participation and completion of skill practices are expected. Class attendance is required for successful completion.

Prerequisites

Materials: This class does not include course materials. Students are required to purchase the necessary materials directly from the bookstore or our designated partner. Please ensure you have all the required materials before the first day of class.

Resources:

  • Participants are required to have a laptop or desktop computer with a minimum 4GB ram, 256GB HD, Core i5. Recommended 8GB ram, 256GB SSD, Core i5. 
  • It is required that you are able to download programming resources to your laptop/desktop for this class. 
  • Access to reliable internet as well as a camera, a headset, and preferably dual monitors. 

Prerequisite and desired knowledge:

To succeed in a data business analytics class, it would be beneficial for students to have some foundational knowledge and skills that can help them grasp the course material more effectively. Here’s a refined list of what would be beneficial to know before starting the class:

1. Basic Mathematics and Statistics:

  • Descriptive Statistics: Understanding of mean, median, mode, variance, and standard deviation.
  • Basic Probability: Familiarity with probability concepts and common distributions (e.g., normal distribution).

2. Basic Computer Literacy:

  • Excel Skills: Proficiency in using Microsoft Excel or Google Sheets for basic data manipulation, creating charts, and using functions.
  • General Computer Skills: Comfort with navigating software applications, managing files, and using the internet for research.

3. Logical and Analytical Thinking:

  • Problem-Solving Skills: Ability to approach problems methodically and think critically.
  • Analytical Reasoning: Capability to analyze information, identify patterns, and make logical conclusions.

4. Business Acumen:

  • Basic Business Concepts: Understanding of fundamental business principles such as finance, marketing, and operations.
  • Decision-Making Processes: Insight into how data is used to support business decisions.

5. Basic Understanding of Data:

  • Data Types: Awareness of different data types (e.g., qualitative vs. quantitative, structured vs. unstructured).
  • Data Collection Methods: Familiarity with basic data collection techniques (e.g., surveys, experiments).

Recommendations

IMPORTANT: Make sure to have your Network Login, DUO authentication, and student email before the first day of class.
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