Data Management & SQL for Business Analysts
Data is a valuable corporate asset that is used to make informed business decisions. Companies are collecting larger volumes and varieties of data and storing it in different types of database management technologies. The skills to access and use these databases, as well as a knowledge of programming languages such as SQL are in high demand.
Data Management & SQL Professional Training Course Offerings
Upcoming schedule for instructor led, live, online courses.
- SQL for Business Analysts: Mar. 4, 6, 11, 13, 2025
Learn more and register
A Certificate of Completion is provided with each course showing number of contact hours (instruction), and field of study as "Information Technology" for continuing education purposes. Most courses provides 14 contact hours and are equivalent to 1.4 CEU's.
The term CEU is not a trademarked term; therefore, any educational institution may use it to describe their courses. Professions and industries usually regulate their approved continuing education within their bylaws and not one institute or accrediting body has become a standard to accept in this regard.
Professionals should always consult their Association or regulating body prior to embarking on continuing education and not assume a CEU will be accepted as part of their professional development.
Introduction to SQL for Business Analysts
This course will be online with a live instructor.
This short course is a hands-on introduction to using SQL for data analysis. It is intended for the business analyst who is familiar with general database concepts and wants to perform their own data discovery. We will explore how to find answers in the data, pitfalls to avoid, and how to take analysis to the next level.
Prerequisite
Familiarity with general database concepts is recommended (but not required) for those with no prior database experience.
- 4 online sessions
- Time commitment 12-16 hours
- Certificate of Completion.
- Required class for “Python for Data Sciencel” Certificate of Competency
Online Course Fees: See registration page
These trainings can also be customized and delivered at your location.
| Day One | Day Two | |
|---|---|---|
Database Fundamentals and Vocabulary Connecting to a Database Basic SQL Syntax Table Joining Exporting Data Aggregate Functions |
Ranking Functions Database Views Data Definition Language Table Creation Indexes and Performance Tuning |
Instructor
Jim Monahan is a Principal Consultant with Pinnacle Solutions Group where he focuses on data related business applications.He holds a BS in Electrical and Computer Engineering and an MBA from the University of Cincinnati.He brings experience in systems across a wide range of industries and technologies including eCommerce and Data Warehousing.
Testimonials
- Thank you for a great course. I learned a lot and your course was designed very well.
- I enjoy these classes and appreciate the chance to learn so much in a short period of time
- Great foundation for learning SQL.
- Instructor clarified if we were getting what we needed and adjusted materials and labs based on class feedback.
- Great pace, materials, labs! Loved having the time to work through them and ask questions, and having solutions to look back on for work.
Data Stewardship and Data Quality
Business stakeholders need to trust the predictions created by analytic solutions. Testing and validating the quality and performance of the predictions is often overlooked in analytics. Data stewardship & quality are key capabilities to enabling mass adoption of data, analytics, and AI throughout the organization. Improving the quality and context of data in the business increases the velocity and impact of analytics solutions to drive strategic value.
The Data Stewardship & Quality is a 4 half-day online training, with live instructors, that provides business professionals with the methods to improve context and quality from data capture through curation. The workshop (detailed below) focuses on defining standards for use cases from data capture, through the analytics itself, into monitoring performance in production. This course includes an overview of the responsibilities, methods, and best practices of data stewardship and quality to drive trust.
PREREQUISITE:
This workshop is for anyone who generates, captures, or interacts with data. There are no prerequisites for this course.
OBJECTIVES
- Understand the responsibilities and expectations of a data steward
- Enable self-service for datasets in the organization
- Define, measure and improve data quality for analytics solutions
- Understand data lineage within data pipelines
- Data quality definitions, calculations and improvements
- Understand use-case centric vs data-centric improvements
- Set thresholds for data quality to enable monitoring
- Monitor and troubleshoot data quality concerns with the organization
| Sessions 1 & 2 | Sessions 3 & 4 |
|---|---|
-Understand data stewardship and why the capability is important to enable AI initiatives -Common roles and responsibilities involved in data stewardship, quality and goverance -Providing context, structure, and quality for the data that the organization relies upon -Key components of an operational analytic/AI solution -Translation from traditional spreadsheet reports into the components of an analytic or AI solution -Communicating crucial dataset factors and conditions for the rest of the organization |
-Creating data walks and schemas to document the context of a dataset -Best practices for data schemas, business process analysis, and documentation of datasets -Key aspects of quality including availability, timeliness and accuracy -How to define the quality of a data set in technical and business terms -Importance of structuring, measuring and monitoring dataset quality -Practical methods to improve the quality of a dataset |
- 4 online sessions
- Time commitment 12-16 hours
- Certificate of Completion.
- Required class for “Python for Data Science” Certificate of Competency
These trainings can also be customized and delivered at your location.
Instructor
Kristy Wedel
Kristy Wedel is the Learning Experience Manager at AlignAI. She has over 15 years of experience in developing and democratizing analytics solutions that drive transparency and efficiency across various industries including healthcare, banking and government. Kristy is passionate about AI, governance and instructional design. She is also a member of the WIA team, a volunteer on the MORPC Regional Data Advisory Committee (RDAC) and a doctoral student at Franklin University.
Introduction to Data Management
Data is the foundation from which business analytics, data visualization, and business intelligence originates. This course focuses on learning the steps to get data into a well-designed database and teaches participants how to use Structure Query Language (SQL) to interact with data. The course will start with topics related to database design and data rules to provide participants with a sound knowledge of databases and relational data design. The class will finish with the coverage of practical examples to solve common business problems, efficient database structure design, and solutions on how to create interactions between analytical tools and a database.
In this short course you will learn:
- Database design and data rules.
- How to get data into a well designed database.
- The steps to use SQL to interact with data.
- Practical examples for solving common business problems, efficient database structure and design, and how to create interactions between analytical tools and a database.
This course will allow novice users and those with some experience the chance to learn skills, syntax, and techniques to perform analytics and data mining on datasets.
| Day One | Day Two | |
|---|---|---|
Data Modeling and Database Design · Conceptual Design · Logocal Design · Normalization · Physical Design · Extract-Transform-Load Processes |
Data Warehousing, Dimensional Modeling, and Big Data · Comparing Relational and Dimensional Modeling · Kimball's 10 Rules of Dimensional Design · Dimension Tables · Fact Tables · Extending Dimensional Models · Key Value Data Models and Big Data · Data Manipulation in Big Data Systems · Extending the ETL with Data Integration |
|
- 4 online sessions
- Time commitment 12-16 hours
- Certificate of Completion.
- Required class for “Python for Data Science” Certificate of Competency
These trainings can also be customized and delivered at your location.
Instructor
Andrew Harrison, PhD is Associate Dean, Graduate Programs and Associate Professor of Information in the Lindner College of Business. Andrew's research interests include consumer fraud, deception, security systems, privacy, media capabilities and virtual worlds.
Testimonials
- "I appreciate the time he took to answer questions and to make sure we understood."
- "While only part of the course is directly applicable to my current position, the concepts learned in this course will enable me to better communicate with DBA’s in the organization."
- "This course was a great opportunity to pull many concepts, tools and jargon together to understand how it all works. Very holistic and helpful."
- "Course covered exactly what I expected from the description. Adequately challenged me with information and pace."
- "The instructor had a ton of knowledge but broke it down into ways that were easy to understand."
For more information about these classes, or for custom training classes, please contact
