The Shidler College of Business’ School of Accountancy offers its Data Analytics Training Program in October of 2020. The four-day training is open to both professionals and students.
American Accounting Association, American Institute of Certified Public Accountants (AICPA), and Local and the Big Four accounting firms have all emphasized the importance of utilizing data analytics and have provided input and guidance regarding Shidler's data analytics programs.
Shidler students who take the class for credit, (Acc 649 for graduate students and ACC 399 for undergraduate students), must pay the relevant university tuition and fees and are not required to pay the registration fee. To receive credit, students are required to attend all sessions and must obtain a score of at least 70% in the test (or project) conducted (assigned) at the end of the class. Students who fail the test (or complete the project) or do not attend all sessions, will be assigned "no credit" grade.
Data Analytics Sessions
October 17–18 and October 24–25, 2020
1:00 p.m. – 5:00 p.m.
Instructor: Eduard O. Merc, Ph.D., MBA/MSIS, CompTIA Certified
Adjunct Business Instructor, Information Technology Management (ITM), Shidler College of Business.
Overview: Participants will gain an overview of data analytics techniques and an introduction to data visualization. This is a “hands on” class, a sixteen-hour program.
Required Course Materials
- Participants must bring their own laptops.
- Participants must have Excel and Tableau Prep and Desktop Software installed (Free student versions are available online at: https://www.tableau.com/academic/students)
A Certificate of Completion (for those who will attend all sessions) and Continuing Professional Education (CPEs) will be issued to participants.
Deadline for registration is September 15, 2020.
October 17th, 2020 -
Topics – General Data Analytics Overview and Tableau Introduction
- Overview of Business Intelligence, Analytics, and Data Science
- Nature of Data, Statistical Modeling and Visualization (Descriptive Analytics I)
- Business Intelligence and Data Warehousing (Descriptive Analytics II)
- Data Mining Process and Methods (Predictive Analytics)
- Optimization and Simulation in Business (Prescriptive Analytics)
- Types of Source Data Files
- Data Analysis Process and Applications
- Overview of Different Data Analytic Tools
- Software Introduction and Getting Started with Tableau Pre/Desktop Tools
October 18th, 2020 –
Topics – Tableau Prep and Desktop: Business Intelligence Tool for Data Visualization
- Tableau Interface, Distributing and Publishing
- Tableau Prep Builder:
- Interface and Getting Started
- Types of Steps
- Building a Flow
- Adding Data to the Flow
- Tableau Prep Conductor
- Connecting to Data:
- Relationships, Metadata, and Extracts
- Data from the Web. Text, PDFs, and Excel Files
- Data Blending and Connecting to Cubes
- Tableau Desktop:
- Interface and Getting Started
- Common Views in Desktop workspace
- Interactive Dashboards
- Flow of Analysis in Desktop
- Course Project Description
October 24th, 2020 –
Topics – Tableau Desktop: Visual Analytics (Basic Level)
- Getting Started with Visual Analytics:
- Hierarchies, Sorting, and Grouping
- Creating and Working with Sets
- Filtering, Parameters, and Formatting
- Dashboard and Stories:
- Building a Dashboard
- Dashboard Formatting
- Dashboard Interactivity and Story Points
- Statistical Techniques to Analyze Your Data
- Class Project/Workshop Time and Q&A
October 25th, 2020 –
Topics – Tableau Desktop: Visual Analytics (Advanced Level)
- Complex Calculations for Additional Data Insight
- Advanced Chart Types and Dashboarding Techniques
- Best Practices in Analytics
- Other Applications of Data Analysis in Business
- Class Project/Workshop Time (Completion) and Q&A
- Final Review/Closing Comments
About the Instructors:
Eduard O. Merc is originally from Slovakia, Eastern Europe. He has been an adjunct instructor at the Information Technology Department (ITM) at Shidler College (UH Mānoa Campus) for the past three years.
Dr. Merc has many years of experience in business analysis, database management systems as well as business technology deployments in corporate and nonprofit environments. He also teaches undergraduate and graduate courses in business intelligence, analytics, and data science.
His research passion is in visual analytics and its role to improve learner outcomes among nontraditional adult students, such as the military student population.