Data Analytics Program - Continuing Education

Data Analytics Program

This non-academic program introduces participants to universal data analysis techniques, tools, and processes used to facilitate cross-functional and informed decision-making.

Participants will learn to safeguard data quality and integrity, produce descriptive statistics, visualize data, and conduct predictive and inferential analyses. Participants will gain skills in using Microsoft Excel to clean, create, visualize, and analyze data.

The Data Analytics Program includes 15 hours of classroom instruction and instructor-led practice, along with a capstone project where participants will integrate and apply the data analysis skills learned.

Details:

Evaluate the usefulness of different statistical techniques and their real-world application
Learn to use predictive and inferential tools to improve decision-making
Identify methods for ensuring data quality and their impact on decision-making
Describe various forecasting techniques and their benefits and limitations
Identify the fundamental concepts of descriptive statistics (populations and samples, measures of central tendency, measures of variability, measures of distribution), and their real-world application
Explain data management techniques including transforming data, recoding data, and handling missing data

This course is designed for learners interested in real-world tools and techniques to capture meaningful data and transform it into valuable information for their organizations. This is especially suited for managers, decision-makers, and professionals taking on data analysis or business intelligence roles in their organization.

Note: PEWD program participants should be 18 years or older.

This program requires a basic understanding of Microsoft Excel.

Dr. Eduardo Millet

Dr. Eduardo Millet is passionate about strategy, innovation, new venture creation, and social entrepreneurship. He has taught at The State University of New York at New Paltz, Southeast Missouri State University, and The University of Texas Rio Grande Valley. Dr. Millet obtained a PhD in Business Administration at the University of Texas Rio Grande Valley, and holds a master’s in Urban and Regional Planning and a B.A in Economics from the University of Minnesota. Dr. Millet has a balanced background of industry and academic experience. He worked at the McAllen Chamber of Commerce where he developed an innovative approach to entrepreneurial development. Originally from Mexico, Dr. Millet moved to McAllen, Texas, where he served as the Director of Institutional Development for the State Government of Yucatan during five years, and as Director of Business Intelligence for a nationwide telecommunications company for three years.

Note: If you are applying for the Continuing Education Private Loan to cover the registration fee for a non-credit continuing education program, then please use the UTRGV Continuing Education School Code: 003599. The loan amount you note in the application cannot exceed the open enrollment fee noted on our website. Books will NOT be included in the loan amount. The minimum for the loans must be $500.00. Some service charges may apply.

*The only loan provider PEWD accepts is Sallie Mae*

Please email pewd@utrgv.edu once you have been approved so we may certify your loan.

Private Loans

Submit a required written request via email three business days before the program start date. Weekends are not considered. Partial refunds are not allowed.

Testimonial:

The Data Analytics course through UTRGV Continuing Ed. came at a perfect time. Post Covid I was working with millions of lines of data at Workforce Solutions due to record breaking Unemployment Insurance claims. Professor Millet taught me much easier methods to sort through large quantities of data that made the work I was doing at the time much easier and much more efficient. The format of the class worked out great with my work schedule. I continue to use the tools I learned in my new position as Performance Analyst at Workforce Solutions.

- Aaron Gonzalez, Performance Analyst

Session Details:

In this session, participants will discuss the significance of data analytics in making informed decisions and explore the capstone/case study exercise. Participants will gain general knowledge of data analytics concepts, data types, and data context.

Learning Outcomes:
• Describe the information value chain
• Discuss the importance of data context and relevance to business processes
• Define common data types
• Define basic statistical terms

In this session, participants will learn how to utilize tools such as Microsoft Excel for data collection, processing, and analysis. Participants will learn and practice how to clean databases and how to conduct meaningful sorting and filtering to facilitate informed decision-making.

Learning Outcomes:
• Describe different decision-making models and tools
• Describe the process steps of data analytics and the tools used in each step
• Describe methods to ensure the quality of data
• Explain data management techniques including transforming data, recoding data, and handling missing data

In this session, participants will learn how to use pivot tables and charts to visualize and present data for decision-making. Participants will learn how to use Microsoft Excel to conduct descriptive statistics, to summarize data, and create histograms for frequency analysis.

Learning Outcomes:
• Identify the fundamental concepts of descriptive statistics and their real-world applications
• Describe how statistics are used in different settings
• Apply fundamental statistics to real-world scenarios
• Use statistics for decision making

In this session, participants will learn how to use data analytics tools to conduct forecasts for predictive and inferential analysis. Participants will learn how to use Microsoft Excel to conduct statistical analyses such as correlation coefficients to determine relationships between variables and simple linear regression to determine expected changes in an observed variable.

At the end of this session, participants will receive a capstone project assignment to be completed by the next class date.

Learning Outcomes:
• Evaluate the usefulness of different predictive and inferential statistical techniques
• Explain the fundamental concepts of predictive and inferential statistics and their real-world application
• Describe various forecasting techniques and types of regression analysis
• Explain the advantages and disadvantages of various predictive and inferential statistical techniques

In this closing session, participants will explore how organizations use measures, metrics, and indicators to prioritize performance goals. Various techniques to create Key Performance Indicators (KPI’s) will be presented.

Participants will review and share lessons learned during the program and through their capstone project assignment (to be submitted at the start of this class).

Learning Outcomes:
• Define measures, metrics, and indicators
• Describe the purpose and use of Key Performance Indicators (KPI’s)
• Determine the best ways to communicate information with specific audiences

Edinburg Course Information

Registration Deadline:
October 21, 2024

Dates:
October 23 - November 20, 2024

Days/Times:
Wednesdays: 6 p.m - 9 p.m

Program Modality:
In-Person

Location:
Edinburg CESS Building

Instructor:
Dr. Eduardo Millet

Cost:
$ 599

Refund Deadline:
October 17, 2024

Notes:

Students requiring accommodation should contact our office pewd@utrgv.edu to learn about the process.

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