5
Classes
15
Credit Hours
$5.8K
Estimated TX Resident Tuition & Fees
Jun 2
Next Start Date

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Training in data analytics is becoming increasingly important for advancement in nearly any career. The University of North Texas is offering a career-enhancing undergraduate certificate in data analytics.

Courses making up the certificate provide an understanding of the underlying fundamental concepts of contemporary data analytics methods, as well as experience in obtaining, wrangling and learning from big data through machine learning and deep learning tools. Courses emphasize applications of theory and tools to solving real-world business problems.

This undergraduate certificate provides 15 hours of undergraduate course credit that can be applied to a university degree, such as the UNT Bachelor of Applied Arts and Sciences Degree.

Why choose the Undergraduate Certificate in Data Analytics?

  • Taught by the same quality graduate faculty who also developed the course.
  • Can be completed quickly with accelerated 8-week course options available.
  • Certificate courses that can be used as electives towards an undergraduate degree.
  • Curriculum is project-based emphasizing real-world applications.
  • The program is available 100% online including optional weekly opportunities to meet virtually with your instructors.
  • Demand greatly outpaces supply for data analytics professionals creating opportunities in virtually all industries where even entry-level positions can garner high salaries.
  • Access to University student resources including the Career Center, Learning Center, and Writing Center.

Apply for the Undergraduate Certificate in Data Analytics

courses

ADTA 4130  Data Analytics and Computational Statistics 1

Provides an overview of quantitative methods essential for analyzing data, with an emphasis on business applications. Topics include identification of appropriate metrics and measurement methods, descriptive and inferential statistics, experimental design, parametric and non-parametric tests, simulation, and linear and logistic regression, categorical data analysis, and select unsupervised learning techniques. Standard and open source statistical packages are used to apply techniques to real-world problems.

ADTA 4230 Data Analytics and Computational Statistics 2

Contemporary techniques of multivariate analysis, including association rules, classification methods, time series, text analysis, and machine learning methods with an emphasis on applications in science and industry. Introduction to state-of-practice computational statistical and data analysis methods and tools.

ADTA 4240 Principles of Data Structures, Harvesting and Wrangling

Provides an introduction to collecting, storing, managing, retrieving, and processing datasets. Techniques for large and small datasets are considered, as both are needed in data science applications. Emphasizes applications and includes many hands-on projects.

ADTA 4340 Methods for Discovery and Learning from Data

This course covers the latest methods for discovery and learning from large data sets. Topics complemented by hands-on projects using data discovery and statistical learning software.

ADTA 4250 - Principles of Data Visualization for Large Data

This course covers the latest methods for discovery and learning from large data sets. Topics complemented by hands-on projects using data discovery and statistical learning software.