Program Overview

Data Analytics Certificate

Program Type

• Certificate
• Graduate Academic Certificate

Format

• On Campus
• Online

Estimated Time to Complete

• 2 semesters

Credit Hours

• 15

Program Description

The Data Analytics Certificate provides students with an understanding of fundamental concepts of contemporary statistical and data analytics methods, as well as experience in obtaining, wrangling, and learning from data. Certificate courses emphasize applications of methods for solving problems in science, business, and industry with real-world data and case studies. This certificate helps prepare graduates to be highly competitive in the employment marketplace.

Why Earn a Data Analytics Certificate?

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.

Data Analytics Certificate Highlights

• Can be completed in as few as seven months if students take two 8-week courses at a time.
• Credit courses that can be used as electives toward an undergraduate degree.
• The curriculum is largely project-based as opposed to high-stakes tests.
• The program is 100% online, including optional weekly opportunities to meet virtually with instructors. Recordings are also made available to students for subsequent review.
• 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.

Data Analytics Certificate Courses

• Data Analytics and Computational Statistics 1 (3 hrs)
  • Provides an overview of quantitative methods essential for analyzing data, with an emphasis on science and industry 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.
• Data Analytics and Computational Statistics 2 (3 hrs)
  • 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.
• Principles of Data Structures, Harvesting, and Wrangling (3 hrs)
  • Introduction to collecting, wrangling, storing, managing, retrieving, and processing datasets.
  • Topics include fundamental concepts and techniques of data engineering, large-scale data harvesting, data wrangling methodologies, and storage and process architectures.
  • Emphasizes applications and includes many hands-on projects.
• Principles of Data Visualization for Large Data (3 hrs)
  • Principles and methods for effective visualization and communication of large data sets.
  • Standard and open-source data visualization packages are used to develop presentations that convey findings, answer science and industry questions, drive decisions, and provide persuasive evidence supported by data.
• Methods for Discovery and Learning from Data (3 hrs)
  • Introduction to contemporary methods for discovery and learning from data sets.
  • Emphasizes applications of predictive and pattern recognition techniques in deriving insights and making decisions in business and science contexts.
  • Topics complemented by hands-on projects using data discovery and statistical learning software.