Data Analytics Undergraduate Certificate

Undergraduate Certificate
in Data Analytics

Total Classes

Total Credit Hours

$5.8k

Estimated Tx Resident Tuition & Fees

Start Dates Per Year

Available Data Analytic
Microcourses

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

IPAC 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.

IPAC 4130 | Data Analytics and Computational Statistics 1 Microcourses

IPAC 4130A
Exploratory Data Analysis

This course introduces quantitative methods essential for analyzing data, with an emphasis on business and industry applications. Topics include the exploratory data analysis framework, descriptive statistics, data visualization, and basic probability models needed for statistical analysis. Standard and open-source statistical packages are used to apply techniques to real-world problems.

IPAC 4130B
Sampling Methods and Inferential Statistics

This course introduces essential sampling and inferential statistics concepts for analyzing and deriving insights from data, with an emphasis on business and industry applications. Topics include sampling methods and distributions, parameter estimation, interval estimation, hypothesis testing, and analysis of variance. Standard and open-source statistical packages are used to apply techniques to real-world problems.

IPAC 4130C
Regression Analysis

This course provides an overview of simple and multivariate linear regression, with an emphasis on business and industry applications. Topics include correlation, simple and multivariate linear regression, and categorical data analysis. Standard and open-source statistical packages are used to apply techniques to real-world problems.

IPAC 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.

IPAC 4230 - Data Analytics and Computational Statistics 2 Microcourses

IPAC 4230A
Coming Soon

Microcourse name and description will be available soon.

IPAC 4230B
Coming Soon

Microcourse name and description will be available soon.

IPAC 4230C
Coming Soon

Microcourse name and description will be available soon.

IPAC 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.

IPAC 4240 - Principles of Data Structures, Harvesting and Wrangling Microcourses

IPAC 4240A
Principles of Data Structures

This course introduces the fundamentals of data engineering, including data types, data scaling, structuring data, and an overview of the tools used in modern data management. Concepts are developed within the context of real-world data analytics applications, including how to approach messy and unstructured data.

IPAC 4240B
Principles of Storing & Harvesting Data

This course provides theoretical knowledge and practical experience for storing and harvesting data using cloud technologies. Concepts include comparing different types of storage technologies, constructing cloud storage, examining cloud storage classes, and batching and streaming data. Exercises and examples will consist of real-world case studies exploring how to store and harvest large datasets.

IPAC 4240C
Principles of Data Wrangling and Queries

This course develops methods for retrieving and processing large datasets. Through a variety of cloud-based and enterprise technologies, students will develop the skills to query, wrangle, and analyze data. Projects employed in this course will include complex datasets from real-world scenarios.

IPAC 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.

IPAC 4340 - Methods for Discovery and Learning from Data Microcourses

IPAC 4340A
Introduction to Big Data, AI, and Machine Learning

This course introduces the fundamentals of data analytics and machine learning with big data. The goal of this course is to provide students with the fundamentals of big data analytics and machine learning. As these fundamentals are introduced, problems are being considered in the context of big data analytics. Exercises and examples will have clean and structured to dirty and unstructured data.

IPAC 4340B
Introduction to Supervised Machine Learning

This course introduces the fundamentals of supervised machine learning. The goal of this course is to provide students with both theoretical knowledge and practical experience leading to mastery of big data analytics and supervised machine learning, using both small and large datasets. As these fundamentals are introduced, exemplary technologies will illustrate how supervised machine learning can be applied to real-world solutions. The problems are being considered in the context of big data analytics. Exercises and examples will consider both simple and complex data structures and data ranging from clean and structured to dirty and unstructured.

IPAC 4340C
Introduction to Unsupervised Machine Learning

This course introduces the fundamentals of unsupervised machine learning. The goal of this course is to provide students with both theoretical knowledge and practical experience leading to mastery of big data analytics and unsupervised machine learning, using both small and large datasets. As these fundamentals are introduced, exemplary technologies will illustrate how unsupervised machine learning can be applied to real-world solutions. The problems are being considered in the context of big data analytics. Exercises and examples will consider both simple and complex data structures and data ranging from clean and structured to dirty and unstructured.

IPAC 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.

IPAC 4250 - Principles of Data Visualization for Large Data Microcourses

IPAC 4250A
Coming Soon

Micro course name and description will be available soon.

IPAC 4250B
Coming Soon

Micro course name and description will be available soon.

IPAC 4250C
Coming Soon

Micro course name and description will be available soon.

UNT Data Analytics

Search form