A new online learning option, providing a manageable pace towards a credential for those already balancing demanding work-life obligations!

Microcourses are stackable, 1-credit hour courses that focus on specific topics in data analytics. The 4-week format allows working professionals to refresh a skill or learn a new skill necessary for success in a current profession. The stackable credit is also a great step toward earning a certificate or a degree in data analytics. Available at both the undergraduate- and graduate-level.
Exploratory Data Analysis & Probability
ADTA 5130A
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.
 

Associated Programs

Sampling, Inference, and Hypothesis Testing
ADTA 5130B
Explores sampling methods 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 Chi-Square tests. Standard and open-source statistical packages are used to apply techniques to real-world problems.

Associated Programs

Correlation & Regression: Generating Solutions
ADTA 5130C
Provides an overview of simple and multivariate linear regression with hands-on application that focuses on business/industry applications designed to provide understanding of the relationships among variables and to help solve problems. 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.

Associated Programs

Principles of Storing and Harvesting Data
ADTA 5240A
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.

Associated Programs

Principles of Data Wrangling and Queries
ADTA 5240B
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.
Principles of Data Structures
ADTA 5240C
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.
Principles of Data Structures
IPAC 4240A
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.
Principles of Storing and Harvesting Data
IPAC 4240B
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.
Principles of Data Wrangling and Queries
IPAC 4240C

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.

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

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.

Associated Programs
Introduction to Supervised Machine Learning
IPAC 4340B
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.

Associated Programs

Introduction to Supervised Machine Learning
IPAC 4340B
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.

Associated Programs

Introduction to Unsupervised Machine Learning
IPAC 4340C
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.
Exploratory Data Analysis
IPAC 4130A
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.
Sampling Methods and Inferential Statistics
IPAC 4130B
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.
Regression Analysis
IPAC 4130C
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.