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.
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.
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.
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.
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.
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.
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.
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
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
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
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.
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.
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.
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.
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.
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.