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Interdisciplinary Studies Master's with a concentration in Advanced Data Analytics
Master's of Science (M.S)
Program type:
Major
Format:
On Campus
Online
AOP
Est. time to complete:
2 years
Credit Hours:
30
Meets the challenge of turning data into action and prepare to enter a rapidly expanding
job market.
Expertise in data analytics is increasingly important for advancement in nearly every
career. The advanced data analytics concentration provides students with an understanding
of the 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. The core courses in the concentration emphasize applications of theory
and tools to solving real-world problems in business, industry and science.
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Request More InfoWhy earn a degree in Interdisciplinary Studies Master's with a concentration in Advanced Data Analytics?
The Interdisciplinary Studies Master’s with a concentration in advanced data analytics is a 30 graduate credit hour program across advanced data analytics and two other fields of study. Students work with an advisor to identify appropriate and relevant supporting fields, as well as to identify and complete an appropriate capstone experience.
The Advanced Data Analytics concentration is a focused interdisciplinary degree designed to provide students with the academic and practical preparation necessary to meet a growing research field and a rapidly expanding need of business and industry.
The program centers on developing deep analytic competencies in statistics and data analysis, coupled with a strong knowledge of contemporary data structures, to prepare students to conduct sophisticated analyses in business, industry, and science.
For additional information, please visit https://interdisciplinarystudies.unt.edu/concentrations/advanced-data-analytics.
Marketable Skills
- Work independently across two or more disciplines
- Teamwork
- Function across organizational silos through discipline integration
- Synthesize information and results
- Write in a professional and coherent manner
Interdisciplinary Studies Master's with a concentration in Advanced Data Analytics Highlights
Your academic counselor can help you connect with collaborative faculty in your area
of interest and make sure that your proposal fits the general guidelines of the degree
before you present your plan.
The program allows you to choose where and how to take you classes: in person, at
our Frisco or Denton locations, or 100% online.
Our online master’s in Interdisciplinary Studies is ranked a Top 25 Program in the
Nation by MyDegreeGuide.com.
Through UNT’s Interdisciplinary Studies program you will learn to sift through information
and produce a body of work that synthesizes multiple perspectives, arriving at a more
comprehensive understanding of the important issues.
Students completing the required Advanced Data Analytics (ADTA) courses (or relevant
substitutions approved by the ADTA advisor) may receive the graduate academic certificate
in data analytics (if all certificate requirements are met).
The interdisciplinary studies program offers students a high degree of flexibility
in designing a program of study that cuts across disciplinary boundaries.
What can you do with a Interdisciplinary Studies Master's with a concentration in Advanced Data Analytics?
Employers recognize the value of individuals who are open-minded, and who call upon a wide array of unconventional tools to solve intricate problems
Interdisciplinary Studies Master's with a concentration in Advanced Data Analytics Courses You Could Take
Harvesting, Storing and Retrieving Data (3 hrs)
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. Traditional survey and experimental design principles
for data collection as well as script-based programming techniques for large-scale
data harvesting from third party sources are covered. Data wrangling methodologies
are introduced for cleaning and merging datasets, storing data for later analysis
and constructing derived datasets. Various storage and process architectures are introduced
with a focus on how approaches depend on applications, data velocity and end-users.
Emphasizes applications and includes many hands-on projects.
Large Data Visualization (3 hrs)
Presents strategies 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 business questions, drive decisions and
provide persuasive evidence supported by data.
Discovery and Learning with Big Data (3 hrs)
Examines the latest methods for discovery and learning from large data sets. Emphasizes
applications of predictive and pattern recognition techniques in making business,
policy and allocation decisions. Topics complemented by hands-on projects using data
discovery and statistical learning software.
Data Analytics I (3 hrs)
Provides an overview of quantitative methods essential for analyzing data, with an
emphasis on business 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.
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