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Data Analytics Certificate
Program type:
Certification
Graduate Academic Certificate
Format:
On Campus
Online
Est. time to complete:
2 semesters
Credit Hours:
15
Enhance industry and scientific decision making with actionable insights from data.
The Data Analytics Certificate provides students 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.
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Request More InfoWhy 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.
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.
Data Analytics Certificate Highlights
This certificate 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 with a focus on doing rather than
high-stakes tests.
The program is 100% online including optional weekly opportunities to meet virtually
with your 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 You Could Take
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.
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