>>Dr. Antonio: Data science analytics or DSA
involves a systematic and interdisciplinary
approaches for transforming data into useful
information. Application domains for DSA are
virtually limitless, ranging from the pursuit
of basic scientific inquiry such as astrophysics
and climate modeling to managing financial
portfolios and assessing risk to process engineering
and optimization to analysis of text from
social media feeds such as twitter for insights
ranging from consumer purchasing trends and
interest to matters of national importance
in security including terrorism and cyber
attacks. Common to all these domains are opportunities
to derive or discover new knowledge and information
that is key to making better decisions.
Hello, I’m John Antonio, professor of Computer
Science and an Associate Dean for the College
of Engineering here at the University of Oklahoma.
The School of Computer Science and the School
of Industrial and Systems Engineering have
teamed together to offer a new and exciting
interdisciplinary Master of Science Degree
in Data Science and Analytics. This program
addresses the rapidly expanding need for more
professionals in this field. Graduates of
this program will have the skills necessary
to satisfy the growing demand for data scientists
and analysts in a wide variety of scientific,
industrial and government domains. Our course
offerings will be both completely online and
we will also cater to on-campus students with
in-person classes and experiences.
The online program’s delivered through OUs
own Janux platform, which connects learners
from around the world for an innovative social
learning environment. A fast track version
of the curriculum both online and on-campus.
It’s available to allow full time students
to complete the program in just fourteen months.
Students have a wide variety of courses to
choose from including, data mining, machine
learning, visual analytics, decision support
systems, intelligent analytics, meta-heuristics,
optimization, applied statistics, text analytics
and others.
The DSA curriculum is appropriate and accessible
for students with varied academic and professional
backgrounds. The curriculum comprises three
major components. First, analysis and systems
skills. Second, technology and computational
skills, and finally, application domain understanding.
Students admitted to the program generally
have some level of skill or background within
each of these three components. However, we
do not apply a cookie cutter approach when
recruiting students to our program. To the
contrary, we highly value and actively recruit
prospective students with diverse backgrounds.
>>Student 1: With my background in microbiology,
I wanted to pursue data science because bioinformatics
is a very important application to that and
so I just thought it would be helpful to continue
in the microbiology field.
>>Dr. Antonio: Students may enter the program
with good technology computational skills,
but have limited experience in applying these
skills in any particular application domain.
>>Student 1: For this I didn’t have any
programming experience and so just already
getting the prereqs and getting started into
the program, I already know so much more about
computer science and data science and I think
that it’s just a very versatile field so
it’s definitely going to help my career.
>>Student 2: It’s a fun program, cause I
mean it’s a trendy thing data science, but
underneath it all is really just computer
science and statistics. And if you like those
things, data science is a convenient way to
package both of them together.
>>Student 1: The versatility is what I’m
really drawn to. Big data is something that’s
really appearing more and more frequently
and so I think that just since it’s applicable
to any field. That’s what really draws me.
>>Dr. Antonio: Diversity in background, experiences,
skills, points of view, and approaches to
problem solving enriches the learning experience
for all students in the program.
A key signature feature of our program is
the opportunity for students to apply analytical
and computational skills to solving practical
problems arising in various application domains.
Specifically, the program requires each student
to work closely with an industry or government
partner solve a real world or a business problem
as part of their thesis or an industrial internship
requirement of the program.
We are indeed excited to bring this new and
innovative graduate program to you. By completing
this program, I am confident you will make
new and important contributions to your current
or future professional work environment. Best
of luck to you in discovering new data driven
approaches to solving problems of critical
importance.
