Offering Real-World Exposure
Each class presents the opportunity
to analyze complex datasets and
formulate and solve real-world problems
to facilitate data-driven decisions.
Throughout the program, students learn
how to use common algorithms such
as association and sequence rules
discovery, memory-based reasoning,
clustering, classification and regression
decision trees, logistic models, and
neural network models. Furthermore,
students gain hands-on experience with
R, Python, and SQL for data mining,
analytics, and data science. Students also
learn to use innovative technologies—
such as MapReduce, Hadoop, Hive HQL,
Spark, Storm, Kafka, TensorFlow, H2O,
and others—to ensure they remain at the
leading-edge of data science and can
address the most challenging questions
facing the world today.
Preparing Students for Industry
Transformation
The University of Chicago recognizes
that the field of analytics is changing
so quickly that it can be difficult for
programs to transform their curriculum
to educate students on the state-of-art
technologies and on the ways these
technologies could be used to solve
practical problems in impactful ways.
As a result, companies are having
difficulty in finding qualified analytics
professionals who can lead analytics
initiatives from day one, without a long
training process. The Master of Science
in Analytics program at the University
of Chicago prides itself in being at
the forefront of teaching the state-of-
art analytical tools with rigor, so that
students learn to use analytics creatively
and flexibly, with the ability to apply
analytics in solving practical problems
effectively.
Delivering Insightful Leaders to the
Industry
The University of Chicago faculty and
alumni contribute to society as scholarly
educators, scientists, political leaders,
medical professionals, business and
community leaders, entrepreneurs,
and more. Alumni and faculty, lecturers
and postdocs go on to become Nobel
laureates, CEOs, university presidents,
attorney general, literary giants, and
astronauts. The MScA program strives
to produce alumni who understand the
business, know a broad set of analytical
tools, and can manage cross-functional
teams.Specialization is Imperative
The program views analytics a dynamic
field that requires analytics professionals
to have a strong foundation in data
science as well as expertise in specific,
functional areas of a business (e.g.,
marketing, finance, or AI). Professionals
with an in-depth knowledge of a
particular business will enjoy lucrative
career prospects; so, specialization
in certain types of analytics will be
necessary for many individuals to
succeed.Future Industry Insights
Commenting on the future of Big Data
and AI industry, program instructors
highlighted the below key trends to drive
the growth:
· Use of a framework or platform to
deliver production quality machine
learning applications, which streamlines
the analytics workflow, starting with data
ingestion and ending with production
deployment.
· Use of methodologies for transparency
into analytic models via explainability
and interpretability, which are key
to the adoption of machine learning
applications in business.17
THE 10 MOST PROMINENT ANALYTICS AND
DATA SCIENCE INSTITUTES IN 2019