Masters of Science in Professional Studies: Data Analytics

Data Chart
 

Professional Studies: Data Analytics
Master of Science Degree

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Program Contact
Dr. Sanjay Modak, Department Head
Offered within the
Department of Graduate Programs and Research


Accredited by the UAE Ministry of Education

This program prepares participants for successful careers helping organizations make better decisions through analytics. It prepares participants to work effectively with complex, real-world data and to create value from it. The program is based on a holistic educational experience, where theory and practice are fully interlaced through the continuous support of public and private sector partners. This program will be the only program in the UAE ensuring the graduates are highly sought-after Data Analysts within the industry.

Overview

This program prepares participants for successful careers helping organizations make better decisions through analytics. It prepares participants to work effectively with complex, real-world data and to create value from it. The program is based on a holistic educational experience, where theory and practice are fully interlaced through the continuous support of public and private sectors partners. This program will be the only program in the UAE ensuring the graduates are highly sought-after Data Analysts within the industry.

The Data Analytics program is designed to be suitable for working individuals. The program gives participants the ability to help organizations increase the efficiency of their operations and gather and interpret data more efficiently. While pursuing their master's, students will also be coached to implement what they learn at workplace yielding more future opportunities. The program is carefully designed to cater for all levels of data analytics: from creating a strategy and vision of the organization towards big data, managing to secure the data, building appropriate infrastructure for data analytics, data analysis and presentation for intelligent decision making.

How data collection and analysis is used for emerging technologies including artificial intelligence, virtual realities, robotics, machine learning and other fields is included in the program. The program is jointly designed with public departments such as Smart Dubai as well as international leading organizations in the field.

 

Typical Job Titles

Data Scientist Data Engineer
Data Architect Machine Learning Engineer
Data Analyst Database Administrator
Statistician Business Analyst

Mission Statement

  1. To develop an understanding of current concepts and methodologies in data analytics that can be effectively utilized by both individuals and organizations to analyze and make decisions from data.
  2. To equip students with the necessary tools - both theoretical and practical - of data analytics in order to derive value from data.

Program Goals

  • Provide a comprehensive understanding of the tools needed to sort, mine, visualize and analyze data.
  • Enable students with the ability to apply these tools to a wide variety of data to deliver a high-quality body of research.

Program Learning Outcomes

  • Evaluate business problems for the purposes of analytical investigation for decision-making.
  • Construct comprehensive analytical models.
  • Compare data collection and preparation techniques in the process of model building for data analysis.
  • Apply data analysis models and methods to large scale data and for emerging technologies.
  • Identify and analyze risks related to data analytics.
  • Demonstrate competencies in innovation, entrepreneurship and sustainability.
  • Execute a data analytics project that involves substantial research with findings and recommendations.

Curriculum

Typical Course Sequence

Total Credit Hours - 129

Course Sem. Cr. Hrs.
PROF-705
Context and Trends
The gateway course for students enrolled in the MS in professional studies degree program. Course provides students with opportunities to interact about controversial issues while discovering foundational knowledge about interdisciplinary history, theory, along with applied problem-solving, research methods and professional ethics. Students use this course as a means of designing and receiving approval for individualized plans of study. (Department permission required). Students should consult their adviser before registering.
 3
PROF-740
Fundamentals of Data Analytics
This course introduces students to foundational skills in data analytics, with a focus on mathematical foundations. Students will explore topics that form the backbone of modern data analytics such as machine learning, data mining, artificial intelligence and visualization. Tools for statistics will be introduced to students for how to go from raw data to a deeper understanding of the patterns and structures within the data, to support making predictions and decision making.
 3
PROF-741
Enterprise Infrastructure for Data Analytics
This course introduces students to the challenges in large and small organizations related to data analysis and storage. Students will be introduced to economic infrastructure approaches for handling data securely. Platforms which are hosted both on-premises of organizations and in the cloud will be covered in this course.
 3
PROF-770
Proposal Seminar
This course guides the student through preparation of the Capstone Proposal that is required for the applied final course of his/her MS in professional studies degree - the Capstone Project. Student will determine a Capstone Project concept, and articulate the methods for implementing the Capstone Project. The course concludes with a paper describing the Capstone Project, including background and description, methodology, anticipated outcomes, and probable Capstone Adviser. Student will meet regularly with the course facilitator. Upon successful completion of this course, student will be registered for the Capstone Project. (Pre-requisites: PROF-705 and core coursework; course restricted to MS in professional studies students)
 0
PROF-776
Research and Thesis
The research and thesis course unfolds over two semesters. Students will receive instruction in standard research methods, as well as the best practices for writing and presenting project findings. The remainder of the course is devoted to students working individually with a faculty supervisor on the research, organization, analysis, and eventual presentation of their project findings. To complete the thesis course, students must successfully defend their work before a committee consisting of a representative sample of the program faculty, the student’s supervisor, and the program director. The committee is responsible for determining the student’s final grade for the thesis. The course is graded on a pass/fail (R, U, or I) basis with a successful defense of the completed thesis being the deciding factor. Department approval required for enrollment.
 6
PROF-790
Data Analytics for Emerging Technologies
This course explores the emerging technologies that are driving the acceleration of applications and the data produced by them Big Data and its 5V characteristics – volume, velocity, veracity, variety and value –across industry, research and academia. Students will be introduced to a range of complemented technology disciplines like cybersecurity, virtual content delivery, artificial intelligence, and smart cities where the uses of real-time analysis on big datasets are applied. Particular focus will be paid to a review of a number of industry verticals and data related to how emerging technologies are used with an emphasis on privacy and ethical considerations.
 3
PROF-799
Special Topics
Special topics are experimental courses announced as offered. Variable credit.
 3
ISTE-600
Foundations of Data Mining
This course provides students with exposure to foundational data mining techniques. Topics include analytical thinking techniques and methods, data/exploring data, classification algorithms, association rule mining, cluster analysis and anomaly detection. Students will work individually and in groups on assignments and case study analyses.
 3
ISTE-780
Data Driven Knowledge Discovery
Rapidly expanding collections of data from all areas of society are becoming available in digital form. Computer-based methods are available to facilitate discovering new information and knowledge that is embedded in these collections of data. This course provides students with an introduction to the use of these data analytic methods, with a focus on statistical learning models, within the context of the data-driven knowledge discovery process. Topics include motivations for data-driven discovery, sources of discoverable knowledge (e.g., data, text, the web, maps), data selection and retrieval, data transformation, computer-based methods for data-driven discovery, and interpretation of results. Emphasis is placed on the application of knowledge discovery methods to specific domains.
 3
ISTE-782
Visual Analytics
This course introduces students to Visual Analytics, or the science of analytical reasoning facilitated by interactive visual interfaces. Course lectures, reading assignments, and practical lab experiences will cover a mix of theoretical and technical Visual Analytics topics. Topics include analytical reasoning, human cognition and perception of visual information, visual representation and interaction technologies, data representation and transformation, production, presentation, and dissemination of analytic process results, and Visual Analytic case studies and applications. Furthermore, students will learn relevant Visual Analytics research trends such as Space, Time, and Multivariate Analytics and Extreme Scale Visual Analytics.
 3
CSEC-733
Information Security Risk Management
This course will provide students with an introduction to the principle of risk management and its three key elements: risk analysis, risk assessment and vulnerability assessment. Students will also learn the differences between quantitative and qualitative risk assessment, and details of how security metrics can be modeled/monitored/controlled and how various types of qualitative risk assessment can be applied to the overall assessment process. Several industry case studies will be studied and discussed. Students will work together in teams to conduct risk assessments based on selected case studies or hypothetical scenarios. Finally, they will write and present their risk assessment reports and findings.
 3

 

To graduate, students need to complete all the requirements as listed in the curriculum graduation policy

Faculty

Website last updated: June 27, 2024