Masters of Science in Professional Studies: Data Analytics | RIT Dubai

Program Contact

Sanjay Modak

Chair - Graduate Programs and Research Department, Associate Professor of Economics

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 be able to apply their learnings in the program at their workplace, enhancing their career prospects. 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

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.
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 - 33

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 (The) seminar course provides a structured context to enable students in the MS in Professional Studies degree program to prepare a formal Thesis Proposal as a prerequisite for entering the final course in the program, PROF-776 Thesis. Activities include researching and defining a real-world, multidisciplinary problem or opportunity; providing a justification as to how the student's multidisciplinary education has prepared him/her to be uniquely qualified to address the defined problem; proposing a solution to the defined problem (a project) that consists of a problem statement, a set of deliverables, a timeline, and a research methodology for assessing the success of the completed project; and establishing a formalized relationship with a thesis supervisor with relevant subject matter expertise who can guide and evaluate the student's work in the subsequent course, PROF 776 Thesis.	0
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-792	Data Analytics Transformations This course equips participants with essential skills to navigate the complexities of data analytics challenges and harness its potential for value creation. Students learn to identify the pitfalls leading to the failure of analytics projects, emphasizing the critical role of the analytics translator in bridging technical insights with business objectives. Through a comprehensive understanding of the business model, participants explore avenues for further value generation via data-driven approaches. They delve into customer profiling techniques, employing data innovation to gather and analyze information for enhanced business intelligence and decision-making. Understanding the customer journey becomes paramount, with an emphasis on its impact on product or service offerings and the entrepreneurial mindset necessary for experimentation and adaptation. Revenue generation and data monetization strategies are explored to ensure financial sustainability and economic value creation, with a focus on business model innovation and leveraging data assets. Lastly, participants learn to develop successful business models by integrating leadership principles with data-driven insights, augmenting traditional approaches with innovative strategies for sustainable growth in today's dynamic market landscape.	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
PROF-776	Thesis Part 1	3
PROF-776	Thesis Part 2	3