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Master of Science in Data Science (new)

  • College Of Engineering & IT
  • graduate

Master of Science in Data Science (new)

Fall & Spring


Full & Part time

Study Mode

30 hours

Total Credit Hours

2 years




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The Master of Science (M.Sc.) in Data Science program is carefully crafted to equip students with the necessary skills and knowledge for a successful career in data analysis, computing, and applied mathematics. This program aims to enhance students’ employability by providing them with a high level of expertise in the field.

Upon completion of the program, graduates will possess the skills required to excel in the public and government sectors, opening up numerous opportunities for career advancement. Additionally, the private sector also offers promising prospects for graduates, as they will have a strong understanding of statistical analysis and automated decision-making processes. This valuable knowledge makes them desirable candidates for businesses, industries, and IT firms.

The M.Sc. degree is awarded upon the successful completion of a minimum of 30 credit hours.  The Master of Science (M.Sc.) in data science is broken down into four (4) semesters having 30 CH course (8 courses of which 3 courses are offered each Semester + 1 Master Thesis of 6 CH).

The goal of the MSDS program:
  • Present the students with techniques to manage complex data sets.
  • Presents the students with data scientist skills to model and analyze data using algorithms and data driven computing programs.
  • Present the students with data scientist skills to formulate and present analytics solutions that is appropriate to the stakeholders.

Admission Requirement

Regular Admission Conditional Admission
Accredited bachelor’s degree in IT or an equivalent degree with a minimum GPA of 3.0 Accredited bachelor’s degree in IT or an equivalent degree with a minimum GPA of 2.50 – 2.99
EmSAT 1400 I Academic IELTS 6.0 or TOEFL (PBT 550 or IBT 79 or CBT 213) EmSAT 1250 or Academic IELTS 5.5 or TOEFL (PBT 530 or IBT 71 or CBT 197)

Who is the program for?

A data science master’s degree program is typically designed for individuals who have a strong background in mathematics, statistics, computer science, or a related field and are interested in pursuing a career in data science. It is a specialized program that provides advanced training and knowledge in the field of data science, combining elements of statistics, machine learning, computer science, and domain expertise. Here are some groups of individuals who may benefit from pursuing a data science master’s degree:
  1. Graduates in STEM Fields: Individuals with a bachelor’s degree in mathematics, statistics, computer science, engineering, or other related fields often pursue a data science master’s degree to enhance their skills and gain expertise in data analysis, predictive modeling, and machine learning.
  2. Working Professionals: Professionals who are already working in fields related to data analysis, business intelligence, or data engineering may opt for a data science master’s degree to advance their careers and acquire specialized skills in data science methodologies, algorithms, and tools.
  3. Career Changers: Individuals from diverse backgrounds, such as business, economics, social sciences, or even humanities, who are interested in transitioning into a data-driven career can pursue a data science master’s degree to gain the necessary skills and knowledge in data analysis and modeling.
  4. Researchers and Academics: Individuals interested in research or academia may pursue a data science master’s degree to deepen their understanding of statistical and computational techniques for analyzing and interpreting data, which can be applied in various scientific domains.
  5. Entrepreneurs and Decision-makers: Professionals who are involved in making data-driven decisions, such as business executives, managers, or entrepreneurs, can benefit from a data science master’s degree to gain a deeper understanding of data analytics, data-driven decision-making, and developing data-driven strategies.
It’s important to note that the specific requirements and prerequisites for data science master’s programs may vary between universities and institutions. Prospective students should carefully review the program curriculum and admission criteria to ensure they meet the necessary prerequisites and have the appropriate background to succeed in the program.

Career Opportunities

A Master of Science in Data Science can open up a wide range of career opportunities in various industries. The demand for data scientists continues to grow as organizations seek to leverage the power of data for insights, decision-making, and problem-solving. Here are some potential career paths for holders of a Master of Science in Data Science:
  1. Data Scientist: Data scientists are responsible for collecting, analyzing, and interpreting large and complex datasets to extract meaningful insights and solve business problems. They use statistical techniques, machine learning algorithms, and programming skills to develop models, build predictive analytics solutions, and provide data-driven recommendations.
  2. Data Analyst: Data analysts focus on analyzing data to identify patterns, trends, and correlations. They work with structured and unstructured data, perform data cleansing and transformation, and generate reports and visualizations to communicate findings to stakeholders. Data analysts may also be involved in designing and implementing data collection methods and databases.
  3. Machine Learning Engineer: Machine learning engineers develop and deploy machine learning models and algorithms to solve specific business problems. They work on training models, optimizing algorithms, and deploying them into production systems. They need a deep understanding of machine learning techniques, programming skills, and experience with tools and libraries for model development and deployment.
  4. Business Intelligence Analyst: Business intelligence analysts use data analysis and visualization techniques to provide insights and support decision-making within an organization. They work with various data sources, create dashboards and reports, and collaborate with stakeholders to understand business requirements and identify opportunities for improvement.
  5. Data Engineer: Data engineers are responsible for designing, building, and maintaining the infrastructure and systems that enable efficient data collection, storage, and processing. They work with big data technologies, develop data pipelines, and ensure data quality and integrity. Data engineers collaborate closely with data scientists and analysts to provide them with reliable and accessible data.
  6. Data and Analytics Consultant: Data and analytics consultants work with clients from different industries to help them leverage data for strategic decision-making and business growth. They provide expertise in data analysis, develop customized solutions, and offer recommendations on data-driven strategies. Consultants need strong communication and problem-solving skills to work with diverse clients and teams.
  7. Research Scientist: Data science graduates can pursue careers in research, working in academia or research institutions. They focus on advancing the field of data science, developing new methodologies, and solving complex research problems using data-driven approaches. Research scientists often contribute to academic publications and collaborate with other researchers.
These are just a few examples, and the field of data science offers numerous other career paths in areas such as healthcare, finance, cybersecurity, marketing, and more. The specific career opportunities may vary based on individual interests, specialization, and industry preferences.

Estimated salary range

Salaries for Master of Science in Data Science holders in the UAE can vary depending on factors such as the level of experience, the industry, the size and reputation of the company, and the specific job role. As of my knowledge cutoff in September 2021, here are some estimated salary ranges for data science professionals in the UAE:
  1. Entry-level Data Analyst or Data Scientist: For individuals with a Master of Science in Data Science and limited experience, the average salary range can be around AED 8,000 to AED 12,000 per month (approximately USD 2,200 to USD 3,300).
  2. Mid-level Data Scientist or Data Engineer: With a few years of experience, mid-level data scientists or data engineers can expect salaries ranging from AED 12,000 to AED 20,000 per month (approximately USD 3,300 to USD 5,500).
  3. Senior Data Scientist or Data Science Manager: For professionals with extensive experience and expertise, senior data scientists or data science managers can earn salaries in the range of AED 20,000 to AED 40,000 per month (approximately USD 5,500 to USD 11,000) or more.
It’s important to note that these figures are just estimates and can vary depending on various factors. Additionally, salaries may also be influenced by other factors such as the company’s benefits package, location, and demand for data science professionals in the market.

Study Plan

Year 1
Semester 1 Courses Code Courses Title Semester 2 Courses Code Courses Title
1 DS601 Fundamentals of Data Science 2 DS621 Research Methods in Data Science
DS602 Statistics & Probability in Data Science DS620 Data Visualization & Data Representation Techniques
DS603 Advanced Database Queries and Data Warehouse in Data Science DS604 Big Data Management using Hadoop
Year 2
Semester 3 Courses Code Courses Title Semester 4 Courses Code Courses Title
3 DSXXX Electives 1 4 DS629 Data Science Thesis (Continued from Semester 3)
DSXXX Electives 2
DS629 Data Science Thesis

Courses Description

DS 601 Fundamentals of Data Science
This course introduces the principles of data science field as well as review common functionalities of toolkits that are suitable for a data scientist portfolio, with emphasis on data sets cleaning, processing, merging, manipulating, as well as statistical concepts and test for significance in data. In particular, this course focuses on how to read in datasets into data structures, to query and index these structures, to merge multiple data structures, to summarize data into tables, to group data into logical categories, and to manipulate dates. The coverage also includes essentials of machine learning topics such as regression, classification and clustering as well as the importance of storytelling and data visualizations in data science process. This course is an intensive practice course using real-world datasets and variety of statistical methods for both data-cleaning activities and compute statistical metrics for data analysis.  
DS 602 Statistics and Probability in Data Science
The course introduces the foundations of probability and statistics necessary for modeling and undertaking statistical decisions. The course combines both the mathematical theory and practices of applying this theory to actual data. Coverage includes probability and probability distributions, descriptive statistics random variables, dependence, statistical test and confidence intervals, correlation, regression, entropy and experimental design.  
DS 603 Big Data Management Using Hadoop
This course introduces the fundamentals of managing big data and the corresponding standard big data platform that is suitable for addressing substantive needs for handling the different significant amount of data. This course includes an introduction to Hadoop, Hadoop components for data organization, storage, retrieval, and analysis and processing data using Hadoop and querying big data. This course will include topics such as large-scale data analysis, data storage systems, data representations, semi-structured data models. This course will involve hands- on practices using Hadoop to handle real-world datasets.  
DS 604 Advanced Database Queries and Data Warehouse in Data Science
This course provides review concepts and practices as applied to database and data warehouse. Coverage includes of developing advanced database queries, distributed databases and performance issues in database system. Moreover, the course introduces state-of-the-art on a set of topics warehouses and dimensional data modeling.
DS 620 Data Visualization & Data Representation Techniques
This course focuses on data visualization techniques for effective communication of data science results through visual analytics dashboards and storytelling. Students will gain a thorough understanding of information visualization design, principles, guidelines, and evaluation criteria. The course covers best practices for creating basic diagrams and charts, statistical charts and tables, and selecting appropriate methods for specific problems. They will learn how to make data-driven decisions, apply interactive techniques to visualizations, and perform data transformations. The course also includes hands-on experience in creating interactive visualizations and dashboards using Python and leading tools such as Power BI, Tableau, and QlikView.
DS 621 Research Methods in Data Science
The purpose of the course is to provide an opportunity for the students to prepare their thesis or research project proposal, excellent writing style and compelling research strategies research, analyze ethical issues that may arise when working datasets. Formulate problem statement, conduct the literature review, identify the type of research method strategy, and plan the overall structure of the study, prepare the project data sets. Also, you will learn about issues of reproducibility, and how to set up your data science product such that it is reproducible.
DS 623 Machine Learning in Data Science
This course introduces machine learning techniques and methods. The coverage includes applied machine learning for the data scientist, the issue of the dimensionality of data, the task of data clustering, clusters evaluation, supervised approaches for building predictive models, data generalization (e.g., cross-validation and over-fitting). This course coverage includes advanced techniques, such as developing ensembles methods, and predictive models’ practical limitations.
DS 624 Text mining in Data Science
This course introduces text mining and manipulation techniques. Course coverage includes working with text toolkits, the text structure for both human and machines, text manipulation needs, regular expressions, text cleaning, preparing text for machine algorithms processing, natural language processing techniques, classifications, and topic and similarity detection in documents. This course involves hands-on practices using real-world documents.
DS 625 Social Network Analysis in Data Science
This course involves hands-on practices using real-world documents. The course introduces the process of modeling the social structures as networks, analyze the connectivity of social networks, measure the importance of nodes with the social network such as centrality and closeness, network evolution over time, a model of network generation and link problem prediction.
DS 626 Management Data Science
The course applies the tools and techniques of management science to engineering problems. It covers linear programming, sensitivity analysis, waiting line, decision analysis, forecasting. The course makes use of available optimization software and spreadsheets to solve practical problems through case studies.
DS 628 Ethics in Data Science
This course introduces ethics, policies and legal issues that face computer professionals and data scientist while working with information systems and datasets. This course will discuss Intellectual Property, privacy debates, laws and professional ethics governing these issues while working with computer systems, as well as the course, will examine the related cyber security issues. Specific case studies and assignments will be used to illustrate the discussed issues.
DS 631 Data Science Thesis
The purpose of this thesis is to provide an opportunity to analyze large-scale real-world problems according to the scientific methods. The student demonstrates an ability to utilize and apply various data science opportunity and techniques to design, implement, test, and evaluate a complex problem, deriving insights from data and sharing ideas with other stakeholders. The focus of the thesis depends on the selected elective courses and therefore corresponds Data science product outcome.

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