Course Description

The MS in Data Science is designed to provide interested students with a comprehensive understanding of the principles, techniques, and applications of data science. The program integrates concepts from mathematics, statistics, computer science, and domain-specific knowledge to act as a basis for understanding and utilizing various data science tools and developing data-driven insights and models. Furthermore, It equips students with the necessary skills to analyze and interpret complex data sets, make data-driven decisions, and develop innovative solutions to real-world problems.

Course Objectives

• Develop a thorough understanding of the basic concepts, theories, and techniques used in data science.
• Learn to use state-of-the-art data science tools and technologies to collect, process, and analyze large and complex datasets.
• Acquire expertise in applying statistical and machine learning models to obtain significant insights in order to make precise predictions from data.
• Develop proficiency in data visualization techniques to effectively communicate findings and insights to both technical and non-technical audiences.
• Develop the ability to work effectively in multidisciplinary teams and collaborate with domain experts to solve real-world data-driven problems.
• Gain practical experience by applying data science methodologies and techniques to industry-relevant projects or research studies.

Learning Outcomes

• Demonstrate a deep understanding of the core principles, theories, and methodologies in data science.
• Collect, clean, and preprocess various types of data from diverse sources for analysis.
• Apply appropriate statistical and machine learning algorithms to solve complex data problems and make accurate predictions.
• Effectively visualize and communicate data-driven insights and findings using appropriate visualization techniques and tools.
• Collaborate effectively with cross-functional teams to address real-world data science challenges and deliver actionable solutions.
• Successfully plan, execute, and present a data science project or research study, demonstrating critical thinking and problem-solving skills.

Eligibility

• At least 16 years of education in relevant subjects such as Data Science, Computer Science, Software Engineering, Information Technology with at least 2.70 CGPA or 2 years Master’s degree in Computing/IT (awarded after 2 years BSc) with a 2.70 CGPA (with a minimum of 120 credit hours in Bachelors and 60 credit hours in the Master program) or 60% aggregate marks in annual system from any HEC recognized university/institute are eligible to apply.
• At least 16 years of education in Statistics, Mathematics, Economics, Management Sciences, Accounting & Finance, Physics, Computer Engineering, Bachelor of Electrical /Electronics Engineering Candidates are eligible to apply. However, such candidates shall be required to qualify an obligatory foundation semester of 9 to 12 credit hours of deficiency courses.

Qualifying the ETS-GRE general test/NTS GAT- General /Institute’s own test or any other test required by the HEC with 50% marks and interview are mandatory for admission.

The Hafiz Quran shall be given a special credit of 20 marks.
The credit marks shall be added subject to fulfillment of basic requisite academic qualification.

Outline of the MS (Data Science) program

The program would be spread over 3 semesters, with a 6-credit hour thesis being offered in the second year.

Course offering plan

Core courses

1. Statistical and Mathematical Methods for Data Science (3)
2. Tools and Techniques in Data Science (2+1)
3. Advanced Machine Learning (3)

Specialization Core Courses (Choose any 2)

1. Advanced Big Data Analytics (3)
2. Deep Learning (3)
3. Advanced Natural Language Processing (3)
4. Distributed Data Processing (3)

Semester-wise course offering plan

*2+1 means 2 hours of Lecture + 3 hours of Lab work.

Elective courses

Following is a non-exhaustive list of elective courses. The Institute can add new courses based on market demand and resource availability.

• Advanced Computer Vision
• Algorithmic Trading
• Bayesian Data Analysis
• Bioinformatics
• Computational Genomics
• Data Visualization
• Deep Reinforcement Learning
• Inference & Representation
• Optimization Methods for Data Science and Machine Learning
• Probabilistic Graphical Models
• Scientific Computing in Finance
• Social Network Analysis
• Time Series Analysis and Prediction