Master of Science – Data Science (MSCDS)

Master of Science – Data Science (MSCDS)

  • The basic objective of the Master of Science – Data Science is to provide to the country a steady stream of competent young men and women with the necessary knowledge, skills and foundations for acquiring a wide range of rewarding careers into the rapidly expanding world of Information Technology.

Graduates possessing any faculty of any statutory University shall be eligible for admission to the Master of Science - Information Technology course.

Sr.No. Name of the subject Subject Code Semester Credit Marks SLM Syllabus
Theory Practicale CEC TEE
Data Structure and AlgorithmsMSCDS-101Sem-14--3070
Relational Database Management SystemMSCDS-102Sem-14--3070
Principles of Data ScienceMSCDS-103Sem-14--3070
Statistical MethodsMSCDS-104Sem-14--3070
Probability and Distribution TheoryMSCDS-105Sem-14--3070
Programming Skills - I MSCDS-106Sem-1--4--100
Regression Analysis and Predictive ModelsMSCDS-201Sem-24--3070
Data Analysis and VisualizationMSCDS-202Sem-24--3070
Big Data Analytics using "R”MSCDS-203Sem-24--3070
Problem Solving using PythonMSCDS-204Sem-24--3070
Cloud Infrastructure and ServicesMSCDS-205Sem-24--3070
Programming Skills - IIMSCDS-206Sem-2--4--100
Artificial IntelligenceMSCDS-301Sem-34--3070
Machine LearningMSCDS-302Sem-34--3070
Natural Language ProcessingMSCDS-303Sem-34--3070
Advanced Python ProgrammingMSCDS-304Sem-3--43070
Data Processing and Modeling with HadoopMSCDS-305Sem-34--3070
Programming Skills - III MSCDS-306Sem-3--4--100
Internship cum Industrial ProjectMSCDS-401Sem-4--24200400
  • A Student is required to pass (successfully complete) each paper with 40% of marks in Continuous Evaluation and Term End Examination
Assessment Pattern of Students
  • Continuous Evaluation - Based on the Assignment (30%) (Two Assignments – Per Paper)
  • Term End Examination - Based on Semester End Examination (70%)

Learners can be admitted

Name: Dr. Himanshu Patel

Designation: Assistant Professor


Our Master of Science in Data Science (MSCDS) opens up a plethora of career opportunities in various industries due to the increasing demand for professionals with expertise in handling and analyzing large datasets.

Here are some common career options for individuals with an MSCDS:

  • Data Scientist: Data scientists are responsible for collecting, cleaning, analyzing, and interpreting large datasets to extract valuable insights and inform business decisions. They typically use statistical techniques, machine learning algorithms, and programming languages such as Python and R.
  • Data Analyst: Data analysts focus on interpreting data to identify trends, patterns, and correlations that can help organizations make data-driven decisions. They use statistical analysis and data visualization tools to present findings in a clear and actionable manner.
  • Machine Learning Engineer: Machine learning engineers develop and deploy machine learning models to solve complex problems and automate processes. They work with algorithms such as neural networks, decision trees, and support vector machines to build predictive models and recommendation systems.
  • Big Data Engineer: Big data engineers design and maintain the infrastructure required to store, process, and analyze large volumes of data. They work with distributed computing frameworks such as Hadoop and Spark and database technologies like NoSQL and SQL to build scalable and efficient data pipelines.
  • Business Intelligence (BI) Developer: BI developers design and implement business intelligence solutions that enable organizations to gather, analyze, and visualize data to gain insights into their operations and performance. They use tools like Tableau, Power BI, and QlikView to create dashboards and reports for decision-makers.
  • Data Architect: Data architects design the structure and integration of data systems to ensure they meet the organization's needs for data storage, accessibility, and security. They develop data models, design data warehouses, and establish data governance policies.
  • Data Engineer: Data engineers build and maintain the systems and processes for collecting, storing, and preparing data for analysis. They work with databases, ETL (extract, transform, load) tools, and data warehousing technologies to ensure data quality and reliability.
  • Quantitative Analyst (Quant): Quants apply mathematical and statistical techniques to financial data to develop trading strategies, risk models, and investment portfolios. They use programming languages like Python, R, and MATLAB to analyze market trends and make data-driven decisions in finance and investment banking.
  • Research Scientist: Research scientists conduct advanced research in data science and related fields, exploring new algorithms, methodologies, and technologies to address complex problems. They often work in academia, government agencies, or research organizations.
  • Healthcare Data Analyst: Healthcare data analysts analyze medical data to improve patient outcomes, optimize healthcare delivery, and support medical research. They work with electronic health records, clinical databases, and healthcare analytics platforms to extract actionable insights from healthcare data.