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Integrated Master of Science in Data Science

Program Code

MSCDS

Level

5 Years Integrated

Duration

5 Year

Department name

Department of Data Science
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Semester Sr no CourseCode Course CourseCredit
Sem-1 1 EC-101 Yoga 2
Sem-1 2 FC-101 Communication Skills 2
Sem-1 3 CC-101 Mathematical Basics 3
Sem-1 4 CC-102 Descriptive Statistics and Probability 3
Sem-1 5 CC-103 Introduction to Data Science 3
Sem-1 6 CC-104 Basics of Computer Organization & Architecture 3
Sem-1 7 CC-105 Information and Communication 3
Sem-1 8 CC-ICT Technology (ICT) 3
Sem-1 9 CC-106 (P) Practical Based on CC-101& CC-102 3
Sem-1 10 CC-107 (P) Practical Based on CC-105 3
Sem-10 1 CC-511 Dissertation/ Project Work 16
Sem-10 2 CC-512 Seminar/ Symposium/ Conference 4
Sem-10 3 CC-513 Research Paper/ MOOCs/ Internship/ OJT 5
Sem-2 1 EC-111 Environmental Studies 2
Sem-2 2 FC-111 Commercial Communication 2
Sem-2 3 CC-111 Calculus and Introduction to Matrices 3
Sem-2 4 CC-112 Numerical and Statistical Methods 3
Sem-2 5 CC-113 Algorithms and Data Structures 3
Sem-2 6 CC-114 Object-Oriented Programming with JAVA 3
Sem-2 7 CC-115 Programming with PYTHON 3
Sem-2 8 CC-116 (P) Practical Based on CC-114 3
Sem-2 9 CC-117 (P) Practical Based on CC-115 3
Sem-3 1 FC-201 Soft Skills I 2
Sem-3 2 CC-201 Matrix Algebra and Calculus 3
Sem-3 3 CC-202 Random Variable and Distributions 3
Sem-3 4 CC-203 Discrete Mathematics 3
Sem-3 5 CC-204 Operating System Concepts 4
Sem-3 6 CC-205 Database Management Systems using SQL and PL/SQL 4
Sem-3 7 CC-206 (P) Data Analysis using Excel 3
Sem-3 8 CC-207 (P) Practical Based on CC-205 3
Sem-4 1 FC-211 Soft Skills II 2
Sem-4 2 CC-211 Linear Algebra 3
Sem-4 3 CC-212 Statistical Inference Theory 3
Sem-4 4 CC-213 Vector Calculus 3
Sem-4 5 CC-214 Introduction to Natural Language Processing 4
Sem-4 6 CC-215 Python for Data Science 4
Sem-4 7 CC-216 (P) R Programming – I 3
Sem-4 8 CC-217 (P) Practical Based on CC-215 3
Sem-5 1 FC-301 Scientific Writing 2
Sem-5 2 CC-301 Differential Equations 3
Sem-5 3 CC-302 Regression Theory 3
Sem-5 4 CC-303 Distributed Platforms 4
Sem-5 5 CC-304 Machine Learning-I 4
Sem-5 6 CC-305 (P) Data Visualization 3
Sem-5 7 CC-306 (P) Practical Based on CC-303 3
Sem-5 8 CC-307 (P) Practical Based on CC-304 3
Sem-6 1 FC-311 Personality Development 2
Sem-6 2 CC-311 Operations Research 4
Sem-6 3 CC-312 Research Methodology 3
Sem-6 4 CC-313 Machine Learning-II 4
Sem-6 5 CC-314 & CC-315 PROJECT – I: Mini-Project 6
Sem-6 6 CC-316 (P) R Programming – II 3
Sem-6 7 CC-317 (P) Practical Based on CC-313 3
Sem-7 1 CC-401 Advanced Algorithms 4
Sem-7 2 CC-402 Text, Image & Video Analytics 4
Sem-7 3 CC-403 Deep Learning 4
Sem-7 4 CC-404 (P) Practical Based on CC-403 4
Sem-7 5 CC-405 (P) Power BI & Tableau 4
Sem-7 6 CC-406 PROJECT – II: Project 5
Sem-8 1 CC-411 Multivariate Analysis 4
Sem-8 2 CC-412 Cloud Computing 4
Sem-8 3 CC-413 Big Data Analytics 4
Sem-8 4 CC-414 Advanced NLP 4
Sem-8 5 CC-415 (P) Practical Based on CC-414 4
Sem-8 6 CC-416 PROJECT – III: Project 5
Sem-9 1 CC-501 Cyber Security 4
Sem-9 2 CC-502 Project Deployment on Cloud AWS 4
Sem-9 3 CC-503 Blockchain Technology 4
Sem-9 4 CC-504 Reinforcement Learning 4
Sem-9 5 CC-505 (P) Practical Based on CC-504 4
Sem-9 6 CC-506 PROJECT – IV: Project 5
Intake
Eligibility Criteria
  • PO1: 1. Foundational Knowledge of Data Science: o Develop a strong understanding of core data science concepts, including statistics, probability, data analysis, and the scientific approach to problem-solving.
  • PO2: 10. Research and Continuous Learning: o Engage in research to explore new methods and advancements in data science. Cultivate a mindset of continuous learning to stay updated with the evolving landscape of data science technologies and practices.
  • PO3: 11. Collaboration and Project Management: o Work effectively in teams, manage data science projects from conception to deployment, and utilize project management tools and methodologies. Demonstrate the ability to manage time, resources, and stakeholder expectations.
  • PO4: 12. Problem-Solving and Critical Thinking: o Apply critical thinking and analytical skills to identify, formulate, and solve complex data-driven problems. Develop innovative solutions and leverage data to support decision-making processes.
  • PO5: 2. Proficiency in Programming and Data Manipulation: o Gain proficiency in programming languages commonly used in data science, such as Python, R, and SQL. Students will be able to manipulate and analyze data efficiently using libraries and tools like Pandas, NumPy, and Spark.
  • PO6: 3. Data Collection and Preprocessing: o Acquire skills in data acquisition from various sources, including databases, APIs, and web scraping, and perform data cleaning and preprocessing to prepare datasets for analysis.
  • PO7: 4. Statistical Analysis and Inference: o Apply statistical methods and hypothesis testing to draw meaningful insights from data, understand data distributions, and make data-driven decisions.
  • PO8: 5. Machine Learning and Predictive Modeling: o Design, implement, and evaluate machine learning models, including supervised, unsupervised, and reinforcement learning techniques. Understand model selection, hyperparameter tuning, and performance evaluation metrics.
  • PO9: 6. Data Visualization and Communication: o Utilize data visualization tools such as Matplotlib, Seaborn, and Tableau to create clear and informative visual representations of data. Effectively communicate findings and insights to stakeholders through visual storytelling and data reporting.
  • PO10: 7. Big Data and Scalable Data Systems: o Understand the principles and tools for working with large-scale data, including Hadoop, Spark, and NoSQL databases. Learn to build scalable data pipelines and handle big data challenges.
  • PO11: 8. Domain-Specific Applications: o Apply data science techniques to solve problems in specific domains such as finance, healthcare, marketing, and more. Understand domain-specific datasets and tailor data science solutions to meet the needs of different industries.
  • PO12: 9. Ethics and Responsible Data Science: o Recognize the ethical considerations in data science, including data privacy, security, bias, and fairness. Develop solutions that adhere to ethical standards and regulatory requirements.
  • PSO1: 1. PSO 1: Advanced Data Analytics and Statistical Modelling o Develop advanced skills in statistical analysis, hypothesis testing, and predictive modeling. Graduates will be capable of building and interpreting complex statistical models to derive insights and support data-driven decision-making.
  • PSO2: 10. PSO 10: Effective Communication and Team Collaboration o Demonstrate strong communication skills to present data-driven insights clearly and persuasively to both technical and non-technical audiences. Graduates will also excel in collaborative environments, working effectively in cross-functional teams to deliver data science solutions.
  • PSO3: 2. PSO 2: Proficiency in Machine Learning and AI Techniques o Gain expertise in implementing machine learning and artificial intelligence algorithms, including regression, classification, clustering, deep learning, and natural language processing. Graduates will be skilled in choosing the appropriate models and optimizing them for specific applications.
  • PSO4: 3. PSO 3: Big Data Technologies and Scalable Data Solutions o Master the use of big data technologies such as Hadoop, Spark, and cloud computing platforms. Graduates will be proficient in handling, processing, and analyzing large-scale datasets, designing scalable data solutions, and optimizing performance for high-volume data environments.
  • PSO5: 4. PSO 4: Data Engineering and Pipeline Development o Acquire skills in data engineering, including the design and implementation of data pipelines, ETL (Extract, Transform, Load) processes, and data warehousing solutions. Graduates will be able to ensure efficient data flow and integration across various data sources and systems.
  • PSO6: 5. PSO 5: Data Visualization and Business Intelligence o Develop expertise in data visualization and business intelligence tools such as Tableau, Power BI, and Python libraries like Matplotlib and Seaborn. Graduates will be able to create compelling visualizations that communicate data insights effectively to business stakeholders.
  • PSO7: 6. PSO 6: Domain-Specific Data Science Applications o Apply data science techniques to solve domain-specific problems in areas such as finance, healthcare, retail, manufacturing, and more. Graduates will understand domain-specific data nuances and be capable of tailoring data science solutions to industry needs.
  • PSO8: 7. PSO 7: Ethical Data Practices and Responsible Data Science o Demonstrate a strong commitment to ethical data practices, including data privacy, security, and bias mitigation. Graduates will be equipped to develop responsible data science solutions that adhere to legal standards and ethical guidelines.
  • PSO9: 8. PSO 8: Research, Innovation, and Continuous Improvement o Engage in research activities to explore and innovate new data science methodologies and applications. Graduates will be prepared to contribute to the advancement of the data science field and pursue lifelong learning to adapt to emerging technologies and trends.
  • PSO10: 9. PSO 9: Integration of Data Science in Business Strategy o Develop the ability to integrate data science into business strategy, providing actionable insights that drive business decisions and growth. Graduates will be able to work closely with business units to align data science initiatives with organizational goals.
Subject Name: Data Visualization

Statements: • Develop skills in data visualization using tools like Matplotlib, Seaborn, and Tableau. • Create informative and effective visualizations to communicate insights from data. • Learn to tell data-driven stories that inform decision-making through clear and impactful presentations.

Subject Name: Big Data Analytics

Statements: • Understand the concepts of big data and the tools used for big data processing, such as Hadoop, Spark, and NoSQL databases. • Learn to handle and analyze large datasets efficiently using distributed computing frameworks. • Implement big data solutions and perform large-scale data analysis to derive insights.

Subject Name: Introduction to Data Science

Statements: • Understand the basic concepts of data science, including data lifecycle, types of data, and the role of data scientists. • Gain proficiency in using tools like Jupyter Notebooks, Python, and R for basic data manipulation and analysis. • Learn to apply the data science process, including problem formulation, data collection, data cleaning, analysis, and interpretation of results.

Academy Year Title Download
2021-2022 M.Sc. (Int) Data Science
2023-2024 Integrated Master of Science in Data Science Semester 2

Gujarat University

Online Admission PhD
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