Department of Mathematics || Dhamma Dipa International Buddhist University

Ph.D in Mathematics M.Sc. Mathematics B.Sc. Mathematics

Course Duration: 5 years

Eligibility: M.Sc in Mathematics

Intake: 10

Research Methodology (MT-PHD-01)

Credit -4/100 Marks Duration: 4 Hrs/ Week

UNIT – I: Foundations of Research

Meaning and Objective of Research, Types of Research, Data Sources: Primary and Secondary, Data Collection, Generation, Arrangement, and Processing

UNIT-II: Basic Computer Applications

Basic computer knowledge, Features and applications related to presentation of text in suitable format and saving the data for future applications. Use of word processing, Practical knowledge of MS Word to type the script, insert tables, figures and graphs, plotting of graphs in excel, Preparation of power point presentations based on the topic of research. Insertion of figures, graphs, charts in presentation. Use of spreadsheet and database software, Preparation of scientific posters for presentations, Internet and its application: Email, WWW, Web browsing, acquiring technical skills, drawing Inferences from data, Cloud computing. Introduction to LaTeX programming for scientific writing, document structuring, mathematical typesetting, insertion of images, tables, references and preparation of research articles, theses and presentations using Beamer.

UNIT-III: Quantitative methods, Statistics and application of Computer in statistics

Measures of Central tendency and Dispersion. Probability distribution- Normal, Binomial and Poisson distribution. Parametric and non-parametric statistics. Confidence interval, Errors. Quantitative Techniques: Levels of significance, Regression and Correlation coefficient. Statistical analysis and fitting of data; Chi‐Square Test, Association of Attributes t‐Test ANOVA Standard deviation, Co‐efficient of variations. Open-source software for quantitative and statistical analysis.

UNIT-IV: Documentation and scientific writing:

Results and Conclusions, Preparation of manuscript for Publication of Research paper, Presenting a paper in scientific seminar, Thesis writing. Structure and Components of Research Report, Types of Report: research papers, thesis, Research proposal, Research Project Reports, Pictures and Graphs, citation styles, writing a review of paper, Bibliography.

Research and Publication Ethics (MT-PHD-02)

Credits-2/100 Marks Duration: 2 Hrs/ Week

UNIT-I: Philosophy, Ethics, and Scientific Conduct

1. Introduction to Philosophy: Definition, nature, and scope; concept, branches.

2. Ethics: Definition, moral philosophy, nature of moral judgments and reactions. Ethics with respect to science and research; intellectual honesty and research integrity.

3. Scientific Misconduct: Falsification, fabrication, and plagiarism (FFP).

4. Redundant Publications: Duplicate and overlapping publications, salami slicing. Selective reporting and misrepresentation of data.

UNIT-II: Publication Ethics

Publication Ethics: Definition, introduction, and importance. Violation of publication ethics, authorship, and contributor ship.

2. Basic Practices and Standards: Setting initiatives and guidelines (COPE, WAME, etc.), conflict of interest.

3. Publication Misconduct: Definition, concept, problems leading to unethical behaviour, identification of publication misconduct, complaints and appeals.

4. Predatory Publishers and Journals.

Practice

1. Open Access Publishing:

A. Publications and initiatives; SHERPA/RoMEO for copyright policies; tools to identify predatory publications (e.g., SPPU software);

B. Journal suggestion tools (JANE, Elsevier Journal Finder, Springer Suggester).

2. Publication Misconduct:

A. Group discussion: Subject-specific ethical issues, FFP, authorship, conflict of interest, complaints and appeals; examples and fraud cases (India and abroad).

B. Software tools: Use of plagiarism checkers (Turnitin, Unkind, etc.).

3. Database and Research Metrics:

A. Databases: Indexing databases, citation databases (Web of Science, Scopus, etc.).

B. Research Metrics: Journal impact factor (JCR), SNIP, SJR, IPP, Cite Score, h-index, g-index, i10 index, altimetric.

Major Paper

Bicomplex Numbers (MT-PHD-03)

Credit-4/100 Marks Duration: 4 Hrs/ Week

UNIT I – Foundations of Complex and Multicomplex Numbers (Compact Overview): Concise revision of classical complex numbers, covering basic algebra, geometry, analytic functions, and Cauchy–Riemann equations. It then introduces multicomplex systems, explaining how Bicomplex numbers arise as a natural extension of the complex field.

UNIT II – Algebraic & Topological Structure of Bicomplex Space: Introducing the idempotent basis and the decomposition of BI complex numbers into two auxiliary complex components. It discusses zero divisors, the null cone, conjugations, and BI complex modulus, and then introduces the induced topology of BI complex space, including open and closed discus regions.

UNIT III – Bicomplex Holomorphic Functions and Analytic Theory: The theory of Bicomplex differentiability and holomorphy. Students learn BI complex Cauchy–Riemann equations, the structure of Bicomplex regular functions, and the formulation of power series in idempotent form. Emphasis is placed on convergence behaviour, analytic regions, BI complex entire functions, and the relationship between bicomplex holomorphy and two classical holomorphic functions.

UNIT IV – Bicomplex Functional Analysis: Spaces and Operators: Introduces Bicomplex normed, Banach and Hilbert spaces, with special attention to hyperbolic-valued norms and bicomplex inner products. It covers the bicomplex Schwarz inequality, orthogonality, projections, and the Riesz representation theorem. The theory of bicomplex linear and bounded operators is developed, including spectrum, eigenvalues, and dual spaces. This forms the analytical foundation for advanced Bicomplex research.

UNIT V – Bicomplex Sequence Spaces and Generalized Convergence: Sequence spaces defined over Bicomplex numbers, including Bicomplex versions of classical spaces such as ∫p, C, and C,C₀

Students study entire Bicomplex sequences, Kothe–Toeplitz duals, and the sixteen dual structures emerging from idempotent decomposition. It also introduces advanced convergence concepts such as bicomplex statistical convergence, λ-statistical convergence, I-convergence, and deferred statistical convergence.

Minor/Elective Sequence Spaces, Summability Theory & Statistical Convergence (MT-PHD-04)

Credits: 4/100 Marks, Duration: 4 Hrs/ Week

UNIT I – Basics of Sequences and Classical Convergence: Introduces sequences, subsequences, boundedness, monotonicity, and ordinary limits. It revisits essential theorems such as Bolzano–Weierstrass and Monotone Convergence, forming the analytical foundation needed for modern convergence concepts.

UNIT II – Statistical Convergence: Definitions and Core Ideas: Natural density and the notion of statistical convergence, explaining how it generalizes ordinary convergence. Statistical Cauchy sequences, regularity and examples highlighting the difference between statistical and classical convergence are developed.

UNIT III – Statistical Limit Points and Completeness Properties: Studies statistical limit points and cluster points, exploring their relationship with ordinary limits. It includes statistical analogues of classical completeness theorems and describes how non-thin subsequences determine statistical behaviour.

UNIT IV – Statistical Monotonicity, Boundedness and Behaviour: Examines statistically monotone and statistically bounded sequences, showing how monotonicity “almost everywhere” shapes convergence. It includes characterizations, decomposition results, and counterexamples important for understanding non-classical behaviour.

UNIT V – Sequence Spaces and Summability Methods: Introduces classical and generalized sequence spaces such as C,C₀ and l∞ along with summability methods like Cesàro and matrix transformations. Students study dual spaces and how statistical convergence fits into summability theory.

Sl. No.	Course Titel	Course Code	Marks	Credit
1.	Research Methodology	MT-PHD-01	100	4
2.	Research & Publication Ethics	MT-PHD-02	100	2
3.	Bicomplex Numbers (Major)	MT-PHD-03	100	4
4.	Sequence Spaces, Summability Theory & Statistical Convergence (Elective)	MT-PHD-04	100	4
5.	Seminar	MT-PHD-05	100	2
Total:			500	16

No fee structure available.

Seminar/Workshop Details:

Seminar (MT-PHD-05)

Credit-2/100 marks

1. Review and critics of Published works:

50 Marks Prepare a comprehensive review, summarize key results, highlight strengths and limitations and provide critical analysis in proper academic format.

2. Power Point Presentation of Review Work

50 Marks Prepare and present a seminar using slides, demonstrating clarity, organization, understanding of the topic and ability to answer questions related to the reviewed material.

HOD Message

Dr. Santonu Debnath

It is my privilege to welcome you to the Department of Mathematics, where learning goes beyond classrooms and equations to inspire innovation and critical thinking. Our department is committed to providing a dynamic academic environment that blends the elegance of pure mathematics with the power of computational and applied techniques. The curriculum is thoughtfully designed to equip students with a deep understanding of mathematical theories while fostering skills in problem-solving, programming, data analysis, and research methodologies. We believe that education is not just about acquiring knowledge, but also about cultivating a mindset of curiosity, perseverance and ethical responsibility. Our faculty members are dedicated mentors, guiding students through seminars, workshops, internships, and collaborative research projects that connect classroom concepts to real-world applications. With special emphasis on areas like graph theory, fuzzy logic, optimization, and computational tools such as MATLAB, Python and C programming, our graduates are well-prepared to excel in academia, industry, and research. We take pride in nurturing talent that contributes meaningfully to society, whether through innovative research, teaching, or professional practice. I warmly invite you to be part of our department’s vibrant academic community, where together we can explore, discover and shape the future through the limitless possibilities of mathematics.

Warm regards,
Assistant Professor, Head of the Department
Department of Mathematics

Dhamma Dipa International Buddhist University

Updates

No updates available.

About the Program: Our Postgraduate Program in Mathematics helps students learn advanced topics in pure and applied mathematics. It also teaches how to use mathematics in solving real-life problems through modeling, computation and research. The course builds skills in logical thinking, problem-solving and creativity, preparing students for higher studies and professional careers.

Future Scope: After completing this program, students can join a Ph.D. in their chosen field of mathematics and work on new ideas that add to the subject’s growth. After a Ph.D., they can also do post-doctoral research in top universities in India or abroad, where they can work with experts and publish their work. Postgraduates can build careers in teaching, research, data science, finance, actuarial science and many other fields that need strong mathematical skills.

Research and Collaboration: Our department supports a strong research culture. Students take part in seminars, workshops and projects that often connect mathematics with other subjects. Teachers work with universities and research centers around the world, giving students chances to learn from global experts, apply for fellowships and attend international conferences.

Programme Educational Objectives:

PEO-1: To develop a strong foundation in advanced mathematical theories, methods, and applications, enabling graduates to excel in academic, research and professional environments.

PEO-2: To cultivate analytical thinking, problem-solving skills and research capabilities for addressing complex challenges in mathematics and its interdisciplinary applications.

PEO-3: To prepare graduates for successful careers in academia, industry and research, and to encourage them to pursue higher studies such as Ph.D and post-doctoral programs at national and international levels.

Program Outcomes:

Here are some Program Outcomes based on the syllabus you provided, which focuses on Graph Theory, Algorithms, and Applications, including practical components like MATLAB, C programming, and fuzzy logic:

Students will be able to apply advanced concepts of vector spaces, linear transformations, and inner product spaces to model, analyze and solve mathematical problems across pure and applied domains.
Students will compute and interpret eigenvalues, eigenvectors, and spectral decompositions, and use diagonalization and matrix factorizations in theoretical and computational applications.
Students will classify and analyze graphs, apply shortest path and spanning tree algorithms, and use finite automata and computability theory for problem-solving in computer science and network systems.
Students will formulate and solve problems in metric spaces, functions of bounded variation, Riemann–Stieltjes integration, and Fourier series in real and functional analysis contexts.
Students will apply concepts of multivariable calculus, complex analysis, and special functions to solve real-world mathematical models in engineering, physics and other sciences.
Students will solve ordinary and partial differential equations analytically and numerically, applying stability and spectral methods in mathematical modelling.
Students will apply principles of group theory, ring theory and field extensions, including Galois theory, to abstract algebraic problem-solving and cryptographic applications.
Students will solve and analyse linear and nonlinear integral equations using kernel methods, Laplace transforms, Fourier transforms and Green’s functions.
Students will apply concepts of topology, normed spaces, Banach spaces, and Hilbert spaces to study continuity, compactness and convergence in advanced mathematical frameworks.
Students will be proficient in implementing numerical methods, C programming, MATLAB and Python to solve computational problems in linear algebra, calculus, and differential equations.
Students will analyse and solve optimization problems using linear programming, integer programming, dynamic programming, inventory models and queuing theory.
Students will conduct research, critically analyse results, and effectively communicate mathematical ideas in written, oral, and computational forms, while modeling uncertainty using fuzzy set theory and decision-making techniques.

Program Specific Outcomes :

PSO 1 – Mathematical Modelling and Analysis : Graduates will be able to design, model, and analyse complex real-world problems using advanced mathematical theories such as graph theory, metric spaces, differential equations, and optimization techniques, integrating analytical and computational approaches.

PSO 2 – Computational and Algorithmic Implementation : Graduates will be proficient in developing and implementing efficient algorithms using MATLAB, C, Python and other computational tools to solve mathematical, engineering, and data-driven problems involving networks, numerical analysis and symbolic computation.

PSO 3 – Intelligent Decision-Making and Applications : Graduates will apply fuzzy logic, finite automata and algorithmic strategies to create intelligent systems for decision-making, uncertainty modeling, and optimization in computer science, engineering and interdisciplinary domains.

Program Highlights

Comprehensive Curriculum – Covers core and advanced topics in Graph Theory, Algorithms, Abstract Algebra, Analysis, Topology, Differential Equations, and Applied Mathematics.
Hands-on Computational Training – Practical exposure to MATLAB, Python, and C programming for solving mathematical and engineering problems.
Integration of Theory and Applications – Emphasis on applying mathematical concepts to real-world domains such as computer networks, optimization, physics, and engineering.
Special Focus on Emerging Areas – Includes fuzzy logic, uncertainty modeling, and computational intelligence for modern research and industrial applications.
Research-Oriented Approach – Encourages project work, seminars, and dissertations to develop analytical thinking, problem-solving skills, and academic writing abilities.

Placement & Internship Info:

Industry Collaboration – Strong linkage with IT companies, research organizations and public sector units for student placement and internships.

Career Opportunities – Graduates are placed in sectors such as software development, data analytics, research and development, banking & finance, education and government services.

Internship Support – Dedicated guidance for securing internships in reputed organizations, including industry-based projects and academic research internships.

Higher Studies Pathways – Students are encouraged and guided to pursue Ph.D. and post-doctoral research opportunities in India and abroad.

Skill Development Programs – Regular workshops, coding bootcamps and research methodology training to enhance employability and career readiness.

Career Prospects:

Graduates of this program have diverse opportunities in academia, research, industry, and government sectors. They can work as:

Data Analysts, Data Scientists and Machine Learning Engineers in IT and analytics firms.

Software Developers and Algorithm Designers in software companies and tech startups.

Mathematical Modellers and Operations Researchers in manufacturing, logistics and supply chain industries.

Research Scientists in government organizations such as ISRO, DRDO, CSIR and research labs.

Educators and Faculty Members in schools, colleges and universities.

Financial Analysts and Risk Managers in banks, insurance companies, and financial institutions.

Competitive Examination Candidates for UPSC, NET, GATE and other national or state-level exams.

Sl. No.	Course Category	Course Code	Course Title	L	T	P	Contact Hours/week	Credit	Full Marks
1.	Program Core-1	MMTCC111	Linear Algebra and Discrete Mathematics	4	0	0	4	4	100
2.	Program Core-2	MMTCC112	Real Analysis and Multivariate Calculus	4	0	0	4	4	100
3.	Program Core-3	MMTCC113	Complex Analysis	4	0	0	4	4	100
4.	Program Core-4	MMTCC114	Ordinary Differential Equations and special function	4	0	0	4	4	100
5.	Basic Science-1	DDEVS	Advanced Computer Skill	4	0	0	4	4	100
Total:				20	0	0	20	20	500

Sl. No.	Course Category	Course Code	Course Title	L	T	P	Contact Hours/week	Credit	Full Marks
1.	Program Core-5	MMTCC121	Abstract Algebra	4	0	0	4	4	100
2.	Program Core-6	MMTCC122	Advanced Topology	4	0	0	4	4	100
3.	Program Core-7	MMTCC123	Integral Equations, Integral Transforms and Calculus of Variations	4	0	0	4	4	100
4.	Basic Science-2	MMTEC121	Operation Research	4	0	0	4	4	100
5.	Basic Science-3	MMTEC122	Fuzzy Set Theory	4	0	0	4	4	100
Total:				20	0	0	20	20	500

Sl. No.	Course Category	Subject Code	Subject Title	L	T	P	Contact Hours/week	Credit	Full Marks
1.	Program Core-8	MMTCC211	Functional Analysis	4	0	0	4	4	100
2.	Program Core-9	MMTCC212	Numerical Analysis and Number Theory	4	0	0	4	4	100
3.	Program Core-10	MMTCC213	Partial Differential Equations and Classical Mechanics	4	0	0	4	4	100
4.	Program Core-11	MMTPR1	Project-I	0	0	6	12	6	100
5.	Basic Science-3	MMTEC211	Fuzzy Logic & Applications	4	0	0	4	4	100
Total:				16	0	6	28	22	500

Sl. No.	Course Category	Subject Code	Subject Title	L	T	P	Contact Hours/week	Credit	Full Marks
1.	Program Core-12	MMTCC221	Probability as a measure and stochastic processes	4	0	0	4	4	100
2.	Program Core-13	MMTCC222	Design and Analysis of Algorithms	4	0	0	4	4	100
3.	Program Core-14	MMTCC223	Lebesgue Measure and Integration	4	0	0	4	4	100
4.	Program Core-15	MMTCC224P	Computer Programming with Practical	2	0	2	6	4	100
5.	Program Core-16	MMTPR2	Project-II	0	0	6	12	6	100

Total:				14	0	8	26	22	500

No fee structure available.

Seminar/Workshop Details:

Regular Academic Seminars – Organized on advanced topics in mathematics, computer science, and interdisciplinary applications to expose students to current research trends.

Hands-on Workshops – Conducted on MATLAB, Python, C programming, LaTeX, and numerical computing for skill enhancement.

Expert Lectures – Sessions by eminent professors, industry professionals and research scientists from reputed institutions in India and abroad.

Fuzzy Logic & AI Training – Specialized workshops on fuzzy set theory, uncertainty modeling, and computational intelligence techniques.

Student-Led Presentations – Encourages students to present papers, case studies, and project findings to build confidence and communication skills.

Collaboration with Professional Bodies – Events in association with AMS, IMS, and other academic societies for broader professional exposure.

HOD Message

Dr. Santonu Debnath

Warm regards,
Assistant Professor, Head of the Department
Department of Mathematics

Dhamma Dipa International Buddhist University

Updates

No updates available.

The undergraduate program in Mathematics provides a strong foundation in mathematical concepts, logical reasoning and problem-solving skills. It integrates core topics such as algebra, calculus, and geometry
with applied areas like mathematical physics and computational techniques. The curriculum is designed to develop analytical thinking and interdisciplinary knowledge, preparing students for academic research, industry roles and further studies in mathematics and related fields.

Programme Educational Objectives (PEOs):

1: Strong Mathematical Foundation: Equip students with fundamental and advanced mathematical concepts to develop analytical and problem-solving skills for academic and professional growth.

2: Interdisciplinary Application: Foster the ability to apply mathematical principles in various scientific,technological and industrial domains to address real-world challenges.

3: Lifelong Learning & Ethics: Encourage continuous learning, research aptitude and ethical responsibility, preparing graduates for higher studies, careers in academia and industry roles.

Program Specific Outcomes (PSOs):

Mathematical Proficiency: Develop a strong understanding of core mathematical concepts, including algebra, calculus, and geometry, to analyze and solve complex problems.
Computational & Analytical Skills: Apply mathematical techniques in computational and scientific domains, enhancing problem-solving abilities in real-world applications.
Research & Career Readiness: Prepare students for higher studies, research, and professional careers by fostering critical thinking, logical reasoning, and interdisciplinary collaboration.

Program Outcomes (POS):

1. Students will acquire fundamental mathematical knowledge to analyze and solve complex problems.

2. Students will develop logical reasoning and critical thinking skills to approach mathematical challenges effectively.

3. Students will gain proficiency in computational techniques and mathematical software for problem solving.

4. Students will apply mathematical concepts in interdisciplinary fields such as science, engineering, and technology.

5. Students will enhance their ability to communicate mathematical ideas clearly and effectively.

6. Students will demonstrate ethical responsibility and integrity in academic and professional settings.

7.Students will develop teamwork and leadership skills for collaborative problem-solving.

8. Students will cultivate a research-oriented mindset to explore advanced mathematical theories and application.

9. Students will engage in lifelong learning to keep up with evolving mathematical knowledge and technological advancements.

10. Students will acquire the skills to apply mathematics in industry, fostering innovation and entrepreneurship.

11. Students will recognize the role of mathematics in addressing environmental, economic, and societal issues.

12. Students will be prepared for higher studies, research, and diverse career opportunities in mathematics and related fields.

Sl. No.	Course Category	Course Code	Course Title	L	P	Contact Hours/week	Credit	Full Marks
1	MCC-1	BMTMCC111	Foundation and classical algebra	4	0	4	4	100
2	MCC-2	BMTMCC112	Matrix theory and 2 dimensional geometry	4	0	4	4	100
3	MEC-1	BPHMEC111	Mathematical Physics & Mechanics	2	2	6	4	100
4	IDC-1	BMTPIDC111	Fundamental of programming for problem solving	3	0	3	3	100
5	SEC-1	BBTSEC111	Mushroom Biology & Production	3	0	3	3	100
6	VAC-1	BPEVAC111	Physical Education and Yoga - I	0	2	4	2	100
Total				16	4	24	20	600

Sl. No.	Category	Code	Course Title	L	P	Hours	Credit	Marks
1	MCC-3	BMTMCC123	Differential Calculus	4	0	4	4	100
2	MCC-4	BMTMCC124	Basic abstract algebra and 3-dimensional geometry	4	0	4	4	100
3	MEC-2	BPHMEC122	Thermal and Physical Properties and Oscillation Waves	2	2	6	4	100
4	AEC-1	-	Communication Skill in English	3	0	3	3	100
5	SEC-2	BZLSEC121	Vermicomposting & Vermiculture	3	0	3	3	100
6	VAC-2	DDEVS	Environmental Science	2	0	2	2	100
Total				18	2	22	20	600

Sl. No.	Category	Subject Code	Subject Title	L	P	Hours	Credit	Marks
1	MCC-5	BMTMCC231	Ordinary and Partial Differential Equation	4	0	4	4	100
2	MCC-6	BMTMCC232	Analysis 1	4	0	4	4	100
3	MEC-3	BCMMEC231	Probability and Statistics	2	2	6	4	100
4	IDC-2	BGGIDC232	Rural Development	3	0	3	3	100
5	AEC-2	BEGAEC232	Effective Technical Communication	3	0	3	3	100
6	SEC-3	BCSSEC233	Basic Computer Skill	2	1	4	3	100
7	VAC-3	BPSVAC233	Indian Constitution	0	2	4	2	100
Total				18	5	28	23	700

Sl. No.	Category	Subject Code	Subject Title	L	P	Hours	Credit	Marks
1	MCC-07	BMTMCC241	Linear Algebra and Complex Analysis - I	4	0	4	4	100
2	MCC-08	BMTMCC242	Integral and Vector Calculus	4	0	4	4	100
3	MEC-4	BCMMEC241	Dynamics and Tensors	4	0	4	4	100
4	IDC-3	BGGIDC243	Environmental Management and Sustainable Development	3	0	3	3	100
5	AEC-3	BEGAEC243	Personality Development Skill for Employment	3	0	3	3	100
6	VAC-4	BPSVAC244	Indian Knowledge System	0	2	4	2	100
7	Internship	-	Summer Internship I	0	2	45	2	100
Total				16	6	67	22	700

Sl. No.	Category	Subject Code	Subject Title	L	P	Hours	Credit	Marks
1	MCC-09	BMTMCC319	Linear Programming and Game Theory	4	0	4	4	100
2	MCC-10	BMTMCC3110	Advance Abstract Algebra and Number Theory	2	2	4	4	100
3	MCC-11	BMTMCC3111	Probability and Statistics	4	0	4	4	100
4	MCC-12	BMTMCC3112	Dynamics and Tensors	4	0	4	4	100
5	MEC-5	-	Minor (Will be Selected by Student)	4	0	4	4	100
6	IDC-3	-	IDC (Will be Selected by Student)	3	0	3	3	100
Total				21	2	23	23	600

Sl. No.	Category	Subject Code	Subject Title	L	P	Hours	Credit	Marks
1	MCC-13	BMTMCC3213	Analysis II	4	0	4	4	100
2	MCC-14	BMTMCC3214	Numerical Analysis and Integral Transformation	4	0	4	4	100
3	MCC-15	BMTMCC3215	Python Programming with Practical	2	2	6	4	100
4	MCC-16	BMTPR1	Project	4	0	8	4	100
5	MEC-6	-	Minor (Will be Selected by Student)	4	0	4	4	100
6	Internship	-	Summer Internship II	0	2	45	2	100
Total				18	4	65	22	600

Sl. No.	Category	Subject Code	Subject Title	L	Hours	Credit	Marks
1	MCC-17	BMTMCC4117	Linear Algebra	4	4	4	100
2	MCC-18	BMTMCC4118	Analysis III	4	4	4	100
3	MCC-19	BMTMCC4119	Complex Analysis	4	4	4	100
4	MCC-20	BMTMCC4120	Ordinary Differential Equations	4	4	6	100
5	MEC-7	-	Minor (Will be Selected by Student)	4	4	4	100
6	MEC-8	-	Minor (Will be Selected by Student)	4	4	4	100
Total				24	24	24	600

Sl. No.	Category	Subject Code	Subject Title	L	P	Hours	Credit	Marks
1	MCC-21	BMTMCC4221	Lebesgue Measure and Integration	4	0	4	4	100
2	MCC-22	BMTMCC4222	Computer Programming with Practical	2	2	6	4	100
3	MCC-23	BMTMCC4223	Abstract Algebra	4	0	4	4	100
4	MCC-24	BMTMCC4224	Integral Equations	4	0	4	4	100
5	MCC-25	BMTMCC4225	Topology	4	0	4	4	100
Total				18	2	22	20	500

Sl. No.	Category	Subject Code	Subject Title	L	P	Hours	Credit	Marks
1	MCC-21	BMTMCC4221	Lebesgue Measure and Integration	4	0	4	4	100
2	MCC-22	BMTMCC4222	Computer Programming with Practical	2	2	4	4	100
3	Project	-	Research Project / Dissertation	0	2	4	12	300
Total				6	2	8	20	500

No fee structure available.

Seminar/Workshop Details:

Regular Academic Seminars – Organized on advanced topics in mathematics, computer science, and interdisciplinary applications to expose students to current research trends.

Hands-on Workshops – Conducted on MATLAB, Python, C programming, LaTeX, and numerical computing for skill enhancement.

Expert Lectures – Sessions by eminent professors, industry professionals and research scientists from reputed institutions in India and abroad.

Fuzzy Logic & AI Training – Specialized workshops on fuzzy set theory, uncertainty modeling, and computational intelligence techniques.

Student-Led Presentations – Encourages students to present papers, case studies, and project findings to build confidence and communication skills.

Collaboration with Professional Bodies – Events in association with AMS, IMS, and other academic societies for broader professional exposure.

HOD Message

Dr. Santonu Debnath

Warm regards,
Assistant Professor, Head of the Department
Department of Mathematics

Dhamma Dipa International Buddhist University

Updates

No updates available.

HOD Message

Dr. Santonu Debnath

Updates

HOD Message

Dr. Santonu Debnath

Updates

HOD Message

Dr. Santonu Debnath

Updates

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