B.Sc

 
S.Y.B.Sc

COURSE DETAILS

Semester III

SR. NO.PAPER
1 Foundation Course Paper – II
2 Any One Group
Physics + Chemistry
Physics + Mathematics
Chemistry + Microbiology
Chemistry + Mathematics

Semester IV

SR. NO.PAPER
1 Foundation Course Paper – II
2 Any One Group
Physics + Chemistry
Physics + Mathematics
Chemistry + Microbiology
Chemistry + Mathematics

COURSE ELIGIBILITY

A candidate for being eligible for admission to the Second Year B.Sc. course commencing from June, 1981, and thereafter must have (1) either kept terms for the First Year B.Sc. course in the academic year commencing from June, 1980 and thereafter and have passed the First Year B.Sc. examination or (2) have kept terms for the First Year B.Sc. course in the academic year commencing from June 1980 and thereafter and have failed at the First Year B.Sc. examination in heads* of passing carryingnot more than 200 marks in which case he will required to appear for the First Year examination in the remaining heads in which he was failed either previously or simultaneously with the Second Year examination, his result of the Second Year examination will not be declared unless he has passed in the remaining subjects of the first Year examination in accordance with the provision of O.213.

OR

A candidate who has passed post S.S.C.(Std.X) Three Year Engineering /Technology Diploma course is eligible for direct admission to Second Year of the B.Sc. degree course.

OR

A candidate who has passed post H.S.C. Diploma (one year after XII Std.) of Maharashtra State Board of Technical Education or A.I.C.T.E. approved or any other recognized Government body in Information Technology/Computer Technology /Computer Engineering /Computer Science /Electrical, Electronics and Video Engineering and Allied Branches/Mechanical and Allied Branches/Production and Allied Branches/Chemical and Allied Branches is eligible for direct admission to the Second Year of the B.Sc. degree course.

Further that such students of Engineering/Technology courses should offer Foundation Course II and any two of the following subject at S.Y.B.Sc.: -
Physics, Chemistry, Mathematics, Statistics, Computer Science Geology, Geography, Economics.
(Note:- In case Student offers Computer Science as one of the subject at S.Y.B.Sc., additional 2 seats per batch of sanctioned Strengh) can be offered for Diploma holders eligible for direct admission to S.Y.B.Sc. (Computer Science).


AUTONOMOUS SYLLABUS 2018-19

Chemistry
Foundation Course
Foundation Course - NSS
Foundation Course - Physical Education


AUTONOMOUS SYLLABUS 2019-20

S.Y.B.Sc. Physics
T.Y.B.Sc

COURSE DETAILS

Semester V

SR. NO.PAPER
1 Any One Major Subject
Chemistry + A.C (Drugs & Dyes)
Physics + A.C.(Electronics Instrumentation)
Microbiology + A.C.(Biotech)
Mathematics + A.C. (Comp.Program & System Analysis)

Semester VI

SR. NO.PAPER
1 Any One Major Subject
Chemistry + A.C (Drugs & Dyes)
Physics + A.C.(Electronics Instrumentation)
Microbiology + A.C.(Biotech)
Mathematics + A.C. (Comp.Program & System Analysis)

AUTONOMOUS SYLLABUS 2018-19

Chemistry
Chemistry - Applied Component - Drug Dyes

AUTONOMOUS SYLLABUS 2019-20

Physics
Physics - Applied Component - Electronic Instrumentation
 

M.Sc

Program Outcomes

SPECIFIC PROGRAMS OFFERED

 
M.Sc Bio-Anlaytical Sciences
M.Sc Chemistry
M.Sc Computer Science

COURSES OFFERED

Semester-I
PGCS101 Analysis of Algorithms and Researching Computing
  • Apply the algorithms and design techniques to solve problems, analyze the complexities of various problems in different domains.
  • Compare two or more algorithms in terms of time and space complexity on growth functions.
  • Evaluation of Algorithms using Dynamic Programming approach, Greedy strategy and Minimum spanning trees (MST) method.
  • Apply the concept of Lower Bound on RSA public-key cryptosystem NP-Completeness and Approximation algorithms.
  • Describe the concept of purpose and products of research,ethics, reviews and quantitative data analysis.
  • Design problem Solution using research methodology.
  • Analyze the Number-Theoretic Algorithms.
  • Apprehend the concept of Research Computing.
  • Comprehend the concept of Minimum spanning trees.
  • Analyze the Greedy Algorithms.
PGCS102 Advanced Networking Concepts
  • Analyzes internet protocols, transfer protocols and routing algorithms.
  • Identify the different types of network topologies, protocols, virtualization technologies primer and central service access .
  • Analyze the concept of device virtualizations.
  • Enumerate the layers of the OSI model and TCP/IP application of manet.
  • Apprehend Ad Hoc networks, Routing protocols and Transmission techniques.
  • Apprehend the different types of network devices and their functions within a network
  • Comprehend the basic concepts of Sensor networks, and how they can be used to assist in network design and implementation
  • Analyze the basic concepts of Broadcasting, multicasting and geocasting.
  • Comprehends RFID techniques
  • Identify the types of MAC protocol issues.
PGCS103 Advanced Database Systems
  • Develop knowledge and understanding of the underlying principles of Distributed Database Management System there Architecture and design strategies in detail.
  • Assimilate your competence in enhancing database models using distributed databases.
  • Evaluate the proper functions of transaction management using ACID properties, Deadlock management, database reliability and parallel query Evaluation .
  • Evaluate the concepts of object oriented database, object query languages
  • Apprehend the practical representation and virtual visualization of Deductive databases and Active databases using Interpretation of model , datalog program and queries.
  • Comprehend the concept of XML Database.
  • Evaluate Query optimization.
  • Recognise Parallel and distributed database systems.
  • Identify New database architectures and query operators.
  • Evaluate the concepts Spatial database , geographical information system.
PGCS104 Robotics and Artificial Intelligence
  • Comprehend the complicated problems using robotics and cybernetics with the help of Artificial intelligence, Actuators, Effectors, Manipulator and freedom of locomotion with deep understanding of robotics.
  • Evaluate Concept of Degree of Freedom.
  • Recognize the concepts of Sensors, implementation of motion and vision for robots and feedback controls.
  • Evaluation of architecture, cost of planning, Algorithms and controls of robots.
  • Determine the concept of Feedback Control.
  • Discuss on Hybrid Control
  • Recognize the coordination and Arbitration of behaviour, Distributed mapping, and navigation.
  • Determine the relation between real brains and simple artificial neural network models.
  • Examine and contrast the most common architectures, for State space search, heuristic search and implementation of Algorithms.
  • Perform various algorithms on Dijkstra’s algorithm, A* algorithm,etc .
Semester-II
PGCS201 Advanced Operating Systems
  • Apprehend the open source operating system, understanding of monolithic, micro, exo Kernels.
  • Evaluate Process and its states and scheduling.
  • Categorize the boot loader with GRUB.
  • Comprehend the mechanisms of memory management and virtual memory in Linux using case studies.
  • Describe concepts of Swapping.
  • Perform page replacement algorithms and design issues for paging systems.
  • Recognize the concept of I/O software and I/O hardware.
  • Analyze the knowledge on open source operating system concepts that includes architecture, deadlock, file system implementations, security and protection mechanism in various versions of Linux.
  • Outline the concept of SQLite, WebKit, OpenGL.
  • Examine the components and aspects of Android operating systems like Android runtime, Application framework, libraries and media frameworks.
  • Perform various Android Application
PGCS202 Design and implementation of Modern Compilers
  • Specify and analyse the lexical, syntactic and semantic structures of advanced language features.
  • Examine the lexical, syntactic and semantic analysis into meaningful phases for a compiler to undertake language translation
  • Outline the concept of scanner, parser, and semantic analyser without the aid of automatic generators.
  • Interprete lexical analyzer and parser generator tool, Finite automata and Regular expression..
  • Comprehend the Automatic construction of efficient parsers, LALR tables and SLR tables.
  • Implement and introduce Syntax-directed translators.
  • Examine the aspects of Tiger compiler like architecture, optimization and building frames in Tiger compiler.
  • Apprehend the principal sources of loop optimization .
  • Understand the concepts of transformation, speeding, alias analysis in Data flow analysis.
  • Differentiate data flow analysis and loop optimization
PGCS203 Cloud Computing (Concepts and Design of Web services)
  • Articulate the main concepts, key technologies, strengths, and limitations of cloud computing and the possible applications for cloud computing
  • Identify the architecture and infrastructure of cloud computing, including service endpoint interface and service implementation bean.
  • Introduce the core issues of cloud computing such as security, privacy,and interoperability.
  • Apprehend the SOAP messaging architecture, client side and server side handler.
  • Comprehend the appropriate cloud computing solutions and recommendations according to the applications used as REST- style web service and ws security.
  • Introduce the concepts of AWS architecture, Amazon VPC, Amazon Cloudfront, Amazon redshift, Amazon Mapreduce.
PGCS204 Cyber and Information Security (Network Security)
  • Evaluate the computer network and information security needs of an organization,types of criminals and Memory address protection.
  • Assess cybersecurity principles of security and memory address protection and database security order to adequately protect an
  • organization critical information and assets.
  • Measure the performance of security systems with IPS and IDS in network security.
  • Evaluate troubleshooting, maintain and update an enterprise-level information security system using vulnerability security management.
  • Implement continuous network monitoring security applied in cloud authentication ,ESX file system security, data security and storage in
  • the cloud.
  • Formulate the deep understanding of mobile systems architecture. Security in cellular VOIP process and wireless networks.
  • Identify some of the factors driving the need for network security.
  • Compare and contrast symmetric and asymmetric encryption systems and their vulnerability to attack, and explain the characteristics of hybrid systems.
  • Manage multiple operating systems, systems software, network services, and security, and demonstrate analytical skills.
  • Compare and contrast symmetric and asymmetric encryption systems and their vulnerability to attack.
PGCS205 Business Intelligence and Big Data Analytics (Business Intelligence)
  • Analyze the operational and decision support system.
  • Identify the different data preprocessing techniques.
  • Evaluate the impact of use and information using knowledge discovery in databases and KDD process models.
  • Analyzes the concepts of business data warehousing and data marts.
  • Apprehend the OLAP and OLTP systems warehouse principles of data modeling and data cube operations.
  • Analyze the logical and technical architecture of data warehouses.
  • Construct the data modelling concepts like star schema and snowflake schema.
  • Construct data models and prototypes needed to gain stakeholder support or achieve business objectives and incorporate data profiling and quality standards.
  • Incorporate the data mining concepts with the help of Apriori algorithm.
  • Analyze the data mining issues like support, confidence, lift and conviction.
PGCS206 Machine Intelligence (Fundamentals of Machine Intelligence)
  • Evaluate the concepts of advanced machine learning techniques ,algorithms, how to package and deploy your models and statistical learning.
  • Apprehend the various standard methods using simple linear regressions and KNN methods.
  • Comprehend the Resampling methods, selection, shrinkage and dimension reduction methods.
  • Assimilate the Regression models and learn the non-linear models using functions, splines.
  • Incorporate the concepts of support vector machines, Classifiers,unsupervised learning, component analysis and clusterings methods.
  • Classify the concept of Clustering
  • Analyze the concept of Tree-based methods
Semester-III
PGCS301 Ubiquitous Computing
  • Interpretation of major concepts and components of ubicom systems, ubiquitous computing applications and holistic frameworks.
  • Incorporate the usage of smart cards, device networks and human computer devices.
  • Describe the concept of context, representation and modeling of context
  • Explain the technologies for sensing context, location tracking services.
  • Identify user interface issues in pervasive computing.
  • Evaluate the concepts of implanted devices and human centered design.
  • Comprehend the principles of distributed computing, mobile computing and their applications using sensors controllers.
  • Describe the role of Sensors and MEMS in development of Context Aware Systems.
  • Assimilate wireless networks, alphanumeric networks and data network access.
  • Analyze and estimate the impact of pervasive computing on future computing applications and society
PGCS302 Social Network Analysis
  • Analyze the area of social network concepts, relationship analysis and relationships using algorithms.
  • Use relational algorithms like dijkstra’s algorithm using top-down and bottom up approaches.
  • Apprehend how network analysis can contribute to increasing knowledge about diverse aspects of society using local and global centrality, Approaches and google page rank algorithm.
  • Analyse social network data using various software packages and similarity and structural equivalences.
  • Ascertaining mode networks, Bi-pirtaite data structure and SVD analysis.
  • Compare different Similarity and dissimilarity distance measuring approaches
PGCS303 Cloud Computing –II (Cloud Computing Technologies)
  • Articulate the main concepts, key technologies, strengths, and limitations,Characteristics, taxonomy, virtualization and cloud computing
  • Identify the architecture and infrastructure of cloud computing,including SaaS, PaaS, IaaS, public cloud, private cloud, hybrid cloud, etc.
  • Apprehend the various frameworks and platforms in cloud technologies using web services and data intensive computing.
  • Attempt to generate new ideas and innovations in cloud computing.
  • Collaboratively research and write a research paper, and present the research online.
  • Incorporating the various software architecture for the betterment of practical knowledge using enterprise applications and cloud applications.
PGCS304 Cyber and Information Security- II (Cyber Forensics)
  • Define the objectives of computer forensics in law enforcements, evidence, case studies, investigation and computer forensic techniques.
  • Comprehend the knowledge of various types of technologies in computer forensics along with malware and internet tracing methods.
  • Assimilate security and wireless technologies along with encryption methods and identity theft.
  • Apprehend the various techniques of data recovery data hiding and evidence collection rules.
  • Identification and analysis of data for reconstructing past events
  • Incorporate the concepts of authentication and verification of cyber forensics
  • Demonstrate network based evidence using principles, protocols and various acquisition methods.
  • Comprehend the various network forensics with the help of NIDS and NIPS systems.
  • Determine the concepts of mobile forensics using identification and data interception using web proxies and evidence analysis .
  • Establish the knowledge of Mobile phone Forensics.
PGCS305 Business Intelligence and Big Data Analytics –II
  • Identification of the characteristics of datasets and compare the trivial data and big data for various applications.
  • Evaluate to select and implement competitive techniques and computing environments that are suitable for the analytic process and tools.
  • Expand the concept of neural network and fuzzy logic.
  • Incorporating integrated machine learning libraries and mathematical and statistical tools with modern technologies like hadoop and mapreduce.
  • Comperhand the various algorithms using mapreduce.
  • Illustrate extension to map reduce & workflow system.
  • Apply shingling of documents using various applications such as jaccard's similarity and methods of high degree of similarity.
  • Explain similarity-preserving, locality sensitive hashing documents its function.
  • Recognize the stream concepts, decaying windows, Real time analytics platform(RTAP)
  • Introduce Stream data model and architecture, Stream computing,Sampling data in a stream.
PGCS306 Machine Learning –II (Advanced Machine Learning)
  • Understanding the probability theory, mixture models and EM algorithm.
  • Apprehending the key concepts, tools and approaches for pattern recognition on complex data sets using kernel inside GLM’s,
  • comparison of discriminative kernel methods.
  • Define the Kernel methods for handling unidirectional graphical models, markov and hidden markov models.
  • Comprehending State-of-the-art as Monte Carlo inference, sampling from standard distributions.
  • Imbibing concepts and the motivations behind different learning frameworks, structure learning knowledge discovery, tree structures and Gaussian graphical models.
  • Define the Concept of Deep learning
Semester-IV
PGCS401 Simulation and Modeling
  • Comprehend the need of simulation and time to simulate for the development process to initiate the real problem using framework and development of conceptual model.
  • Understand the principle and techniques of simulation methods and intellectual concepts of verification and validation of models, dealing
  • with initialization of models.
  • Apprehend the components of continuous and discrete systems,communication between agents and building agent based models to simulate any system from different fields.
  • Assimilate simulation methods and select the suitable technique on the problems, design and behaviors of models.
  • Understanding simulate any discrete system using queuing systems,statecharts at runtime and statecharts for dynamic objects.
  • Imbibe the work effectively with 3D animations and randomness in models.
  • Analyze the conceptual models.
  • Interpret the concept of Dynamics Systems.
  • Apprehend the concept of Randomness in Models.
  • Comprehend the concept of Virtual and real time.
PGCS402 Cloud Computing –III (Building Clouds and Services)
  • Imbibe the concepts of cloud computing standards, security threats and security mechanisms.
  • Understand various concepts of PKI, IAM, SSO and hardened virtual server images.
  • Apprehend the concepts of network perimeter, virtual servers, cloud storage devices in cloud computing.
  • Comprehend the various concepts of cloud computing like Cloud Mechanisms, Remote Administration, Resource, SLA and Billing
  • Management System.
  • Assimilate the applications and concepts of cloud architectures.
  • Evaluate the process of cloud delivering models, metrics and Cloud Management Mechanisms.
PGCS403 Cyber and Information Security- II (Cryptography and Crypt Analysis)
  • Recognize the information of elementary number theory,algorithms and theorems.
  • Define the congruence properties,linear congruence in detail.
  • Understand the concepts of quadratic residues and reciprocity.
  • Expand the concept of Shift Cipher, Substitution Cipher, Affine Cipher, Vigenère Cipher, Vermin Cipher, Hill Cipher, Permutation Cipher, Stream Cipher.
  • Illustrate the Block Ciphers, Algorithm Modes, DES, Double DES, Triple DES.
  • Comprehend cryptographic hash functions,secure hash algorithms.
  • Implementing the concepts of RSA algorithms, Public Key Cryptosystems: The idea of public key Cryptography.
  • Explain the cryptosystems, Diffie-Hellman Key Agreement and Knapsack problem.
  • Absorbing key agreement and key agreement scheme, public key.infrastructures and models privacy in cryptosystems.
  • Classify the Trust model:Networked PKIs, The web browser Model, Pretty Good Privacy.
PGCS404 Business Intelligence and Big Data Analytics –III (Intelligent Data Analysis)
  • Comprehend the clustering techniques and portioning algorithms using K-means and k-medoids, clustering stream and parallelism.
  • Understand the implementation of AGNES and DIANA algorithms.
  • Apprehend the distance based algorithms, KNN methods, trees, document classification, regression models and trees.
  • Determine the concept of Eigen value, Eigen vectors and decompositions .
  • Define link analysis, pagerank algorithm.
  • Classify Recommendation Systems
  • Analyze the Chi Squared Automatic Interaction Detector.
  • Apprehend the concept of Classification And
  • Regression Tree.
  • Interpret the Evaluation techniques.
  • Comprehend the implementation of CLARA and CLARANS.
PGCS405 Machine Learning –III (Computational Intelligence)
  • Comprehend the Artificial neural networks and reinforcement in machine learning.
  • Understand the concepts of genetics algorithms and working in machine learning.
  • Apprehend the concepts of evolutionary strategies and programming.
  • Imbibe the concepts of particle swarm optimization, and advanced topics and applications in computational swarm intelligence.
  • Assimilate the Artificial immune systems and fuzzy systems.
  • Define the concept of Rough Sets.
  • Interpret the concept of Fuzzy Sets, Fuzzy Logic
  • and Reasoning, Fuzzy Controllers.
  • Analyze the Particle Swarm Optimization(PSO).
  • Interpret between Supervised Learning Neural Networks, Unsupervised Learning Neural Networks,
  • Analyze the concept of Ant Algorithms.
M.Sc Information Techology
M.Sc Mathemetics
M.Sc Microbiology
M.Sc Physics
 
 
M.Sc. [Part-I]
M.Sc. [Part-II]

COURSE DETAILS

M. Sc. Chemistry

Semester III

Semester IV

Organic Chemistry Organic Chemistry
Inorganic Chemistry Inorganic Chemistry
Analytical Chemistry Analytical Chemistry

M. Sc. Microbiology

Semester III

Semester IV

Tools and Techniques : Research Methodology Tools and Techniques: Biomolecular Analysis
Food Microbiology Pharmaceutical Microbiology
Advances In   Biotechnology Advances in Biotechnology
Applied and Environmental Microbiology Applied and Environmental Monitoring & Management

M. Sc. Physics

Semester III

Semester IV

Condensed Matter Physics Computational Methods
Nuclear & Particle Physics Experimental Techniques
Material Science – I (Imperfection In Crystal) Material Science – III(Mechanical Properties and Liquid Crystal)
Material Science – II(Physics of Metals & Alloys) Material Science – IV(Special Materials)
Material Science Lab – I Material Science Lab – II
Material Science Project work - I Material Science Project work - II

AUTONOMOUS SYLLABUS


Physics
Inorganic Chemistry
Organic Chemistry
Analytical Chemistry
 

B.Sc (Computer Science)

F.Y.B.Sc. (Computer Science)
S.Y.B.Sc. (Computer Science)

COURSE DETAILS

Semester III

SR. NO.PAPER
1 Discrete Mathematics
2 Object Oriented Design using UML and Python
3 Data Structures and Algorithms Using Python

Semester IV

SR. NO.PAPER
1 Operating System and Linux
2 Java Programming
3 Web Technologies
T.Y.B.Sc. (Computer Science)

COURSE DETAILS

Semester V

SR. NO.PAPER
1 Data Communication, Networking & Security-I
2 Advanced Java – I
3 Operating Systems
4 Database Management System - II

Semester VI

SR. NO.PAPER
1 Data Communication, Networking & Security-II
2 Advanced Java - II
3 Linux
4 Software Engineering

M.Sc (Computer Science)

M.Sc. (Computer Science) [Part-I]
M.Sc. (Computer Science) [Part-II]

COURSE DETAILS

Semester III

SR. NO.PAPER
1 Ubiquitous Computing
2 Social Network Analysis
3 Elective I - Track A: Cloud Computing –II (Cloud Computing Technologies)
4 Elective I - Track B: Cyber and Information Security- II (Cyber Forensics)
5 Elective II - Track C: Business Intelligence and Big Data Analytics –II (Mining Massive Data sets )
6 Elective II - Track D: Machine Learning –II (Advanced Machine Learning)

Semester IV

SR. NO.PAPER
1 Simulation and Modeling
2 Specialization - Track A: Cloud Computing –III (Building Clouds and Services)
3 Specialization - Track B: Cyber and Information Security- II (Cryptography and Crypt Analysis)
4 Specialization - Track C: Business Intelligence and Big Data Analytics –III (Intelligent Data Analysis)
5 Specialization - Track D: Machine Learning –III (Computational Intelligence)

B.Sc (Information Technology)

F.Y.B.Sc (Information Technology)

Autonomous Syllabus 2018-2019

FYBSc Information Technology

S.Y.B.Sc (Information Technology)

COURSE DETAILS

Semester III

SR. NO.PAPER
1 Logic and Discrete Mathematics
2 Computer Graphics
3 Advanced SQL
4Object Oriented Programing with C++
5 Modern Operating System

Semester IV

SR. NO.PAPER
1 Software Engineering
2 Multimedia
3Java & Data Structure
4 Quantitative Techniques
5 Embedded System

Autonomous Syllabus 2018-2019

SYBSc Information Technology

T.Y.B.Sc (Information Technology)

COURSE DETAILS

Semester V

SR. NO.PAPER
1Network Security
2 ASP.net with C#
3 Software Testing
4Advanced Java
5 Linux Administration

Semester VI

SR. NO.PAPER
1Internet Technology
2 Digital Signal &System
3Data Warehousing
4 Project Management
5 Project

M.Sc (Information Technology)

M.Sc. (Information Technology) [Part-I]

Autonomous Syllabus 2018-2019

M.Sc. (Information Technology) [Part-I]

M.Sc. (Information Technology) [Part-II]

COURSE DETAILS

Semester III

SR. NO.PAPER
1 Embedded System
2 Information Security Management
3 Elective 1
Virtualization
Artificial Neural Network
4Elective 2
Digital image Processing
Ethical Hacking

Semester IV

SR. NO.PAPER
1 Artificial Intelligence
2 IT Infrastuctrure Management
3Elective 1
Intelligence System
Real Time Embedde System
Computer Forensies
4 Elective 2
Design Embedde Control System
Advanced Image Processing
Cloud management Project

Autonomous Syllabus 2018-2019

M.Sc. (Information Technology) [Part-II]

B.Sc (Biotechnology)

F.Y.B.Sc (Biotechnology)
S.Y.B.Sc (Biotechnology)

COURSE DETAILS

Semester III

SR. NO.PAPER
1 Immunology & medical Biotechnology
2 Biochemistry
3 Genetics and Molecular Biology

Semester IV

SR. NO.PAPER
1 Instrumentation & Fermentation Technology
2 Ecology & Environmental Biotechnology
3Molecular Biology and Instrumentation
SYBSc Biotechnology Syllabus 2019-20
T.Y.B.Sc (Biotechnology)

COURSE DETAILS

Semester V

SR. NO.PAPER
1Cell Biology and Medical Biotechnology
2 Biochemistry Immunology and Instrumentation
3 Genetics and Molecular Biology
4Industrial Biotechnology

Semester VI

SR. NO.PAPER
1Cell Biology, Medical Biotechnology and Biostatistics
2 Biochemistry Immunology and Instrumentation
3Molecular Biology & Bioinformatics
4 Industrial Biotechnology
T.Y.B.Sc Biotechnology Syllabus 2020-21

M.Sc (Bioanalytical Sciences)

M.Sc. (Bioanalytical Sciences) [Part-I]

Autonomous Syllabus 2019-2020

MSc Part-I Bio-Analytical Syllabus
M.Sc. (Bioanalytical Sciences) [Part-II]

Autonomous Syllabus 2019-2020

MSc Part-II Bio-Analytical Syllabus

B.Sc (Microbiology)

F.Y.B.Sc (Microbiology)

Autonomous Syllabus 2018-2019

F.Y.B.Sc. (Microbiology)

S.Y.B.Sc (Microbiology)

Autonomous Syllabus 2019-2020

S.Y.B.Sc. (Microbiology)

T.Y.B.Sc (Microbiology)

Autonomous Syllabus 2018-2019

T.Y.B.Sc. (Microbiology)

M.Sc (Microbiology)

M.Sc I (Microbiology)

Autonomous Syllabus 2019-2020

M.Sc.I (Microbiology)

M.Sc II (Microbiology)

Autonomous Syllabus 2019-2020

M.Sc.II (Microbiology)

B.Sc (Mathematics)

F.Y.B.Sc (Mathematics)

Autonomous Syllabus 2019-2020

F.Y.B.Sc (Mathematics)

S.Y.B.Sc (Mathematics)

Autonomous Syllabus 2019-2020

S.Y.B.Sc (Mathematics)

T.Y.B.Sc (Mathematics)

M.Sc (Mathematics)

M.Sc I (Mathematics)

Autonomous Syllabus 2019-2020

M.Sc I (Mathematics)

M.Sc II (Mathematics)

Autonomous Syllabus 2019-2020

M.Sc I (Mathematics)

B.Voc (Food Technology)

F.Y.B. Voc.
S.Y.B. Voc.
T.Y.B. Voc.

ADMLT

PhD