Comprehensive Course Listing
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | SC101 | General Chemistry | 3-1-0-4 | - |
| 1 | SC102 | General Physics | 3-1-0-4 | - |
| 1 | SC103 | General Biology | 3-1-0-4 | - |
| 1 | SC104 | Mathematics I | 3-1-0-4 | - |
| 1 | SC105 | Chemistry Lab | 0-0-3-1 | - |
| 1 | SC106 | Physics Lab | 0-0-3-1 | - |
| 1 | SC107 | Biology Lab | 0-0-3-1 | - |
| 1 | SC108 | Mathematics Lab | 0-0-3-1 | - |
| 2 | SC201 | Organic Chemistry | 3-1-0-4 | SC101 |
| 2 | SC202 | Cell Biology | 3-1-0-4 | SC103 |
| 2 | SC203 | Thermodynamics | 3-1-0-4 | SC102 |
| 2 | SC204 | Calculus | 3-1-0-4 | SC104 |
| 2 | SC205 | Organic Chemistry Lab | 0-0-3-1 | SC101 |
| 2 | SC206 | Cell Biology Lab | 0-0-3-1 | SC103 |
| 2 | SC207 | Thermodynamics Lab | 0-0-3-1 | SC102 |
| 2 | SC208 | Calculus Lab | 0-0-3-1 | SC104 |
| 3 | SC301 | Protein Chemistry | 3-1-0-4 | SC201 |
| 3 | SC302 | Environmental Impact Assessment | 3-1-0-4 | SC203 |
| 3 | SC303 | Quantum Physics | 3-1-0-4 | SC203 |
| 3 | SC304 | Data Analytics | 3-1-0-4 | SC204 |
| 3 | SC305 | Protein Chemistry Lab | 0-0-3-1 | SC201 |
| 3 | SC306 | Environmental Lab | 0-0-3-1 | SC203 |
| 3 | SC307 | Quantum Physics Lab | 0-0-3-1 | SC203 |
| 3 | SC308 | Data Analytics Lab | 0-0-3-1 | SC204 |
| 4 | SC401 | Bioinformatics | 3-1-0-4 | SC301 |
| 4 | SC402 | Marine Ecology | 3-1-0-4 | SC302 |
| 4 | SC403 | Nanotechnology | 3-1-0-4 | SC303 |
| 4 | SC404 | Scientific Writing | 3-1-0-4 | SC304 |
| 4 | SC405 | Bioinformatics Lab | 0-0-3-1 | SC301 |
| 4 | SC406 | Marine Ecology Lab | 0-0-3-1 | SC302 |
| 4 | SC407 | Nanotechnology Lab | 0-0-3-1 | SC303 |
| 4 | SC408 | Scientific Writing Lab | 0-0-3-1 | SC304 |
| 5 | SC501 | Advanced Biochemistry | 3-1-0-4 | SC401 |
| 5 | SC502 | Climate Change and Sustainability | 3-1-0-4 | SC402 |
| 5 | SC503 | Computational Physics | 3-1-0-4 | SC403 |
| 5 | SC504 | Statistical Modeling | 3-1-0-4 | SC404 |
| 5 | SC505 | Advanced Biochemistry Lab | 0-0-3-1 | SC401 |
| 5 | SC506 | Climate Change Lab | 0-0-3-1 | SC402 |
| 5 | SC507 | Computational Physics Lab | 0-0-3-1 | SC403 |
| 5 | SC508 | Statistical Modeling Lab | 0-0-3-1 | SC404 |
| 6 | SC601 | Drug Design | 3-1-0-4 | SC501 |
| 6 | SC602 | Conservation Biology | 3-1-0-4 | SC502 |
| 6 | SC603 | Quantum Computing | 3-1-0-4 | SC503 |
| 6 | SC604 | Machine Learning | 3-1-0-4 | SC504 |
| 6 | SC605 | Drug Design Lab | 0-0-3-1 | SC501 |
| 6 | SC606 | Conservation Biology Lab | 0-0-3-1 | SC502 |
| 6 | SC607 | Quantum Computing Lab | 0-0-3-1 | SC503 |
| 6 | SC608 | Machine Learning Lab | 0-0-3-1 | SC504 |
| 7 | SC701 | Advanced Materials | 3-1-0-4 | SC601 |
| 7 | SC702 | Marine Conservation | 3-1-0-4 | SC602 |
| 7 | SC703 | Neural Networks | 3-1-0-4 | SC603 |
| 7 | SC704 | Scientific Research Methods | 3-1-0-4 | SC604 |
| 7 | SC705 | Advanced Materials Lab | 0-0-3-1 | SC601 |
| 7 | SC706 | Marine Conservation Lab | 0-0-3-1 | SC602 |
| 7 | SC707 | Neural Networks Lab | 0-0-3-1 | SC603 |
| 7 | SC708 | Scientific Research Lab | 0-0-3-1 | SC604 |
| 8 | SC801 | Capstone Project | 3-1-0-4 | SC701 |
| 8 | SC802 | Research Thesis | 3-1-0-4 | SC702 |
| 8 | SC803 | Scientific Communication | 3-1-0-4 | SC703 |
| 8 | SC804 | Professional Development | 3-1-0-4 | SC704 |
| 8 | SC805 | Capstone Project Lab | 0-0-3-1 | SC701 |
| 8 | SC806 | Research Thesis Lab | 0-0-3-1 | SC702 |
| 8 | SC807 | Scientific Communication Lab | 0-0-3-1 | SC703 |
| 8 | SC808 | Professional Development Lab | 0-0-3-1 | SC704 |
Advanced Departmental Elective Courses
Advanced departmental elective courses in the Bachelor of Science program at Sri Subbaiah Degree College Anantapur are designed to provide students with specialized knowledge and practical skills in their chosen fields. These courses are offered in the later semesters and are tailored to meet the needs of students who wish to pursue advanced research or professional careers in specific areas of science.
The course 'Advanced Biochemistry' delves into the molecular mechanisms of biological processes, focusing on enzyme kinetics, protein structure-function relationships, and metabolic pathways. Students engage in laboratory experiments that involve protein purification, enzyme assay techniques, and structural analysis using advanced spectroscopic methods. The course emphasizes the application of biochemistry in drug design and biotechnology, preparing students for careers in pharmaceutical and biotech industries.
'Climate Change and Sustainability' explores the scientific, social, and economic dimensions of climate change. Students analyze climate data, assess environmental impacts, and develop strategies for sustainable development. The course includes fieldwork and research projects that address real-world challenges such as carbon emissions, renewable energy, and ecosystem conservation. This course is particularly relevant for students interested in environmental science, policy development, and sustainability consulting.
'Computational Physics' introduces students to numerical methods and computational modeling in physics. The course covers topics such as numerical integration, Monte Carlo simulations, and finite element methods. Students use programming languages such as Python and MATLAB to solve complex physics problems and simulate physical phenomena. This course is ideal for students who wish to pursue careers in computational research, data analysis, or engineering.
'Statistical Modeling' focuses on the application of statistical techniques in scientific research. Students learn to design experiments, analyze data, and interpret results using statistical software such as R and SPSS. The course covers probability distributions, hypothesis testing, regression analysis, and time series modeling. This course is essential for students interested in data science, research, and analytics.
'Drug Design' explores the principles and techniques of rational drug design and development. Students study molecular docking, structure-based drug design, and pharmacophore modeling. The course includes laboratory sessions where students synthesize and test potential drug compounds. This course is ideal for students who wish to pursue careers in pharmaceutical research, drug development, or medicinal chemistry.
'Conservation Biology' examines the principles and practices of biodiversity conservation and ecosystem management. Students learn about endangered species, habitat restoration, and conservation policies. The course includes fieldwork and research projects that address issues such as deforestation, wildlife protection, and sustainable agriculture. This course is relevant for students interested in environmental conservation, wildlife management, and policy development.
'Quantum Computing' introduces students to the principles and applications of quantum computing. The course covers quantum algorithms, quantum error correction, and quantum information theory. Students gain hands-on experience with quantum computing platforms and programming languages such as Qiskit and Cirq. This course is ideal for students who wish to pursue careers in quantum research, cybersecurity, or advanced computing.
'Machine Learning' explores the algorithms and techniques used in artificial intelligence and data science. Students learn about supervised and unsupervised learning, neural networks, and deep learning. The course includes practical projects where students develop machine learning models using Python and TensorFlow. This course is essential for students interested in AI research, data science, or software development.
'Advanced Materials' focuses on the structure, properties, and applications of advanced materials. Students study nanomaterials, composite materials, and smart materials. The course includes laboratory sessions where students synthesize and characterize materials using advanced techniques such as X-ray diffraction and electron microscopy. This course is ideal for students who wish to pursue careers in materials science, engineering, or research.
'Neural Networks' explores the architecture and applications of artificial neural networks in scientific computing. Students learn about deep learning, convolutional neural networks, and recurrent neural networks. The course includes practical projects where students implement neural network models using Python and TensorFlow. This course is relevant for students interested in AI research, data science, or computational biology.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in the belief that real-world problem-solving is essential for developing scientific expertise and innovation. Project-based learning is integrated throughout the curriculum to provide students with opportunities to apply theoretical knowledge to practical challenges.
Mini-projects are mandatory components of the program, beginning in the second year. These projects are designed to be interdisciplinary, allowing students to explore connections between different scientific disciplines. Students work in teams to investigate a specific problem, conduct research, and develop solutions. The projects are evaluated based on criteria such as scientific rigor, creativity, presentation skills, and teamwork.
The final-year thesis/capstone project is a significant component of the program. Students select a research topic in consultation with a faculty mentor and conduct an in-depth investigation. The project involves literature review, experimental design, data collection, and analysis. Students present their findings in a written thesis and an oral presentation. The capstone project is evaluated by a panel of faculty members and is a requirement for graduation.
Project selection is guided by student interests, faculty expertise, and industry needs. Students are encouraged to propose their own research ideas or to work on projects suggested by faculty members. The department provides resources and support for project development, including access to laboratories, equipment, and research databases.
Faculty mentors play a crucial role in guiding students through the project process. They provide expertise, feedback, and encouragement throughout the project lifecycle. The department maintains a mentorship program that pairs students with faculty members based on their research interests and career aspirations.