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BE in Computer Science Engineering at Amruta Institute of Engineering and Management Sciences
Ramanagara, Karnataka
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About the Specialization
What is Computer Science & Engineering at Amruta Institute of Engineering and Management Sciences Ramanagara?
This Computer Science & Engineering program at Amruta Institute of Engineering and Management Sciences focuses on foundational computing principles and advanced technological applications. It prepares students for the dynamic Indian IT industry, emphasizing problem-solving, algorithm design, and software development. The curriculum aligns with the rapidly evolving demands of digital transformation and innovation in India.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and logical reasoning seeking entry into the technology sector. It also caters to individuals passionate about coding, software development, data science, and artificial intelligence, aspiring to contribute to India''''s burgeoning tech ecosystem. Prerequisites include a solid grasp of basic science and strong analytical skills.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Software Developer, Data Analyst, AI Engineer, and Cybersecurity Specialist. Entry-level salaries range from INR 3.5-6 LPA, growing significantly with experience. The program aligns with industry certifications like AWS, Azure, and Google Cloud, enhancing employability in major Indian IT hubs.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate consistent time to practice core programming concepts in C/Java. Focus on understanding data structures and algorithms deeply, as these are critical building blocks for all advanced computer science topics. Regularly solve problems on online platforms to build logic and problem-solving skills.
Tools & Resources
GeeksforGeeks, HackerRank, LeetCode (for basics), NPTEL courses on Data Structures
Career Connection
Strong fundamentals are the bedrock for coding interviews and essential for securing placements in core software development roles at companies like TCS, Infosys, Wipro, and various startups.
Build a Strong Mathematical Base- (Semester 1-3)
Focus intently on Discrete Mathematics, Linear Algebra, and Calculus. These subjects provide the theoretical underpinning for algorithms, data science, and artificial intelligence. Understand proofs, logic, and numerical techniques thoroughly, seeking help promptly for any confusion.
Tools & Resources
Khan Academy, MIT OpenCourseware (Mathematics), Textbooks by C.L. Liu, Kenneth Rosen
Career Connection
A robust mathematical background is indispensable for roles in data science, machine learning, and research, differentiating candidates in competitive analytical positions.
Engage in Technical Communication- (Semester 1-2)
Actively participate in communicative English and technical writing exercises. Develop strong verbal and written communication skills by presenting ideas, writing clear reports for lab work, and participating in group discussions. This prepares for professional interactions and documentation.
Tools & Resources
Grammarly, Toastmasters (if available), Technical report writing guidelines
Career Connection
Effective communication is crucial for team collaboration, client interaction, and successful project delivery in any IT role, making graduates well-rounded professionals.
Intermediate Stage
Undertake Practical Mini-Projects and Internships- (Semester 3-5)
Beyond lab exercises, initiate small personal projects or contribute to open-source initiatives to apply learned concepts (e.g., build a simple web app, a command-line tool). Actively seek summer internships or virtual internships to gain early industry exposure and practical experience.
Tools & Resources
GitHub, Kaggle (for data science projects), LinkedIn for internship searches, College''''s placement cell
Career Connection
Practical projects and internships demonstrate problem-solving abilities and real-world application of skills, significantly boosting resume value for placements in product-based companies.
Specialize in a Niche Skillset- (Semester 4-6)
Identify an area of interest within CSE (e.g., Web Development, Data Science, Cyber Security, AI/ML) and start deep-diving into it. Utilize online courses and certifications to build specialized skills beyond the curriculum. This helps in tailoring your profile for specific industry demands.
Tools & Resources
Coursera, Udemy, edX, NPTEL advanced courses, AWS/Azure/Google Cloud certifications
Career Connection
Specialized skills make you a more attractive candidate for specific job roles and industries, potentially leading to higher-paying positions in your chosen domain.
Actively Network and Participate in Tech Events- (Semester 3-6)
Attend college tech fests, workshops, seminars, and industry events. Network with peers, faculty, and industry professionals. Join college clubs related to your interests (e.g., coding club, AI club) to collaborate and learn from others. These interactions open doors to opportunities.
Tools & Resources
College technical clubs, Meetup groups in Bangalore, LinkedIn for professional networking
Career Connection
Networking can lead to mentorship, internship opportunities, and even direct job referrals, which are invaluable for career advancement in the Indian tech landscape.
Advanced Stage
Focus on Capstone Project & Portfolio Building- (Semester 7-8)
Dedicate significant effort to the final year project (Phase I & II). Choose a challenging problem, develop a robust solution, and document it thoroughly. Simultaneously, curate a strong online portfolio showcasing your projects, coding skills, and specialized expertise.
Tools & Resources
GitHub profile, Personal website/blog, Project documentation tools
Career Connection
A strong capstone project and well-maintained portfolio are often the most impactful elements for securing top placements, especially with product-based companies and R&D roles.
Intensive Placement Preparation- (Semester 6-8)
Begin rigorous preparation for placement interviews early. This includes mastering aptitude tests, practicing coding interview questions (data structures, algorithms), and honing soft skills for group discussions and HR rounds. Focus on company-specific preparation for target firms.
Tools & Resources
InterviewBit, Glassdoor for company interview experiences, Mock interview sessions, Aptitude books
Career Connection
Thorough preparation directly translates into higher chances of cracking interviews and securing lucrative job offers from leading companies in India and MNCs.
Explore Post-Graduation Avenues- (Semester 7-8)
For those interested in higher studies or entrepreneurship, start exploring options like M.Tech, MS abroad, or startup incubators. Prepare for competitive exams like GATE, GRE, or research proposal writing. Connect with alumni who have pursued similar paths for guidance.
Tools & Resources
GATE preparation platforms, GRE/TOEFL resources, University websites for M.Tech/MS, College entrepreneurship cell
Career Connection
Strategic planning for post-graduation opens doors to advanced research roles, specialized engineering positions, or leading innovative ventures, providing diverse career trajectories.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with Physics and Mathematics as compulsory subjects along with Chemistry/Biology/Biotechnology/Technical Vocational as one of the optional subjects with minimum 45% marks (40% for SC/ST/OBC category candidates) in aggregate from a recognized Board/University.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSL101 | Calculus and Differential Equations | Core | 3 | Differential Calculus, Integral Calculus, Partial Derivatives, Multiple Integrals, Differential Equations |
| PCL101 | Engineering Physics | Core | 3 | Quantum Mechanics, Laser Physics, Optical Fibers, Semiconductor Physics, Nano-materials |
| PCL102 | Engineering Physics Lab | Lab | 1 | Measurement techniques, Optical experiments, Semiconductor device characteristics, Acoustics, Magnetic properties |
| EGH101 | Communicative English | Core | 2 | Basic English Grammar, Reading Comprehension, Writing Skills, Verbal Communication, Listening Skills |
| CSL101 | Programming for Problem Solving | Core | 3 | Introduction to C Programming, Control Structures, Functions and Arrays, Pointers and Structures, File Handling |
| CSL102 | Programming for Problem Solving Lab | Lab | 1 | C program implementation, Debugging techniques, Algorithm design, Data structure basics, Problem-solving exercises |
| HSS101 | Universal Human Values | Ability Enhancement | 1 | Ethics and Morality, Human-Nature Relationship, Professional Ethics, Values in Education, Holistic Development |
| MED101 | Engineering Graphics | Core | 3 | Orthographic Projections, Sectioning of Solids, Isometric Projections, AutoCAD Basics, Development of Surfaces |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSL201 | Linear Algebra and Fourier Series | Core | 3 | Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors, Fourier Series, Z-Transforms |
| CHL201 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion and Control, Polymers, Water Technology, Fuels and Combustion |
| CHL202 | Engineering Chemistry Lab | Lab | 1 | Volumetric analysis, Instrumental analysis, Water quality tests, Synthesis of polymers, Corrosion studies |
| CSL201 | Introduction to Data Science | Core | 3 | Data Collection & Preprocessing, Exploratory Data Analysis, Data Visualization, Statistical Methods, Machine Learning Basics |
| CSL202 | Introduction to Data Science Lab | Lab | 1 | Python programming for Data Science, Data manipulation with Pandas, Data visualization with Matplotlib/Seaborn, Basic machine learning algorithms, Case studies |
| EEL201 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Magnetic Circuits, Transformers, DC Machines |
| EEL202 | Basic Electrical Engineering Lab | Lab | 1 | Circuit laws verification, AC circuit analysis, Transformer characteristics, Motor control, Power measurement |
| MED201 | Workshop Practice | Lab | 1 | Carpentry, Fitting, Welding, Foundry, Sheet metal |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS31 | Discrete Mathematical Structures | Core | 3 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics |
| 21CS32 | Data Structures and Applications | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 21CS33 | Analog and Digital Electronics | Core | 3 | Diode Circuits, Transistor Biasing, Operational Amplifiers, Logic Gates, Combinational and Sequential Circuits |
| 21CS34 | Computer Organization and Architecture | Core | 3 | Basic Computer Organization, Instruction Sets, CPU Design, Memory System, I/O Organization |
| 21CSL35 | Data Structures Lab | Lab | 2 | Implementation of data structures, Algorithm analysis, Problem solving using data structures, Memory management, Debugging |
| 21CSL36 | Analog and Digital Electronics Lab | Lab | 2 | Diode characteristics, Transistor amplifier design, Op-amp applications, Logic gate verification, Flip-flop implementation |
| 21MAT31 | Transform Calculus, Fourier Series and Numerical Techniques | Core | 3 | Laplace Transforms, Inverse Laplace Transforms, Fourier Series, Numerical Methods for ODEs, Finite Differences |
| 21KSK37/21KBK37 | Technical Kannada/Constitution of India, Professional Ethics & Cyber Law | Ability Enhancement | 1 | Kannada grammar, Technical vocabulary, Indian Constitution basics, Professional ethics, Cybersecurity laws |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS41 | Design and Analysis of Algorithms | Core | 3 | Algorithm Complexity, Greedy Algorithms, Divide and Conquer, Dynamic Programming, Graph Algorithms |
| 21CS42 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| 21CS43 | Microcontroller and Embedded Systems | Core | 3 | 8051 Microcontroller Architecture, Assembly Language Programming, Interfacing Techniques, Embedded C Programming, Real-time Operating Systems |
| 21CS44 | Object Oriented Programming with JAVA | Core | 3 | OOP Concepts, Java Basics, Inheritance and Polymorphism, Exception Handling, Multithreading |
| 21CSL45 | Operating Systems Lab | Lab | 2 | System calls, Process creation and synchronization, Memory allocation strategies, File system operations, Shell scripting |
| 21CSL46 | Microcontroller and Embedded Systems Lab | Lab | 2 | 8051 programming, Interfacing I/O devices, Timer/Counter applications, Serial communication, Mini projects |
| 21CSL47 | Object Oriented Programming with JAVA Lab | Lab | 2 | Class and object design, Inheritance and polymorphism implementation, GUI programming with Swing/JavaFX, JDBC connectivity, Web application basics |
| 21CIV48/21BSC48 | Environmental Studies/Research Methodology & Intellectual Property Rights | Ability Enhancement | 1 | Ecosystems and biodiversity, Pollution control, Sustainable development, Research ethics, Patent and copyright laws |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS51 | Management, Entrepreneurship for IT Industry | Core | 3 | Management Principles, Organizational Behavior, Entrepreneurship Development, IT Project Management, Business Ethics |
| 21CS52 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer Protocols, Transport Layer Protocols |
| 21CS53 | Database Management Systems | Core | 3 | Database Architecture, ER Modeling, Relational Algebra, SQL Queries, Transaction Management |
| 21CSL54 | Computer Networks Lab | Lab | 2 | Network simulation tools, Socket programming, Packet analysis with Wireshark, Routing protocols, Network security tools |
| 21CSL55 | Database Management Systems Lab | Lab | 2 | SQL DDL/DML, Stored procedures, Database triggers, JDBC/ODBC connectivity, Normalization |
| 21CS56X | Professional Elective - 1 (e.g., Artificial Intelligence, Advanced JAVA Programming) | Elective | 3 | Varies based on elective choice (e.g., AI: Search algorithms, Knowledge representation, ML basics), Varies based on elective choice (e.g., Adv Java: Servlets, JSP, Hibernate, Spring) |
| 21CS57X | Open Elective - 1 (e.g., Introduction to Python Programming, Introduction to Web Technologies) | Open Elective | 3 | Varies based on elective choice (e.g., Python: Basic syntax, data structures, functions, modules), Varies based on elective choice (e.g., Web Tech: HTML, CSS, JavaScript basics, DOM) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS61 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| 21CS62 | Software Engineering | Core | 3 | Software Life Cycle Models, Requirements Engineering, Software Design, Software Testing, Project Management |
| 21CS63 | Web Technology and its Applications | Core | 3 | HTML5 and CSS3, JavaScript and DOM, Server-side Scripting (PHP/Node.js), Web Services (REST/SOAP), Web Security |
| 21CSL64 | Web Technology Lab | Lab | 2 | Responsive web design, Client-side scripting exercises, Server-side application development, Database integration, Deployment basics |
| 21CS65X | Professional Elective - 2 (e.g., Machine Learning, Cloud Computing) | Elective | 3 | Varies based on elective choice (e.g., ML: Supervised/Unsupervised learning, Deep Learning basics), Varies based on elective choice (e.g., Cloud: IaaS, PaaS, SaaS, Virtualization, AWS/Azure basics) |
| 21CS66X | Open Elective - 2 (e.g., Big Data Analytics, Mobile Application Development) | Open Elective | 3 | Varies based on elective choice (e.g., Big Data: Hadoop, Spark, NoSQL databases), Varies based on elective choice (e.g., Mobile: Android/iOS basics, UI/UX, database integration) |
| 21CSMP67 | Mini Project | Project | 2 | Problem definition, Design and implementation, Testing and debugging, Report writing, Presentation skills |
| 21CSI68 | Internship (Non-Credit) | Internship | 0 | Industry exposure, Practical skill development, Professional communication, Teamwork, Problem-solving in real-world scenarios |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS71 | Artificial Intelligence and Machine Learning | Core | 3 | AI Foundations, Search Algorithms, Supervised Learning, Unsupervised Learning, Neural Networks |
| 21CS72X | Professional Elective - 3 (e.g., Cyber Security, Data Mining) | Elective | 3 | Varies based on elective choice (e.g., Cyber Security: Cryptography, Network Security, Ethical Hacking), Varies based on elective choice (e.g., Data Mining: Association Rules, Classification, Clustering, Web Mining) |
| 21CS73X | Professional Elective - 4 (e.g., Internet of Things, Advanced Database Management Systems) | Elective | 3 | Varies based on elective choice (e.g., IoT: IoT Architecture, Sensors, Actuators, Communication Protocols), Varies based on elective choice (e.g., Adv DBMS: Distributed DB, Object-Oriented DB, Data Warehousing) |
| 21CSP74 | Project Work Phase - I | Project | 3 | Literature Survey, Problem Formulation, System Design, Resource Planning, Initial Prototyping |
| 21CSS75 | Seminar | Seminar | 1 | Literature review, Topic presentation, Technical writing, Q&A handling, Research methodology |
| 21CSI76 | Internship (Credit Based) | Internship | 3 | Industry work experience, Application of theoretical knowledge, Project delivery, Professional networking, Mentorship |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS81X | Professional Elective - 5 (e.g., Data Analytics with Python, Blockchain Technology) | Elective | 3 | Varies based on elective choice (e.g., Data Analytics: NumPy, Pandas, Scikit-learn, Data visualization), Varies based on elective choice (e.g., Blockchain: Cryptographic primitives, Bitcoin, Ethereum, Smart Contracts) |
| 21CS82 | Project Work Phase - II | Project | 10 | System Implementation, Testing and Evaluation, Result Analysis, Documentation, Project Defense |
