.png&w=1920&q=75)
B-E in Computer Science Engineering Data Science at Cambridge Institute of Technology
Bengaluru, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Computer Science & Engineering (Data Science) at Cambridge Institute of Technology Bengaluru?
This Computer Science & Engineering (Data Science) program at Cambridge Institute of Technology focuses on equipping students with advanced skills in data analysis, machine learning, and artificial intelligence. Recognizing India''''s booming digital economy, the program emphasizes practical application and theoretical depth to meet the growing industry demand for data professionals. It stands out by integrating core CSE concepts with specialized data science modules, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for ambitious fresh graduates seeking entry into the burgeoning field of data science, analytics, and AI. It also caters to working professionals aiming to upskill for roles in data engineering, machine learning, or business intelligence. Individuals with a strong analytical bent, a foundational understanding of mathematics, and a keen interest in data-driven problem-solving will thrive in this curriculum.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, and AI Engineer. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly higher. The program''''s rigorous curriculum aligns with requirements for various industry certifications, offering strong growth trajectories in leading Indian and global technology firms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop strong foundational programming skills in C and Python by consistently practicing coding challenges and building small projects. Focus on understanding data types, control flow, functions, and basic algorithms thoroughly.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, NPTEL Programming in Python
Career Connection
A robust programming base is crucial for all future technical roles, forming the backbone for understanding complex data science algorithms and system development during placements.
Build a Strong Mathematical & Statistical Core- (Semester 1-3)
Dedicate significant effort to understanding calculus, linear algebra, probability, and statistics. These mathematical concepts are the bedrock of data science algorithms and machine learning models.
Tools & Resources
Khan Academy, MIT OpenCourseWare (Linear Algebra/Calculus), NCERT Maths Books (Class 11/12 for basics), NPTEL courses on Probability and Statistics
Career Connection
Solid mathematical reasoning enables deeper understanding of ML algorithms, better model selection, and effective interpretation of results, highly valued in advanced data science roles.
Engage in Peer Learning and Technical Clubs- (Semester 1-2)
Join college technical clubs (e.g., AI/ML club, Coding Club) and participate actively in group study sessions and hackathons. Collaborative learning helps clarify concepts and builds problem-solving aptitude.
Tools & Resources
College AI/ML/Coding Clubs, GitHub for collaborative projects, Discord/WhatsApp study groups
Career Connection
Develops teamwork, communication, and networking skills essential for professional success, while also enhancing technical knowledge and exposure to diverse problem-solving approaches.
Intermediate Stage
Undertake Mini-Projects and Kaggle Challenges- (Semester 3-5)
Apply theoretical knowledge by building small data science projects using real-world datasets. Participate in Kaggle competitions to test skills against others and learn from diverse solutions.
Tools & Resources
Kaggle.com, GitHub, Google Colab, DataCamp tutorials
Career Connection
Practical project experience is paramount for building a strong portfolio, which is critical for demonstrating capabilities to potential employers during interviews and securing internships.
Master Data Analytics Tools and Databases- (Semester 4-6)
Become proficient in tools like R, SQL, and explore NoSQL databases. Understand data manipulation, visualization, and querying techniques thoroughly, as these are fundamental industry skills.
Tools & Resources
DataCamp, Coursera (SQL, R courses), W3Schools (SQL), Power BI/Tableau Public (free versions)
Career Connection
Expertise in data analytics tools and database management directly translates to entry-level job requirements for Data Analysts, Business Intelligence Developers, and Data Engineers.
Seek Early Internship and Industry Exposure- (Semester 5-6)
Actively search for internships during summer breaks, even if unpaid initially. Gaining real-world industry experience helps bridge the gap between academic learning and industry expectations.
Tools & Resources
LinkedIn Jobs, Internshala, College Placement Cell, Networking events
Career Connection
Internships provide crucial exposure to professional environments, build a professional network, and often lead to pre-placement offers, significantly boosting career prospects.
Advanced Stage
Specialize and Build a Capstone Project- (Semester 6-8)
Choose advanced electives aligned with career interests (e.g., Deep Learning, NLP, Big Data). Develop a significant, impactful final year project that showcases specialized skills and addresses a real-world problem.
Tools & Resources
TensorFlow/PyTorch, Hadoop/Spark, AWS/Azure free tier, Research papers
Career Connection
A strong capstone project is a key differentiator in job applications, demonstrating expertise, problem-solving abilities, and readiness for specialized roles in AI/ML engineering or data architecture.
Intensive Placement Preparation- (Semester 7-8)
Engage in rigorous preparation for placements by practicing aptitude, technical interviews (data structures, algorithms, SQL, ML concepts), and soft skills. Attend mock interviews and career counseling sessions.
Tools & Resources
LeetCode, InterviewBit, Glassdoor (for company-specific questions), College Placement Cell workshops
Career Connection
This focused effort directly increases the chances of securing desirable job offers from top companies, ensuring a smooth transition into a professional data science or AI career.
Network Professionally and Stay Updated- (Semester 6-8)
Attend industry conferences, webinars, and workshops. Connect with alumni and professionals on platforms like LinkedIn. Continuously learn about new technologies and trends in data science and AI.
Tools & Resources
LinkedIn, Meetup groups, Coursera/edX for advanced courses, Tech blogs (Towards Data Science)
Career Connection
Professional networking opens doors to unexpected opportunities, mentorship, and keeps skills relevant in a rapidly evolving field, fostering long-term career growth and leadership potential.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 (PUC 2nd year) or equivalent examination with English as one of the languages and obtained a minimum of 45% of marks in aggregate in Physics and Mathematics along with Chemistry/Biotechnology/Biology/Electronics/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Vocational subjects (40% for SC/ST/OBC candidates of Karnataka State).
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 |
|---|---|---|---|---|
| 21MATS11 | Calculus and Differential Equations | Core | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Differential Equations, Laplace Transforms |
| 21PHY12/21CHY12 | Engineering Physics/Chemistry | Core | 4 | Quantum Mechanics, Material Science, Electrochemistry, Corrosion, Polymers |
| 21ELE13 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| 21CIV14 | Elements of Civil Engineering & Mechanics | Core | 3 | Engineering Materials, Building Construction, Surveying, Force Systems, Stress and Strain |
| 21EGDL15 | Engineering Graphics & Design Lab | Lab | 3 | Orthographic Projections, Isometric Projections, Solid Modeling, Drafting Standards |
| 21PCD16 | Programming for Problem Solving Lab | Lab | 2 | C Programming Basics, Conditional Statements, Loops, Arrays, Functions |
| 21KSK17 | Kannada (Ability Enhancement Course) | Ability Enhancement | 1 | Basic Kannada Grammar, Conversational Kannada, Kannada Literature Overview |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS21 | Linear Algebra and Differential Equations | Core | 4 | Matrices, Vector Spaces, Eigenvalues and Eigenvectors, Higher Order Differential Equations, Partial Differential Equations |
| 21CHY22/21PHY22 | Engineering Chemistry/Physics | Core | 4 | Water Technology, Fuels, Energy Storage, Lasers, Optical Fibers |
| 21CPL23 | Programming in Python | Core | 3 | Python Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming, File Handling |
| 21ELN24 | Basic Electronics and Communication Engineering | Core | 3 | Diode Circuits, Transistors, Op-Amps, Digital Logic, Communication Systems |
| 21WSL25 | Workshop Practice | Lab | 2 | Welding, Carpentry, Fitting, Sheet Metal, Foundry |
| 21PPL26 | Programming in Python Lab | Lab | 2 | Python Data Structures, Functions, File Operations, Object-Oriented Concepts, Web Scraping Basics |
| 21NSS27 | National Service Scheme (NSS)/Physical Education (PE)/Yoga | Ability Enhancement | 1 | Community Service, Physical Fitness, Yoga and Meditation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS31 | Transforms and Numerical Methods | Core | 3 | Fourier Series, Z-Transforms, Numerical Solutions of Equations, Numerical Integration, Finite Differences |
| 21CS32 | Data Structures | Core | 4 | Arrays and Pointers, Linked Lists, Stacks and Queues, Trees and Graphs, Searching and Sorting |
| 21CS33 | Analog and Digital Electronics | Core | 3 | Boolean Algebra, Logic Gates, Combinational Logic, Sequential Logic, A/D and D/A Converters |
| 21CSDS34 | Database Management Systems | Core | 4 | ER Modeling, Relational Algebra, SQL, Normalization, Transaction Management |
| 21CSDS35 | Discrete Mathematics | Core | 3 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Counting and Probability |
| 21CSL36 | Data Structures Lab | Lab | 2 | Linked List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting Algorithm Analysis |
| 21CSDSL37 | Database Management Systems Lab | Lab | 2 | SQL Queries, Schema Design, Triggers and Procedures, Database Connectivity, Query Optimization |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS41 | Analysis and Design of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 21CS42 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| 21CSDS43 | Object Oriented Programming with Java | Core | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Multithreading |
| 21CSDS44 | Computer Organization and Architecture | Core | 3 | CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining, Instruction Set Architecture |
| 21CSDS45 | Probability and Statistics for Data Science | Core | 3 | Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis |
| 21CSL46 | Operating Systems Lab | Lab | 2 | Shell Scripting, Process Creation, CPU Scheduling, Memory Allocation, Synchronization |
| 21CSDSL47 | Object Oriented Programming Lab | Lab | 2 | Java Class Design, Inheritance Applications, Exception Handling Programs, Multithreading Implementation, GUI Development Basics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSDS51 | Data Analytics with R | Core | 4 | R Programming Fundamentals, Data Manipulation in R, Data Visualization with R, Statistical Modeling in R, Machine Learning with R |
| 21CSDS52 | Artificial Intelligence | Core | 4 | Introduction to AI, Search Algorithms, Knowledge Representation, Machine Learning Fundamentals, Natural Language Processing |
| 21CSDS53 | Computer Networks | Core | 3 | Network Topologies, OSI Model, TCP/IP Protocol Suite, Routing Algorithms, Network Security Basics |
| 21CSDS54 | Web Technology and its Applications | Core | 3 | HTML, CSS, JavaScript, DOM Manipulation, Server-side Scripting, Web Services (REST/SOAP), Web Security |
| 21CSDS55x | Professional Elective - 1 (e.g., Image Processing) | Elective | 3 | Image Enhancement, Image Segmentation, Feature Extraction, Image Compression, Object Recognition |
| 21CSDSL56 | Data Analytics with R Lab | Lab | 2 | Data Importing and Cleaning, Descriptive Statistics in R, Inferential Statistics, Data Visualization Libraries, Basic Predictive Models |
| 21CSDSL57 | Web Technology Lab | Lab | 2 | Front-end Development, Backend Scripting, Database Integration, API Usage, Responsive Design |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSDS61 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Model Evaluation |
| 21CSDS62 | Big Data Analytics | Core | 4 | Introduction to Big Data, Hadoop Ecosystem, MapReduce, Spark Framework, NoSQL Databases |
| 21CSDS63 | Cloud Computing | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, AWS/Azure Basics |
| 21CSDS64x | Professional Elective - 2 (e.g., Natural Language Processing) | Elective | 3 | Text Preprocessing, Tokenization and Stemming, Word Embeddings, Sentiment Analysis, Machine Translation |
| 21CSDS65x | Open Elective (e.g., Entrepreneurship and Startup Management) | Elective | 3 | Startup Ecosystem, Business Plan Development, Funding Sources, Intellectual Property, Marketing Strategies |
| 21CSDSL66 | Machine Learning Lab | Lab | 2 | Python for ML (Scikit-learn), Data Preprocessing, Building Classifiers, Regression Models, Cross-validation Techniques |
| 21CSDSL67 | Big Data Analytics Lab | Lab | 2 | Hadoop HDFS Operations, MapReduce Programming, Spark RDDs, Hive Queries, NoSQL Database Operations |
| 21CSI68 | Internship / Industrial Training | Internship | 3 | Industry Exposure, Practical Skill Application, Problem Solving, Report Writing, Presentation Skills |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSDS71 | Deep Learning | Core | 4 | Neural Networks Basics, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| 21CSDS72 | Data Visualization and Storytelling | Core | 3 | Principles of Data Visualization, Dashboard Design, Interactive Visualizations, Storytelling with Data, Tools (Tableau/Power BI/D3.js) |
| 21CSDS73x | Professional Elective - 3 (e.g., IoT and Data Analytics) | Elective | 3 | IoT Architecture, Sensor Data Acquisition, Edge Computing, IoT Security, Data Analytics for IoT |
| 21CSDS74x | Professional Elective - 4 (e.g., Ethical Hacking for Data Security) | Elective | 3 | Information Gathering, Vulnerability Assessment, Penetration Testing, Cybersecurity Laws, Data Privacy |
| 21CSDP75 | Project Work - Phase 1 | Project | 2 | Problem Identification, Literature Survey, System Design, Resource Planning, Feasibility Study |
| 21CSDS76 | Deep Learning Lab | Lab | 2 | Neural Network Implementation, CNN for Image Classification, RNN for Sequence Data, Hyperparameter Tuning, Transfer Learning |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSDS81 | Business Intelligence and Data Warehousing | Core | 4 | Data Warehouse Architecture, ETL Processes, Dimensional Modeling, OLAP Operations, Business Reporting |
| 21CSDP82 | Project Work - Phase 2 | Project | 10 | System Implementation, Testing and Debugging, Performance Evaluation, Report Generation, Project Defense |
| 21CSSS83 | Seminar/Technical Report | Project | 2 | Technical Writing, Presentation Skills, Research Methodology, Literature Review, Topic Exploration |
