B.C.A. (Hons.) Mobile Applications & Cloud Technology

The BCA (Mobile Application and Cloud Technology) programme is a four-year undergraduate degree program approved by AICTE. It is designed to provide students with a strong foundation in computer applications, while specializing in mobile app development and cloud computing. This industry-focused programme blends theoretical knowledge with practical expertise, equipping students with the skills required to excel in the rapidly evolving IT landscape. With a curriculum that covers mobile application development, cloud technology, and core computing concepts, students will gain hands-on experience in building scalable applications, managing cloud infrastructure, and leveraging emerging technologies. This programme prepares graduates for dynamic career opportunities in software development, cloud computing, and IT services, making them industry-ready professionals in the digital era.

The program prepares graduates for dynamic roles across various IT and professional sectors.
  • Specialized Cloud & IT Support Roles:
  • Cloud Architect
  • Cloud Support Engineer
  •  Cloud Administrator
  • Cloud Engineer
  • Cloud Security Specialist
  • Remote Desktop Engineer
Development Roles:
  • Software Engineer / Developer
  •  Mobile Application Developer
  • UI Engineer
Professional Services & Enterprises:
  • Small, Medium, and Large Professional Services IT Companies.
  • Enterprise Application Product and Service Companies.
  • Internet and VAS (Value Added Service) providers.
  • E-Commerce and M-Commerce companies.

These are mandatory, foundational subjects to build a strong, uniform understanding of computer science and IT.

This course explores the hardware foundations of computing. Module 1 covers binary number systems, Boolean algebra, and K-map simplification. Module 2 details logic gates and combinational circuits like adders and multiplexers. Module 3 addresses sequential logic, including flip-flops, registers, and various counters. Module 4 examines basic computer architecture, functional units, and instruction cycles. Module 5 focuses on memory hierarchy (RAM, ROM, Cache) and Input/Output organization, including DMA and addressing modes. Students learn to interpret hardware-level operations and the interaction between essential computer components.

This course provides the mathematical rigor necessary for computational analysis. Module 1 covers matrix basics, elementary operations, and rank determination. Module 2 focuses on determinants, linear independence, eigenvalues, and eigenvectors. Module 3 introduces solution methods for systems of linear equations, such as Gauss-Elimination and Gauss-Seidal. Module 4 delves into propositional logic and logical equivalences to foster critical thinking. Module 5 consists of a practicum applying these concepts to real-world scenarios, such as image compression and system stability analysis, bridging the gap between abstract algebra and its practical computational applications.

This course provides a deep dive into Object-Oriented Programming (OOP). Module 1 contrasts procedural and OOP methodologies, covering basic C++ syntax, control statements, and class definitions. Module 2 explores constructors, destructors, and constructor overloading to manage object lifecycles. Module 3 addresses polymorphism through function and operator overloading, inheritance, and the use of pointers. Module 4 focuses on advanced features like virtual/friend functions and implements comprehensive file handling and error management strategies. Students gain the ability to build reusable and modular software components.

This course provides the statistical foundation for data analysis. Module 1 covers descriptive statistics, including central tendency, dispersion, moments, and correlation/regression analysis. Module 2 introduces probability theory, covering experiments, theorems, and Bayes’ theorem. Module 3 explores random variables (discrete and continuous), probability mass/density functions, and marginal distributions. Module 4 details mathematical expectation, moment-generating functions, and conditional variance. Module 5 involves a practicum using Excel/R to solve numerical problems, enabling students to perform computational analysis on datasets.

This course examines the principles of relational database management systems. Module 1 introduces database concepts, users, and the entity-relationship (ER) model. Module 2 covers the relational model, integrity constraints, and formal query languages like relational algebra and calculus. Module 3 focuses on Structured Query Language (SQL) for DDL and DML commands, nested queries, and view management. Module 4 delves into normalization (1NF through 5NF/BCNF), indexing structures, transaction processing, security, and advanced topics like NoSQL databases. Students learn to design and manage efficient databases.

This course teaches efficient data management and algorithm design. Module 1 defines data structures and introduces time/space complexity analysis alongside array fundamentals. Module 2 covers searching (linear/binary) and sorting algorithms (selection, bubble, insertion, quick, merge). Module 3 details stack and queue implementations, including application scenarios like postfix expression evaluation and circular queues. Module 4 explores linked lists (singly, doubly, circular), trees (binary search trees), and graph traversal techniques (BFS, DFS). Practical components involve implementing these structures to solve classic computational problems.

This course provides a comprehensive guide to building dynamic web applications with PHP. Module 1 covers PHP syntax, variables, operators, and control structures. Module 2 focuses on advanced PHP, including user-defined functions, recursion, and object-oriented concepts like classes, inheritance, and constructors. Module 3 details web form handling, including data capturing and file uploads. Module 4 examines file system management and database connectivity with MySQL, teaching students how to retrieve, insert, and update database records to build full-featured, data-driven web applications.

This course focuses on Android app development using Java. Module 1 covers the Android platform evolution, project structure, the manifest file, and UI layout design. Module 2 explores the activity lifecycle, state changes, intents, and fragment management. Module 3 examines local data storage techniques, including SharedPreferences, SQLite databases, and content providers for data sharing. Module 4 focuses on multimedia integration, utilizing cameras for photo/video capture, and provides the necessary steps for deploying an application to the Google Play Store.

This course outlines the structured approach to software development. Module 1 details software engineering paradigms, the software development life cycle (SDLC) models, and the distinction between software and system engineering. Module 2 addresses requirement engineering, functional/non-functional requirements, project planning (Gantt/PERT charts), and prototyping. Module 3 focuses on design processes, heuristics, UI design, and modeling using DFDs and UML diagrams. Module 4 examines testing taxonomies, including black-box testing, white-box testing, and system debugging procedures.

This course provides a fundamental overview of OS components. Module 1 defines OS structures, services, system calls, and multi-threaded processes. Module 2 covers CPU scheduling algorithms, process synchronization using semaphores and monitors, and deadlock characterization, avoidance, and detection. Module 3 explores logical/physical memory address spaces, paging, segmentation, and virtual memory management techniques like thrashing and page replacement. Module 4 details file system interfaces, directory structures, disk scheduling algorithms, and security threats like password and program threats.

This course covers networking foundations. Module 1 defines network types, topologies, devices, and the OSI/TCP-IP reference models. Module 2 details multiplexing, switching techniques, and transport/network layer protocols including TCP, UDP, IPv4, and IPv6. Module 3 examines data transmission, flow control (Stop-and-Wait, ARQ), error correction, and congestion control algorithms like Leaky/Token Bucket. Module 4 focuses on wireless networks, LAN standards (IEEE 802.11), security protocols (WEP/WPA), and modern cellular generations (2G–5G).

This course introduces image analysis techniques. Module 1 covers image formation, camera models, color spaces (RGB/HSV), and pixel relationships. Module 2 focuses on spatial domain enhancement (histogram equalization) and frequency domain filtering (Fourier transform, low/high-pass filters). Module 3 details image restoration using mean/Wiener filters and compression standards like JPEG and PNG. Module 4 addresses segmentation via edge detection (Sobel/Canny) and thresholding, along with feature extraction techniques for shape, texture, and color.

This course emphasizes mobile application quality assurance. Module 1 covers SDLC phases and fundamental testing types, including white/grey/black-box, regression, and stress testing. Module 2 introduces the JUnit framework, directory structures, and the Android Testing Framework. Module 3 explores Appium architecture, inspection tools, and locator strategies for native/hybrid apps. Module 4 details the Calabash testing framework, utilizing Cucumber and Gherkin syntax for UI interaction testing and handling slow application behaviors.

This course studies the theoretical foundations of computation. Module 1 covers automata theory, regular expressions, finite automata (DFA/NFA), and minimization techniques. Module 2 explores context-free grammars (CFG), parse trees, ambiguity removal, and pushdown automata (PDA). Module 3 details Turing machines, transition diagrams, and undecidable problems. Module 4 focuses on compiler construction, including lexical analysis, token recognition, top-down parsing (recursive descent), and bottom-up parsing techniques (LR, SLR, CLR) to understand how programming languages are processed.

This course explores the essentials and current practices in virtualization. Module 1 covers the history, business drivers, and technology innovations of virtualization, including hypervisors and full/para-virtualization. Module 2 details server and desktop virtualization, including server consolidation and disaster recovery strategies. Module 3 examines storage, input/output, and network virtualization. Module 4 focuses on industry-leading software like VMware, Microsoft Hyper-V, and Google Virtualization, covering their architecture, features, and implementation.

This course provides an introduction to AI methodologies. Module 1 covers the evolution of AI, the Turing test, and key concepts. Module 2 focuses on problem-solving strategies, heuristic search (A*, game playing), and mean-end analysis. Module 3 examines knowledge representation via predicate logic, semantic networks, and expert systems. Module 4 details planning, including classical, hierarchical, and non-deterministic domain planning. Practical sessions involve predicate logic and planning exercises.

This course delves into neural network development. Module 1 covers foundations, including mathematical basics, activation functions, and gradient descent. Module 2 details architectures like CNNs, RNNs, and Transformers. Module 3 explores regularization, hyperparameter tuning, and transfer learning. Module 4 focuses on applications including computer vision, NLP, speech recognition, and time-series forecasting.

This course bridges cloud infrastructure and data analytics. Module 1 covers cloud models, service architectures, and providers. Module 2 details storage models and computing services like Docker and serverless computing. Module 3 examines scaling strategies using parallel computing and MapReduce. Module 4 focuses on Big Data analytics services, including Hadoop and case studies in healthcare and social media analytics.

This course prepares students for formal research. Module 1 covers the meaning, types, ethics, and design of research. Module 2 details data collection methods, sampling techniques, and scaling. Module 3 focuses on data analysis, including central tendency, regression, and hypothesis testing. Module 4 teaches report writing, research proposals, and the use of computer applications in research.

This course outlines professional research integrity. Module 1 covers philosophy and ethics. Module 2 focuses on scientific misconduct (FFP - Falsification, Fabrication, Plagiarism) and redundant publications. Module 3 addresses publication ethics, predatory journals, and open access initiatives. Module 4 explores research metrics (h-index, impact factor) and software tools like Turnitin.

Discipline Specific Elective courses are specialized courses that allow students to dive deeper into their chosen major or minor fields, providing tailored knowledge in specific areas of their discipline.

This course provides a comprehensive insight into cloud computing principles. Module 1 defines cloud technology, its evolution, NIST characteristics, and the need for cloud adoption. Module 2 explores architectural influences like High-Performance Computing and distributed computing, detailing the NIST cloud model and service/deployment models. Module 3 addresses cloud security, covering threat agents, risk management, and industrial platforms like AWS and Azure. Module 4 focuses on cloud migration, including the seven-step migration model, risk mitigation strategies, and enterprise-level adoption challenges, preparing students to evaluate and implement various cloud service environments effectively.

This course advances Android development skills for complex applications. Module 1 covers Kotlin syntax, advanced UI design, Material Design components, and responsive layout strategies. Module 2 delves into memory management and asynchronous programming using Kotlin Coroutines for handling background tasks and UI updates. Module 3 focuses on network interactions using Retrofit for API calls, JSON parsing, and optimizing performance with image-loading libraries like Glide. Module 4 details location-based services, Google Maps integration, and implementing Firebase Cloud Messaging for push notifications, enabling the creation of scalable, high-performance mobile applications.

This course offers a complete web development workflow. Module 1 introduces HTML5, covering semantic tags, forms, and document structure. Module 2 focuses on CSS for responsive styling, including the box model, positioning, and modern layout techniques. Module 3 covers JavaScript, documenting object manipulation, event handling, and modern front-end frameworks like React. Module 4 addresses server-side development with Node.js, covering event loops, modularity, and database connectivity with MongoDB. Students gain proficiency in building complete, dynamic web applications by integrating front-end interfaces with robust, scalable back-end services and database systems.

This course imparts knowledge for building and managing cloud infrastructure. Module 1 explores cloud delivery models, business cost metrics, and Service Level Agreements (SLAs). Module 2 details infrastructure mechanisms, including logical network perimeters, virtual servers, cloud usage monitors, and resource clustering. Module 3 examines cloud architectures such as dynamic scalability, workload distribution, and elastic resource capacity. Module 4 covers management mechanisms, focusing on remote administration, billing systems, and critical security tools like PKI, encryption, and Identity and Access Management (IAM) to ensure secure, efficient resource oversight.

This course integrates intelligence into mobile apps. Module 1 covers machine learning fundamentals, including supervised learning algorithms and the Android Studio ML development environment. Module 2 introduces Firebase ML Kit for features like text and face recognition, alongside TensorFlow Lite for loading pre-trained models. Module 3 focuses on object detection, implementing real-time identification using camera inputs and pre-trained models like YOLO. Module 4 explores Convolutional Neural Networks (CNNs), transfer learning, and image classification, enabling students to build intelligent Android applications that process visual data directly on the device.

This course introduces Large Language Models (LLMs). Module 1 covers transformer architecture, self-attention, and language modeling basics. Module 2 details causal language modeling, generative pre-trained transformers (GPT), and bidirectional encoders like BERT. Module 3 explores the T5 model, text-to-text taxonomy, data cleaning pipelines, and scaling techniques. Module 4 examines optimizers like Adam/LION, loss functions, and parameter-efficient fine-tuning (PEFT), preparing students to approach, critique, and implement various LLM-based solutions for real-world NLP challenges.

This course explores the dynamic field of generative modeling. Module 1 introduces generative vs. discriminative models, GMMs, and GANs. Module 2 details GAN core concepts, including adversarial training, architectures like DCGAN, and Variational Autoencoders (VAEs) for latent space exploration. Module 3 discusses advanced topics like attention mechanisms, self-supervised learning, and future industrial research trends. Module 4 emphasizes practical application, using Python and TensorFlow to build, train, and test GAN and VAE models, culminating in a capstone project that applies these techniques to domain-specific datasets.

Multi-Disciplinary Elective courses are designed to provide a holistic and broad education outside the core computer science stream.

Computer Hardware and Assembling (24UBCAMDE101) This course offers a practical guide to computer systems. Module 1 covers the evolution and classification of computers and their functional block diagrams. Module 2 details internal hardware like power supplies (SMPS), inverters, and UPS systems. Module 3 explores expansion slots, processors, and BIOS setup. Module 4 examines memory units, secondary storage technologies, and various I/O devices like scanners and printers. Module 5 provides hands-on training for PC assembly, CMOS configuration, OS installation, and driver management. Students gain the technical skills required to build, maintain, and troubleshoot functional computing systems in a corporate environment.

This course teaches the essential skills for web creation. Module 1 covers HTML structure, including tags, headings, lists, tables, and multimedia integration. Module 2 focuses on CSS for styling, covering the box model, margin/padding properties, and layouts. Module 3 introduces the publishing process, guiding students through website architecture, theme application, and hosting. Students gain the ability to design and maintain functional, aesthetically pleasing web pages, providing a solid foundation for more advanced web development technologies.

This course offers advanced mathematical techniques for computation. Module 1 covers numerical root-finding (Bisection, Newton-Raphson), interpolation (Newton’s formulas), and integration (Trapezoidal/Simpson’s rules). Module 2 introduces Linear Programming (LPP) and graphical methods for solving two-variable optimization problems. Module 3 details the Simplex method and Big M technique for broader optimization. Module 4 examines transportation and assignment problems, including the North-West corner method and MODI method for finding optimal solutions. These tools are essential for complex resource optimization and algorithmic accuracy.

SEC courses equip students with practical, hands-on training to directly improve employability.

This course builds a strong foundation in procedural programming. Module 1 introduces C basics, tokens, and data types. Module 2 covers input/output operations, decision-making, and iterative control statements. Module 3 focuses on advanced concepts like strings, user-defined functions, storage classes, and pointer arithmetic. Finally, Module 4 emphasizes data management, including structures, unions, file handling, and dynamic memory allocation. Students gain the ability to write robust C programs to solve complex computational problems.

This course delivers theoretical and practical knowledge of Linux. Module 1 introduces the architecture, distributions, and basic shell navigation. Module 2 covers essential commands, file permissions, pipes, and filters for efficient task execution. Module 3 examines the Linux file system, directory paths, process states, and communication utilities. Module 4 focuses on system administration, detailing the booting process (GRUB/LILO), managing user accounts, system monitoring, and security configurations. This training prepares students for administrative roles in a Linux-based computing environment.

This course establishes a foundation in object-oriented programming using Java. Module 1 introduces Java architecture, data types, control structures, and basic input/output operations. Module 2 explores class fundamentals, "this" keyword, method overloading, and array handling. Module 3 covers object-oriented principles including inheritance, method overriding, interfaces, packages, and exception handling. Module 4 focuses on GUI development using AWT and Swing, including event handling and component design. Practical labs emphasize IDE proficiency, array manipulations, and building simple GUI applications.

This course teaches professional document creation using LaTeX. Module 1 introduces the LaTeX environment, document classes, and basic structural commands. Module 2 focuses on advanced formatting, including table generation, font effects, custom page styles, and multi-column layouts. Module 3 covers inserting graphics, using specialized environments like tabular/cases, and implementing bibliography management with BibTeX. Module 4 explores creating slide presentations using the Beamer class and formatting professional CVs through Overleaf templates.

This course provides a practical introduction to generative AI. Module 1 covers the architecture and capabilities of Large Language Models (LLMs) like ChatGPT, focusing on the art of effective prompt engineering. Module 2 explores AI tools for productivity, content creation (writing, design, code), and communication. Module 3 is a project-based module where students select, design, and implement an AI-powered solution to solve a real-world problem. Practical lab work involves testing coding scripts, generating content, and organizing plans using AI assistants.

This course provides an introduction to the theory and application of ML algorithms. Module 1 defines machine learning, its lifecycle, and basic applications. Module 2 focuses on supervised learning, covering regression (linear/polynomial) and classification (decision trees, logistic regression, SVM). Module 3 examines unsupervised learning, detailing clustering algorithms (k-means, hierarchical) and dimensionality reduction techniques (PCA, t-SNE). Module 4 covers model evaluation using cross-validation and metrics like precision, recall, and F1 score, alongside feature engineering for data preprocessing.

This internship provides practical industry immersion. It requires students to complete at least four weeks of training in an IT firm or relevant organization during the summer break. Students are responsible for maintaining a daily work log, submitting weekly progress reports, and completing assigned project modules. The internship concludes with a final report documenting objectives, tools/technologies used, challenges, and key learning outcomes, which is evaluated alongside an industry supervisor’s feedback and a viva-voce presentation.

This course is a practical application of the entire program’s curriculum. Module 1 focuses on project management, defining objectives, scheduling, resource planning, feasibility reporting, and software requirement specifications. Module 2 covers the design documentation phase, including architectural, interface, and data design, followed by testing and implementation procedures involving validation checks and exception handling. Students are expected to build an individual project using the latest languages and platforms to demonstrate their technical proficiency, culminating in a hard-bound report and a final viva-voce examination.

These courses represent the capstone of the program. Students apply core computer science concepts to real-world software or data science problems. The projects involve the entire SDLC: requirements analysis, architectural design, implementation, and rigorous testing. For the Research Stream, students complete a formal Dissertation, while others engage in Major Project II, requiring a hard-bound report and a successful viva-voce defense, demonstrating mastery of the technical skills acquired throughout the BCA program.

Value Added Courses (VAC) are supplementary, non-core courses designed to focus on holistic personality development, practical technical skills, and ethical values

This course blends technology, environmental awareness, and personal health. Module 1 covers internet research techniques and environmental studies. Module 2 examines IT's impact on E-learning and academic services like NPTEL. Module 3 addresses societal impacts, including cyber laws, piracy, privacy, and digital addiction. Module 4 focuses on e-waste management and green computing practices. Module 5 introduces Yoga, covering asanas, pranayama, and stress management techniques. This course ensures students maintain a balanced, environmentally conscious approach to technology.

This course explores the intersection of IT and environmental sustainability. Module 1 defines green IT, focusing on sustainable development, carbon footprints, and environmental impacts of IT hardware. Module 2 addresses the concept of green devices, detailing the full lifecycle of hardware from manufacturing to disposal and recycling. Module 3 emphasizes paperless office strategies, intranet use, and green storage technologies like SSDs and RAID configurations. This course aims to guide students toward responsible, energy-efficient, and eco-friendly computing practices in professional settings.

This course focuses on holistic personal health. Module 1 defines health vs. wellness, emphasizing nutrition and the importance of a balanced diet. Module 2 addresses body systems, malnutrition, sedentary lifestyle risks, and mental health factors including stress and suicidal tendencies. Module 3 covers lifestyle-related diseases like hypertension, diabetes, and cardiovascular issues, alongside substance abuse and corrective measures for postural deformities. Module 4 examines the role of yoga, meditation, and adequate sleep in maintaining physical and mental health.

This course provides awareness of security threats and defensive techniques. Module 1 introduces the cybersecurity landscape, the importance of data integrity, and ethical hacking fundamentals. Module 2 focuses on network security, specifically securing infrastructure and addressing wireless network vulnerabilities, as well as best practices for web application security. Module 3 details incident response and management, teaching students how to identify cyber threats, develop response plans, and apply hacking techniques through hands-on laboratory exercises to understand real-world security scenarios.