by | Jul 11, 2026 | JNTUK R23 Syllabus
The second semester of third year deepens the data and platform side of IoT with analytics, cloud computing, and machine learning as core subjects, then opens up two professional-elective slots plus an open elective. Two labs reinforce cloud and analytics skills, and the semester closes with soft-skills/IELTS training, a technical-writing and IPR audit course, and a mandatory 8-week industry internship over the following summer. Total load is 20-1-8 contact hours for 23 credits.
Subjects
IoT Data Analytics
- Total: 3-0-0, 3 credits (Professional Core)
- Unit 1: Big-data platforms for IoT and interoperability challenges across smart-city applications.
- Unit 2: RFID authentication issues and adaptive neural approaches to self-aware IoT systems.
- Unit 3: Fog computing as a distributed analytics platform and metadata management for smart grids.
- Unit 4: Web-enabled building automation, intelligent transportation, and smart health-tracking systems.
- Unit 5: Sustainability analytics in cloud-based M2M systems and social-network analysis for smart environments.
Cloud Computing
- Total: 3-0-0, 3 credits (Professional Core)
- Unit 1: Cloud fundamentals — service models (IaaS/PaaS/SaaS) and deployment models.
- Unit 2: Cloud-enabling technologies — parallel/distributed computing, SOA, and virtualization.
- Unit 3: Virtualization and containers, including Docker and orchestration with Kubernetes.
- Unit 4: Cloud challenges — interoperability, scalability, energy efficiency, and security architecture.
- Unit 5: Advanced topics — serverless computing, cloud-centric IoT, edge/fog computing, and DevOps.
Machine Learning
- Total: 3-0-0, 3 credits (Professional Core)
- Unit 1: Machine learning paradigms, learning stages, and data representation.
- Unit 2: Nearest-neighbor models, distance measures, and classifier/regression performance evaluation.
- Unit 3: Decision-tree models, random forests, and the Bayes classifier.
- Unit 4: Linear discriminants — perceptrons, SVMs, logistic regression, and multi-layer perceptrons.
- Unit 5: Clustering techniques — k-means, fuzzy c-means, and spectral clustering.
Professional Elective-II options:
Students choose one of the following four subjects (or an approved 12-week MOOC) as Professional Elective-II.
DevOps
- Unit 1: DevOps lifecycle, workflow principles, and CI/CD automation concepts.
- Unit 2: Source-code management with Git and code-quality analysis with tools like SonarQube.
- Unit 3: Continuous integration using Jenkins, including pipelines and master/agent architecture.
- Unit 4: Continuous delivery and containerization with Docker.
- Unit 5: Configuration management with Ansible and container orchestration with Kubernetes/OpenShift.
IoT Security
- Unit 1: Fundamentals of the IoT security ecosystem and cryptographic building blocks.
- Unit 2: Cloud computing service models relevant to IoT deployments.
- Unit 3: Benefits and challenges of cloud computing, including public-versus-private trade-offs.
- Unit 4: Core security concepts for IoT devices — confidentiality, integrity, and authentication.
- Unit 5: IoT security threats and countermeasures, including virtualization-specific attacks.
Multi Agent Systems
- Unit 1: Foundations of agent systems and their relationship to objects, expert systems, and distributed systems.
- Unit 2: Intelligent-agent architectures — reactive, reasoning, and hybrid agents.
- Unit 3: Multi-agent communication standards and cooperative distributed problem solving.
- Unit 4: Multi-agent decision-making — game-theoretic equilibria and computational social choice.
- Unit 5: Resource allocation, auction mechanisms, bargaining strategies, and logical foundations of multi-agent reasoning.
Automata Theory & Compiler Design
- Unit 1: Finite automata (DFA/NFA) and an introduction to compiler phases.
- Unit 2: Regular expressions/languages and the lexical-analysis phase of compilation.
- Unit 3: Context-free grammars, parse trees, and top-down parsing.
- Unit 4: Pushdown automata and bottom-up/LR parsing techniques.
- Unit 5: Turing machines, decidability, and later compiler phases such as code generation.
Professional Elective-III options:
Students choose one of the following four subjects (or an approved 12-week MOOC) as Professional Elective-III.
Blockchain Technologies
- Unit 1: Blockchain fundamentals, consensus mechanisms, and cryptocurrency basics.
- Unit 2: Public blockchain systems (Bitcoin, Ethereum) and smart contracts.
- Unit 3: Private and consortium blockchain systems, plus initial coin offerings.
- Unit 4: Blockchain security — privacy, scalability, and identity-management challenges.
- Unit 5: Industry case studies and hands-on blockchain development with Python and Hyperledger Fabric.
Natural Language Processing
- Unit 1: Language modeling, morphology, and text normalization/tokenization.
- Unit 2: N-gram models and part-of-speech tagging approaches.
- Unit 3: Syntactic analysis — context-free grammars, parsing, and probabilistic CFGs.
- Unit 4: Semantics and pragmatics, including word-sense disambiguation.
- Unit 5: Discourse analysis, coreference resolution, and standard lexical resources like WordNet.
Security Assessment and Risk Analysis
- Unit 1: Core computer-security principles and threat modeling.
- Unit 2: Secure software design representations and vulnerability-aware design review.
- Unit 3: Software assurance models and risk-based security testing.
- Unit 4: Enterprise security — cryptography, authentication schemes, and PKI.
- Unit 5: Security frameworks for internet-based e-commerce and e-service systems.
Android Application Development
- Unit 1: Mobile device security issues and secure development strategies.
- Unit 2: WAP/mobile HTML security and common web-style application attacks.
- Unit 3: Bluetooth technology architecture and its security vulnerabilities.
- Unit 4: SMS/MMS and WAP protocol-level attacks.
- Unit 5: Enterprise mobile security controls — encryption, sandboxing, and app signing/permissions.
Open Elective-III
- Total: 3-0-0, 3 credits
- Selected from the university-wide open elective pool available to IoT students that semester.
Cloud Computing Lab
- Total: 0-0-3, 1.5 credits (Professional Core)
- Web-services and IPC/messaging exercises.
- Virtual-machine setup with VirtualBox/VMware and cloud instance provisioning on AWS EC2/OpenStack.
- Google App Engine deployment and Docker container web-server setup.
- Hadoop single-node cluster setup, OpenFaaS serverless demos, and CloudSim scheduling simulations.
IoT Data Analytics Lab
- Total: 0-0-3, 1.5 credits (Professional Core)
- Core Java data-structure implementations (linked lists, stacks, queues, sets, maps).
- Hadoop installation across standalone, pseudo-distributed, and fully distributed modes.
- MapReduce programs — word count, weather-data mining, shortest path, and PageRank.
- Pig and Hive exercises for data sorting, grouping, and querying.
Soft Skills or IELTS
- Total: 0-1-2, 2 credits (Skill Enhancement Course)
- Unit 1: Communication skills — intrapersonal/interpersonal skills and verbal/non-verbal communication.
- Unit 2: Critical thinking — active listening, analytical reasoning, and case analysis.
- Unit 3: Problem solving and decision making, including conflict resolution.
- Unit 4: Emotional intelligence and stress management.
- Unit 5: Leadership skills — team building, public speaking, and time management.
Technical Paper Writing & IPR (Audit Course)
- Total: 2-0-0, 0 credits
- Unit 1: Technical report writing fundamentals and structuring conventions.
- Unit 2: Drafting, illustrations, and plain-English editing practices.
- Unit 3: Proofreading, summarizing, and presenting final reports.
- Unit 4: Word-processing tools for reports — tables of contents, tracked changes, and citations.
- Unit 5: Intellectual property fundamentals — patents, copyrights, and the patenting process.
Note: A mandatory industry internship of 8 weeks runs during the following summer vacation.
by | Jul 11, 2026 | JNTUK R23 Syllabus
Final-year first semester ties IoT development to cloud deployment as the sole professional-core subject, pairs it with a human-resource-management course, and opens two more professional-elective slots alongside two open electives. An ethical-hacking skill course, a Constitution of India audit course, and evaluation of the prior industry internship round out the term. Total load is 19-1-2 contact hours for 21 credits.
Subjects
IoT Applications Development on Cloud Platform
- Total: 3-0-0, 3 credits (Professional Core)
- Unit 1: IoT vision, strategic research directions, and standardization efforts.
- Unit 2: M2M-to-IoT architectural overview and market perspective.
- Unit 3: IoT value-creation applications across industry, retail, and healthcare, alongside cloud-computing fundamentals.
- Unit 4: Cloud infrastructure — storage, virtualization, application development, and scalability design.
- Unit 5: IoT and cloud governance, privacy, and security considerations.
Human Resource Management
- Total: 2-0-0, 2 credits (Management Course-II)
- Unit 1: HR management concepts, philosophy, and policy frameworks.
- Unit 2: HR system design and human-resource information systems.
- Unit 3: Functional HR areas — recruitment, compensation, and employee relations.
- Unit 4: HR planning, succession, and strategic HR management.
- Unit 5: HR practices specific to the service sector, including customer-facing employee management.
Professional Elective-IV options:
Students choose one of the following four subjects (or an approved 12-week MOOC) as Professional Elective-IV.
Ad-hoc and Sensor Networks
- Unit 1: MANET characteristics and topology-based/position-based routing algorithms.
- Unit 2: Broadcast/multicast data-transmission schemes in ad-hoc networks.
- Unit 3: Geocasting techniques and TCP behavior over ad-hoc networks.
- Unit 4: Wireless sensor network architecture and lower-layer (physical/MAC/routing) issues.
- Unit 5: Upper-layer WSN concerns, including transport-layer adaptation and sensor-robot integration.
Malware Analysis and Reverse Engineering
- Unit 1: Malware analysis fundamentals, lab setup, and classification techniques.
- Unit 2: Malware forensics — registry analysis, packer identification, and rogue-certificate detection.
- Unit 3: Kernel/malware debugging techniques across Windows and virtualized environments.
- Unit 4: Memory forensics using tools such as Volatility.
- Unit 5: Domain/IP research techniques for tracing malicious infrastructure.
Cryptocurrency Technologies
- Unit 1: History of money and the cryptographic foundations of digital currency.
- Unit 2: Cryptographic primitives underlying blockchain — hashing, digital signatures, and consensus.
- Unit 3: Bitcoin mechanics and the Ethereum ecosystem.
- Unit 4: Solidity programming and smart-contract development on Ethereum.
- Unit 5: Cryptocurrency regulation and blockchain applications beyond currency.
Designing IoT Architectures
- Unit 1: The IoT landscape — applications, architectures, and protocol concepts.
- Unit 2: IoT device design trade-offs and event-driven system analysis.
- Unit 3: Industrial IoT (IIoT) architecture and Industry 4.0 concepts.
- Unit 4: Security and safety engineering for IoT applications.
- Unit 5: Security testing approaches, including fuzz testing of industrial protocols like Modbus.
Professional Elective-V options:
Students choose one of the following four subjects (or an approved 12-week MOOC) as Professional Elective-V.
Cyber Physical Systems
- Unit 1: Symbolic synthesis techniques for cyber-physical system models.
- Unit 2: Security requirements, attack models, and countermeasures for cyber-physical systems.
- Unit 3: Synchronization challenges in distributed cyber-physical systems.
- Unit 4: Real-time scheduling under fixed and variable timing constraints.
- Unit 5: Model integration and semantic formalization across CPS domain-specific languages.
Intrusion Detection and Prevention System
- Unit 1: History and foundational concepts of intrusion detection.
- Unit 2: Intrusion prevention architectures and vulnerability-analysis techniques.
- Unit 3: Snort installation, configuration, and operating modes.
- Unit 4: Writing and managing Snort rules, plus integration with MySQL.
- Unit 5: Using ACID/SnortSnarf and comparing IDS/IPS architectural models.
Industry IoT
- Unit 1: Industrial revolutions leading to Industry 4.0 and smart factories.
- Unit 2: Sensors, actuators, and embedded/wireless implementation for industrial processes.
- Unit 3: IoT gateways, edge systems, and real-time monitoring dashboards.
- Unit 4: Cyber-physical systems, AR/VR, and AI applied to industrial platforms.
- Unit 5: Industrial IoT applications across healthcare, power, and facility management.
Augmented Reality & Virtual Reality
- Unit 1: AR fundamentals — displays, tracking, and calibration.
- Unit 2: Computer vision for AR and AR software architecture.
- Unit 3: VR fundamentals — geometry of virtual worlds and optics.
- Unit 4: Human visual physiology and perception as applied to VR rendering.
- Unit 5: Motion, interaction, and audio rendering in virtual environments.
Open Elective-III
- Total: 3-0-0, 3 credits
- Selected from the university-wide open elective pool available to IoT students that semester.
Open Elective-IV
- Total: 3-0-0, 3 credits
- Choice between subjects such as Computer Networks or Quantum Science and Technology (covering quantum mechanics fundamentals, quantum information theory, quantum computing algorithms, quantum communication protocols like BB84, and emerging quantum hardware platforms).
Ethical Hacking (Skill Enhancement Course)
- Total: 0-1-2, 2 credits
- Unit 1: Basic system-hacking concepts and clearing forensic tracks.
- Unit 2: Advanced Windows configuration manipulation and registry-level tricks.
- Unit 3: Password cracking techniques across operating systems and services.
- Unit 4: Scripting fundamentals (Perl) applied to security tooling.
- Unit 5: Virus/malware mechanics and basic self-replicating code construction for educational analysis.
Constitution of India (Audit Course)
- Total: 2-0-0, 0 credits
- Unit 1: History of the Constitution’s drafting and its guiding philosophy.
- Unit 2: Fundamental rights, directive principles, and fundamental duties.
- Unit 3: Organs of governance — legislature, executive, and judiciary.
- Unit 4: Local administration — municipalities and panchayati raj institutions.
- Unit 5: The Election Commission and institutional safeguards for marginalized groups.
Evaluation of Industry Internship
- Total: 2 credits, no weekly contact hours
- Assessment of the industry internship completed during the prior semester’s vacation period.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The final semester is dedicated entirely to a full-time industry internship combined with the major project, giving students an extended, immersive capstone experience rather than coursework. There are no theory subjects or lab courses this term — the entire semester’s credit weight sits on a single internship-and-project track.
Subjects
Internship & Project Work
- Total: 0-0-24, 12 credits
- Full-semester placement combining an industry internship with the student’s capstone project.
- Structured as sustained hands-on engagement rather than discrete units, culminating in a project report and evaluation in place of end-semester examinations.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The third year opens with the program’s core machine-learning and networking courses alongside software engineering, plus the student’s first professional elective — a choice among automata/compiler theory, object-oriented analysis and design, soft computing, or IoT. A skill-enhancement full-stack web development course and a Flutter-based UI tinkering lab add practical breadth, and the community-service internship from the prior year is formally evaluated in this term.
Semester load: roughly 15 lecture, 1 tutorial and 10 practical hours per week, totaling 23 credits.
Subjects
Machine Learning
- Unit 1: Machine-learning paradigms, the modeling pipeline, and dataset considerations
- Unit 2: Proximity-based models such as k-nearest neighbors
- Unit 3: Decision-tree and Bayes-rule-based classifiers
- Unit 4: Linear discriminants — perceptrons, SVMs, logistic regression, and multi-layer perceptrons
- Unit 5: Clustering approaches, including k-means, fuzzy c-means, and spectral clustering
Computer Networks
- Unit 1: Network types, topologies, and the OSI/TCP-IP reference models
- Unit 2: Data-link layer framing, error control, and sliding-window protocols
- Unit 3: Media access control schemes and Ethernet standards
- Unit 4: Network-layer routing algorithms, congestion control, and IPv4/IPv6
- Unit 5: Transport-layer protocols (UDP/TCP) and application-layer services like HTTP, email, and DNS
Software Engineering
- Unit 1: Software life-cycle models from waterfall through agile and spiral approaches
- Unit 2: Project management, effort estimation, and requirements specification
- Unit 3: Software design principles, agile practices, and user-interface design
- Unit 4: Coding practices, testing strategies, and software quality standards
- Unit 5: CASE tools, software maintenance, and software reuse
Professional Elective-I options:
students choose one of Automata Theory & Compiler Design, Object Oriented Analysis and Design, Soft Computing, Internet of Things, or an approved NPTEL/SWAYAM course.
Automata Theory & Compiler Design
- Unit 1: Regular expressions, finite automata, and their equivalence
- Unit 2: Context-free grammars and pushdown automata
- Unit 3: Lexical analysis and top-down parsing
- Unit 4: Bottom-up parsing and syntax-directed translation
- Unit 5: Intermediate code generation and code optimization
Object Oriented Analysis and Design
- Unit 1: Managing complexity in large software systems
- Unit 2: UML fundamentals and structural modeling
- Unit 3: Class/object diagrams and advanced structural constructs
- Unit 4: Behavioral modeling — use cases, interactions, and activity diagrams
- Unit 5: Advanced behavioral and architectural modeling (state charts, components, deployment)
Soft Computing
- Unit 1: Neural network basics and biological inspiration
- Unit 2: Perceptron learning and backpropagation networks
- Unit 3: Fuzzy sets, relations, and membership functions
- Unit 4: Fuzzy inference systems and neuro-fuzzy hybrids
- Unit 5: Genetic algorithms and genetic-fuzzy hybrid systems
Internet of Things
- Unit 1: IoT overview, M2M communication, and connectivity principles
- Unit 2: Business models, layered IoT architectures, and standardization
- Unit 3: Web connectivity protocols for connected devices
- Unit 4: Data acquisition, organization, and business-process integration
- Unit 5: Cloud-based storage and computing for IoT, plus sensing/RFID technology
Machine Learning Lab
- Central-tendency and dispersion computations, and preprocessing techniques
- Implementing KNN, decision tree, and random forest classifiers
- Naïve Bayes, SVM, and multi-layer perceptron classification exercises
- Regression algorithms and clustering (k-means and related methods)
- Total: 3 practical hours per week, 1.5 credits
Computer Networks Lab
- Framing, checksum, and error-correction coding exercises
- Sliding-window and stop-and-wait protocol simulations
- Routing algorithm implementation (Dijkstra, distance-vector)
- Packet analysis with Wireshark and network scanning with Nmap
- NS2-based simulation of packet loss, congestion, and throughput
- Total: 3 practical hours per week, 1.5 credits
Full Stack Development-1 (Skill Enhancement Course)
- HTML structuring — lists, links, images, tables, forms, and frames
- CSS styling, selector types, and the box model
- JavaScript fundamentals — I/O, conditional logic, loops, and built-in/user-defined objects
- Functions, event handling, and form validation
- An introduction to Node.js
- Total: 1 tutorial and 2 practical hours per week, 2 credits
User Interface Design using Flutter (Tinkering Lab)
- Dart language basics and Flutter widget exploration
- Layout composition using Row, Column, and Stack widgets
- Responsive design and navigation between screens
- State management and custom widget/theme styling
- Form validation, animation, and REST API data fetching
- Total: 2 practical hours per week, 1 credit
Note: the Community Service Project Internship completed the previous summer is formally evaluated this semester, and students may alternatively take Entrepreneurship Development & Venture Creation in place of Open Elective-I.
by | Jul 11, 2026 | JNTUK R23 Syllabus
The second half of third year shifts focus to deep learning, operating systems, and data visualization as core subjects, with two elective slots covering topics from cryptography and cloud computing to computer vision and NoSQL databases. A soft-skills course and a technical-writing/IPR audit course prepare students for placements and research communication, alongside a mandatory summer industry internship or mini-project.
Semester load: roughly 20 lecture, 1 tutorial and 8 practical hours per week, totaling 23 credits.
Subjects
Deep Learning
- Unit 1: Biological neuron models and the perceptron learning algorithm
- Unit 2: Multilayer perceptrons, backpropagation, and regularization
- Unit 3: Modern optimizers (Adam, RMSprop, etc.) and training stabilization techniques
- Unit 4: Recurrent networks (LSTM, GRU) and convolutional architectures
- Unit 5: Recent developments — variational autoencoders and transformer models
Operating Systems
- Unit 1: OS services, system calls, and overall system structure
- Unit 2: Process management, threading models, and CPU scheduling
- Unit 3: Synchronization primitives and deadlock handling
- Unit 4: Memory management strategies and virtual memory
- Unit 5: File systems and protection mechanisms
Data Visualization
- Unit 1: The visualization process and principles of visual perception
- Unit 2: Building visual representations and visual-analytics workflows
- Unit 3: Classifying visualization systems and handling multi-dimensional/text data
- Unit 4: Visualizing hierarchical and network structures
- Unit 5: Visualizing volumetric, geographic, and collaborative data; evaluating visualizations
Professional Elective-II options:
Social Media Analytics, Cryptography & Network Security, Recommender Systems, Cloud Computing, or Sensor Networks.
Social Media Analytics
- Unit 1: Evolution of the web and characteristics of social media platforms
- Unit 2: The seven-layer social media analytics framework
- Unit 3: Text analytics techniques applied to social content
- Unit 4: Action analytics on social platforms
- Unit 5: Hyperlink analytics and viral-content detection
Cryptography & Network Security
- Unit 1: Core security concepts and classical encryption techniques
- Unit 2: Mathematical foundations of symmetric and asymmetric cryptography
- Unit 3: Block and stream ciphers, plus public-key algorithms (RSA, Diffie-Hellman, elliptic curve)
- Unit 4: Hash functions, message authentication, and digital signatures
- Unit 5: Transport, IP, and email security protocols
Recommender Systems
- Unit 1: Recommender system fundamentals and rating data
- Unit 2: Collaborative filtering approaches
- Unit 3: Content-based and knowledge-based recommendation
- Unit 4: Hybrid recommendation strategies
- Unit 5: Evaluation methods and the role of community/trust signals
Cloud Computing
- Unit 1: Cloud service and deployment models (IaaS/PaaS/SaaS, public/private/hybrid)
- Unit 2: Distributed computing foundations and service-oriented architecture
- Unit 3: Virtualization and container technologies (Docker, Kubernetes)
- Unit 4: Cloud economics, interoperability, and security challenges
- Unit 5: Serverless computing and cloud-centric IoT/edge computing
Sensor Networks
- Unit 1: Wireless network types and an introduction to sensor networks
- Unit 2: Single-node hardware architecture and network scenarios
- Unit 3: MAC and routing protocols for sensor networks
- Unit 4: Topology control, time synchronization, and localization
- Unit 5: Sensor node platforms, operating systems, and simulation tools
Professional Elective-III options:
Software Project Management, Quantum Computing, Computer Vision, NoSQL Databases, or an approved NPTEL/SWAYAM course.
Software Project Management
- Unit 1: Conventional software management and software economics
- Unit 2: Project life-cycle phases from inception through transition
- Unit 3–5: Project planning, tracking, organizational structures, and the tools used to manage schedule, cost, and resources
Quantum Computing
- Unit 1: Origins of quantum computing and qubits versus classical bits
- Unit 2: Underlying linear algebra and quantum-mechanical principles
- Unit 3: Qubit representation and quantum circuit design
- Unit 4: Core quantum algorithms (Deutsch-Jozsa, Shor, Grover)
- Unit 5: Quantum error correction and quantum cryptography
Computer Vision
- Unit 1: Camera models, radiometry, and shading
- Unit 2: Linear filtering, edge detection, and texture analysis
- Unit 3: Multi-view geometry and image segmentation
- Unit 4: Model fitting and motion tracking
- Unit 5: Geometric camera calibration and model-based vision
NoSQL Databases
- Unit 1: History and categories of NoSQL databases
- Unit 2: Comparing relational and NoSQL data models, replication, and sharding
- Unit 3: Document databases (MongoDB) and their use cases
- Unit 4: Column-family stores (HBase, Cassandra)
- Unit 5: Key-value and graph databases (Riak, Neo4j)
Deep Learning Lab
- Multi-layer perceptron and CNN implementations for image classification
- Text-classification exercises using embeddings and RNNs
- Transfer learning with pre-trained models such as VGG16
- Total: 3 practical hours per week, 1.5 credits
Data Visualization Lab
- Histogram, line-chart, and bar-chart exercises in R
- Box plots, scatter plots, and mosaic plots across sample datasets
- Heatmaps, geographic map visualizations, and 3D graphing
- Total: 3 practical hours per week, 1.5 credits
Soft Skills (Skill Enhancement Course)
- Unit 1: Analytical thinking, listening, and communication skills
- Unit 2: Self-management — time, stress, and anger management, plus workplace etiquette
- Unit 3: Grammar, correspondence, and professional writing
- Unit 4: Group discussions, resumes, and interview preparation
- Unit 5: Interpersonal relationships in professional settings
- Total: 1 tutorial and 2 practical hours per week, 2 credits
Technical Paper Writing & IPR (Audit Course)
- Unit 1: Fundamentals of technical report writing
- Unit 2: Drafting, editing, and plain-English writing conventions
- Unit 3: Proofreading and presenting final reports
- Unit 4: Word-processor techniques for formatting long documents
- Unit 5: Intellectual property fundamentals — patents, copyright, and the patenting process
- Ungraded audit course; no credits attached
Note: this semester is paired with a mandatory eight-week Industry Internship or Mini Project during the summer vacation.
