Syllabus of B. Tech III Sem AIDS (RGPV)
Syllabus of B. Tech. III Sem AIDS (RGPV)
Syllabus of AD-301 (Technical Communication)
Source: (rgpv.ac.in)
UNIT-1 : Technical Communication Skills
Understanding the process and scope of Communication,
Relevance, & Importance of Communication in a Globalized world,
Forms of Communication,
Role of Unity,
Brevity and Clarity in various forms of communication.
UNIT-2 : Types of Communication
Verbal & Non-verbal Communication,
Classification of NVC,
Barriers to Communication,
Communicating Globally, Culture and Communication.
Soft Skills: Interpersonal Communication, Listening, Persuasion, Negotiation,
Communicating bad news/messages,
communicating in a global world.
UNIT-3 : Writing Skills
Traits of Technical Writing,
Principles of Business Writing,
Style of Writing,
Writing Memos, Letters,
Reports, and Types of technical reports,
Characteristics,
format and structure of technical reports,
Writing Research Papers.
Speaking Skills: Audience-awareness,
Voice, Vocabulary and Paralanguage,
Group Discussion,
Combating Nervousness,
Speaking to one and to one Mock Presentations.
UNIT-4 : Job Interviews
Preparing for interviews
assessing yourself,
Drafting Effective Resume,
Dress, decorum and Delivery techniques,
Techniques of handling interviews,
Use of Nonverbals during Interviews,
Handling turbulence during interviews.
Group Discussion: Objective, Method, Focus,
Content, Style and Argumentation skills.
Professional Presentations: Individual Presentations.
(Audience Awareness, Body Language, Delivery and Content of Presentation.)
UNIT-5 : Grammar & Linguistic ability: Basics of grammar
common error in writing and speaking,
Study of advanced grammar,
Vocabulary, Pronunciation Etiquette,
Syllables, Vowel sounds, Consonant sounds,
Tone: Rising tone, Falling Tone,
Flow in Speaking, Speaking with a purpose,
Speech & personality, Professional Personality Attributes.
== END OF UNITS==
Syllabus of AD-302 (Probability and Statistics for Data Science)
Source: (rgpv.ac.in)
Unit-1 : Data Science
Introduction, Data Science Life Cycle
Statistics: Descriptive and Inferential Statistics,
Measures of central tendency: Arithmetic Mean, Median and Mode.
Geometric mean, Harmonic Mean and Partition values.
Measures of dispersion: Dispersion, Range, Quartile Deviation, Mean deviation,
Standard Deviation, Variance and Coefficient of Dispersion.
UNIT-2 : Theory of probability and Probability
Skewness, Kurtosis, Moments, Measure of skewness and kurtosis.
Theory of probability: Introduction and definition of Probability,
Event, Sample Space, Law of addition and multiplication of Probabilities and Conditional
Probability. Independent and Dependent events, Bayes’ theorem,
Mathematical Expectations and Moment generating functions.
UNIT-3 : Theoretical Distribution and Curve fitting
Discrete Distribution- Binomial Distribution and Poisson Distribution.
Continuous Distribution –Rectangular and Normal distribution.
Curve fitting: Curve fitting and Methods of Least square,
fitting a Straight line and a Parabola.
UNIT-4 : Correlation and Regression
Correlation, Coefficient of Correlation, Rank Correlation,
Lines of Regression.
Multiple and Partial Correlation.
UNIT-5 : Testing of hypothesis
Null and Alternative hypothesis, two types of errors,
level of significance and power of the test.
Tests of significance: Chi-square distribution,
test of popular variance and test of goodness of fit. t, F ,Z distribution and tests based on them.
== END OF UNITS==
Syllabus of AD-303 (Data Structures)
Source: (rgpv.ac.in)
Unit-1 : Introduction to Data Structure
Concepts of Data and Information,
Classification of Data structures,
Abstract Data Types,
Implementation aspects: Memory representation.
Data structures operations and its cost estimation.
Introduction to linear data structures- Arrays,
Linked List: Representation of linked list in memory,
different implementation of linked list.
Circular linked list, doubly linked list, etc.
Application of linked list: polynomial manipulation using linked list, etc.
UNIT-2 : Stacks and Queue
Stacks as ADT,
Different implementation of stack,
multiple stacks.
Application of Stack: Conversion of infix to postfix notation using stack,
evaluation of postfix expression,
Recursion. Queues: Queues as ADT,
Different implementation of queue,
Circular queue, Concept of Dqueue and Priority Queue,
Queue simulation, Application of queues.
UNIT-3 : Tree
Definitions - Height, depth, order, degree etc.
Binary Search Tree - Operations, Traversal, Search.
AVL Tree, Heap, Applications and comparison of various types of tree;
Introduction to forest, multi-way Tree, B tree, B+ tree, B* tree and red-black tree.
UNIT-4 : Graphs
Introduction, Classification of graph: Directed and Undirected graphs, etc,
Representation,
Graph Traversal: Depth First Search (DFS), Breadth First Search (BFS),
Graph algorithm: Minimum Spanning Tree (MST)-Kruskal, Prim’s algorithms.
Dijkstra’s shortest path algorithm; Comparison between different graph algorithms.
Application of graphs.
UNIT-5 : Sorting
Introduction, Sort methods like: Bubble Sort, Quick sort. Selection sort, Heap sort, Insertion sort, Shell sort, Merge sort and Radix sort;
comparison of various sorting techniques.
Searching: Basic Search Techniques, Sequential search, Binary search,
Comparison of search methods.
Hashing & Indexing.
Case Study: Application of various data structures in operating system, DBMS etc.
== END OF UNITS==
Syllabus of AD-304 (Artificial Intelligence)
Source: (rgpv.ac.in)
Unit-1 :
Fundamental of Artificial Intelligence,
history, motivation and need of AI,
Production systems, Characteristics of production systems ,
goals and contribution of AI to modern technology, search space,
Different search techniques: hill Climbing, Best first Search, heuristic search algorithm, A* and AO* search techniques etc.
UNIT-2 :
Knowledge Representation, Problems in representing knowledge,
knowledge representation using propositional and predicate logic,
comparison of propositional and predicate logic, Resolution, refutation,
Deduction, theorem proving, inferencing, monotonic and non-monotonic reasoning.
UNIT-3 :
Probabilistic reasoning, Baye's theorem,
semantic networks, scripts,
schemas, frames, conceptual dependency,
forward and backward reasoning.
UNIT-4 :
Game playing techniques like minimax procedure, alpha-beta cut-offs etc,
planning, Study of the block world problem in robotics,
Introduction to understanding, natural language processing (NLP), Components of NLP, application of NLP to design expert systems.
UNIT-5 :
Expert systems (ES) and its Characteristics, requirements of ES,
components and capability of expert systems,
Inference Engine Forward & backward Chaining,
Expert Systems Limitation, Expert System Development Environment,
technology, Benefits of Expert Systems.
== END OF UNITS==
Syllabus of AD-305 (Object Orien ted Programming & Methodology)
Source: (rgpv.ac.in)
Unit-1 : Introduction to Object Oriented Thinking & Object Oriented Programming
Comparison with Procedural Programming,
features of Object oriented paradigm– Merits and demerits of OO methodology;
Object model; Elements of OOPS, IO processing, Data Type, Type Conversion, Control Statement, Loops, Arrays.
UNIT-2 : Encapsulation and Data Abstraction
Concept of Objects: State, Behavior & Identity of an object;
Classes: identifying classes and candidates for Classes Attributes and Services,
modifiers, Static members of a Class, Instances,
Message passing, and Construction and destruction of Objects.
UNIT-3 : Relationships
Inheritance: purpose and its types, ‘is a’ relationship;
Association, Aggregation.
Concept of interfaces and Abstract classes.
UNIT-4 :
Polymorphism: Introduction, Method Overriding & Overloading,
static and runtime Polymorphism.
Virtual Function, friend function, Static function, friend class.
UNIT-5 :
Strings, Exceptional handling,
Introduction of Multi-threading and Data collections.
Case study like: ATM, Library management system.
== END OF UNITS==
Syllabus of AD-306 (Computer Workshop/Introduction to Python-I)
Source: (rgpv.ac.in)
Module-1 :
Introduction to python language,
Basic syntax, Literal Constants, Numbers,
Variable and Basic data types, String, Escape Sequences,
Operators and Expressions, Evaluation Order, Indentation,
Input, Output, Functions, Comments.
Module-2 :
Data Structure: List, Tuples, Dictionary,
DataFrame and Sets, constructing,
indexing, slicing and content manipulation.
Module-3 :
Control Flow: Conditional Statements - If, If-else,
Nested If-else.
Iterative Statement - For, While, Nested Loops.
Control statements - Break,
Continue, Pass.
Module-4
Object oriented programming: Class and Object, Attributes,
Methods, Scopes and Namespaces,
Inheritance, Overloading, Overriding,
Data hiding,
Exception: Exception Handling, Except clause,
Try finally clause, User Defined Exceptions.
Module-5 :
Modules and Packages: Standard Libraries: File I/0,
Sys, logging, Regular expression,
Date and Time, Network programming,
multi-processing and multi threading.
== END OF UNITS==