Data Science
CD 301
(Technical Communication)
CD-302
(Introduction to Probability and Statistics)
CS-303
(Data Structures)
CD-304
( Database Management Systems )
CD-305
(Object Oriented Programming & Methodology )
Syllabus
- 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.
- 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.
- 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 thousand, Mock Presentations.
- Job Interviews: Preparing for interviews, assessing yourself, Drafting Effective Resume, Dress, decorum and Delivery techniques, Techniques of handling interviews, Use of Non verbals 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.)
- 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.
Syllabus
- Unit 1: Basic Probability- Probability spaces, conditional probability, independence; Discrete random variables, Independent random variables, the multinomial distribution, Poisson approximation to the binomial distribution, infinite sequences of Bernoulli trials, sums of independent random variables; Expectation of Discrete Random Variables, Moments, Variance of a sum, Correlation coefficient, Chebyshev's Inequality.
- Unit 2: Continuous Probability Distributions- Continuous random varibales and their properties, distribution functions and densities, normal, exponential and gamma densities.
- Unit 3: Bivariate Distributions- Bivariate distributions and their properties, distribution of sums and quotients, conditional densities, Bayes' rule.
- Unit 4: Basic Statistics- Measures of Central tendency: Moments, skewness and Kurtosis - Probability distributions: Binomial, Poisson and Normal - evaluation of statistical parameters for these three distributions, Correlation and regression – Rank correlation.
- Unit 5: Applied Statistics- Curve fitting by the method of least squares- fitting of straight lines, second degree parabolas and more general curves. Test of significance: Large sample test for single proportion, difference of proportions, single mean, difference of means, and difference of standard deviations.
- Unit 6: Small samples- Test for single mean, difference of means and correlation coefficients, test for ratio of variances - Chi-square test for goodness of fit and independence of attributes.
Syllabus
- 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.
- 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.
- 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.
- 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.
- 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.
Syllabus
- Basic Concepts of Data and Information, Overview of Information Systems, File organization and access methods; Introduction to DBMS, Difference between DBMS and traditional file storage system. Characteristics of DBMS. Data Models, Schemas and Instances, DBMS architecture, Components of DBMS. Data Independence. Study of Entity Relationship Model, Type of attributes, Entity types, Relationship and Cardinalities, Participation, Roles and constraints.
- Relational Data Model: Domains, Tuples, Attributes, Relations, keys and types of keys, Integrity Constraints, Relational Algebra: Queries using Select operation, project operation, renaming, joins, union, intersection, difference, division, and product etc. Relational Calculus, Tuple calculus. Query Language: SQL –basic SQL queries, functions, constraints, joins and nested queries, QBE (Query By Example), Indexing, and PL/SQL.
- Normalization Theory and Database methodologies: Relation Schemas, Functional Dependencies- Definition and rules of axioms, Normal forms- 1NF, 2NF, 3NF and BCNF, Dependency preservation, properties, loss less join decomposition. Query Processing and Optimization: Various algorithms to implement select, project & join operation of relational algebra, complexity measures.
- Transaction Processing: Introduction to Concurrency and Recovery, Read and Write Operations, Transaction properties, Transaction states, Schedules, Serializability, types of serializability and test for serializability, Concurrency Control: Types of Locks, Timestamp Based, Validation Based etc. Multiversion schemes, Recovery: Basic concepts, techniques based on deferred update and immediate update, Shadow paging, check points.
- Storage structure: Secondary Storage Devices, RAID, Heap Files and Sorted files, Hashing techniques, Indexing techniques: Bitmap Indices, Case Study of any contemporary DBMS.
Syllabus
- 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.
- Encapsulation and Data Abstraction- Concept of Objects: State, Behavior & Identity of an object; Classes: identifying classes and candidates for Classes Attributes and Services, Access modifiers, Static members of a Class, Instances, Message passing, and Construction and destruction of Objects.
- Relationships – Inheritance: purpose and its types, ‘is a’ relationship; Association, Aggregation. Concept of interfaces and Abstract classes.
- Polymorphism: Introduction, Method Overriding & Overloading, static and run time Polymorphism. Virtual Function, friend function, Static function, friend class.
- Strings, Exceptional handling, Introduction of Multi-threading and Data collections. Case study like: ATM, Library management system.