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所在平台: Udemy |
课程主页: https://www.udemy.com/course/jeca-wb-joint-entrance-for-computer-application-preparation/
课程评论：没有评论
课程名称：JECA、西孟加拉州准备课程与MCQ解题技巧 课程概述：2022年，JECA（西孟加拉城镇入学考试）的考试大纲调整为计算机科学或应用程序。本课程涵盖了所有大纲内容，通过UGC NET班级的录播讲座，学生将在课程中学习如何解决计算机科学的多项选择题（MCQ）。所有讲座中包含多种示例和解题快捷技巧，旨在提升学生解决计算机科学MCQ的能力。本课程也将对希望在印度西孟加拉州各大学就读计算机科学硕士课程的学生有所帮助。所有视频均为孟加拉语。 课程内容主要涵盖以下主题： 1. C程序设计：变量与数据类型、输入输出操作、运算符与表达式、控制流语句、函数、数组、指针、字符串处理、结构体与共用体、文件处理、预处理指令、命令行参数。 2. 面向对象编程：数据类型、条件语句、循环、函数、开关语句、指针、结构体、数组、字符串、函数重载、函数模板、变量作用域、类型别名（typedef/using）、共用体、枚举类型、类、构造函数、构造函数重载、成员初始化、指向类的指针、运算符重载、关键字“this”、静态成员、常量成员函数、类模板、模板特化、命名空间、友元（友元函数与类）、继承、多态、虚成员和抽象基类。 3. Unix：命令及其不同选项，如ls、ps、pwd、mv、cp、touch、cat等。 4. 数据结构：搜索、排序、栈、队列、链表、树、图。 5. 计算机基础：总线结构、基本输入输出、子程序、中断、DMA、RAM、ROM、管道、系统调用。 6. 操作系统：进程、线程、CPU调度、死锁、同步、内存管理、磁盘管理、文件管理。 7. 计算机网络：网络概念、应用领域、分类、参考模型、传输环境与技术、路由算法、IP、UDP和TCP协议、IPv4和IPv6、可靠数据传输方法、应用协议、网络安全、管理系统及通信网络的视角。 8. 数据库管理系统：数据库介绍、ER图、关系代数、关系演算、SQL、规范化、事务、索引、查询优化。 9. 软件工程：软件工程简介、过程的通用视角、过程模型、软件需求、需求工程过程、系统模型、设计工程、测试策略、产品度量、过程与产品的度量、风险管理、质量管理。 10. 机器学习：分类、决策树学习、人工神经网络、支持向量机、贝叶斯学习、聚类、隐马尔可夫模型。 该课程旨在帮助学生全面掌握计算机科学的MCQ解题技巧，提升其在相关考试中的表现。
In 2022, JECA, WB Exam syllabus changed to computer science or applications. In this course, all syllabus are covered with previously recorded lecture from UGC NET Batch, so students will learn how to solve MCQ Questions of Computer science. All lectures are include various example and shortcut tricks to solve MCQ on Computer Science and Applications. Main aim of this course is to make capable to solve MCQ Computer Science. This course will also be helpful to those students who are seeking admission for MSc in Computer Science courses in various Universities in West Bengal, India. All videos are in Bengali. JECA Syllabus are based on the following Topics- 1. C Programming: Variables and Data types, IO Operations, Operators and Expressions, Control Flow statements, Functions, Array, Pointers, String Handling, Structures and Unions, Files Handling, Pre-Processor Directives, Command Line Arguments.2. Object Oriented Programming: Data Types, If / Else If / Else, Loops, Function, Switch case, Pointer, Structure, Array, String, Function Overloading, Function templates, SCOPE of variable, Type aliases (typedef / using), Unions, Enumerated types (enum), Class, Constructors, Overloading Constructors, Member initialization in constructors, Pointers to classes, Overloading Operators, Keyword ‘this', Static Members, Const Member Functions, Class Templates, Template Specialization, Namespace, Friendship (Friend Functions & Friend Classes), Inheritance, Polymorphism, Virtual Members, Abstract base class.3. Unix: Following commands and its different options: Is, ps, pwd, mv, cp, touch, cat, time, cal, bc, sort, diff, wc, comm, In, du, kill, sleep, chmod, chown, chgrp, top, nice, renice, cut, paste, grep, file, whereis, which, echo, env, PATH, CLASSPATH, find.vi editor, shell, wildcard, shell script.4. Data Structure: Searching, Sorting, Stack, Queue, Linked List, Tree, Graph.5. Introduction of Computers: Bus structure, Basic I/O, Subroutines, Interrupt, DMA, RAM, ROM, pipeline, system calls.6. Operating System: Process, Thread, CPU Scheduling, Deadlock, Synchronization, Memory Management, Disk Management, File Management.7. Computer Network: Concepts of networking, Application areas, Classification, Reference models, Transmission environment & technologies, Routing algorithms, IP, UDP & TCP protocols, IPv4 and IPv6, Reliable data transferring methods, Application protocols, Network Security, Management systems, Perspectives of communication networks.8. Database Management System: Introductions to Databases, ER diagram, Relational Algebra, Relational Calculus, SQL, Normalization, Transactions, Indexing, Query optimization.9. Software Engineering: Introduction to Software Engineering, A Generic view of process, Process models, Software Requirements, Requirements engineering process, System models, Design Engineering, Testing Strategies, Product metrices, Metrices for Process & Products, Risk management, Quality Management.10. Machine Learning: Classification, Decision Tree Learning, Artificial Neural Networks, Support Vector Machines, Bayesian Learning, Clustering, Hidden Markov Models.
