Friday, February 19, 2021
Monday, February 15, 2021
BSc CSIT 6th Semester Syllabus | All Subject Syllabus BSc. CSIT 6th Sem |
BSc CSIT 6th Semester Syllabus | All Subject Syllabus BSc. CSIT 6th Sem
Download csit 6th-semester subject wise Syllabus:
Total Cost of Studying BSc. CSIT in Nepal, Scope and Average Salary of IT professionals in Nepal
Total Cost of Studying BSc. CSIT in Nepal, Scope and Average Salary of IT professionals in Nepal
IT industry has resonated to be one of the biggest and the speediest developing industries in the world. Due to different advantages like liberal pay, adaptable working hours, and inventive working environment Data Innovation is one of the foremost sought-after courses in Nepal and the globe.
Bachelor of Science in Computer Science and Information Technology (BSc. CSIT) is a Four-year (8 Semesters) course affiliated to Tribhuvan University College outlined to supply the understudy with all sorts of information within the field of Data Innovation and Computing. This course is offered by 53 constituent and associated colleges of Tribhuvan College all over the country.
The major subjects taught in BSc. CSIT is as following:
- Operating Systems
- Computer Architecture & Organization
- Software Engineering
- Object-Oriented Programming
- Data Warehousing and Mining
- JAVA Programming and Website Design
- Data Structures
- Foundations of Computer Systems
- Design and Analysis of Algorithms
- Database Management Systems
- Multimedia Applications
- E-Commerce and ERP
- Computer Networks
What is the average cost of studying for BSc. CSIT in Nepal?
"Average Cost is Rs.10 Lakh"
The average cost that will be incurred by students to complete their four-year full-time undergraduate program specializing in Information Technology is in the range of Rs 5-15 lakh in total. The average cost for an IT postgraduate program is in the range of Rs 10 lakh.
What Are the professional Scope of BSc. CSIT in Nepal?
The scope of IT is increasing with time. CSIT students can involve in the following major areas.
- Software Engineer
- IT Administrator
- Project Manager
- IT Business Consultant
- Senior Software Programmer/Developer
- Systems Developer
- Web Developer
- Database Manager
- Network Administratve
What is the Average Salary of IT professionals in Nepal?
The average salary of IT professionals in Nepal is relatively higher than in other sectors of Nepal. The average range of the salary is between Rs. 17000 to Rs. 75000. So the average salary is around Rs. 46000.
Find All Blogs about BSc. CSIT (csit ) Here
The Complete 2021 Web Development Course Free Download
The Complete 2021 Web Development Course Free Download
Free Download for CSIT students
What you’ll learn
Go from Zero skills to building Powerful Web Applications on a highly professional level using the latest 2020 Web Technologies.
Use a Portfolio of over 15 highly professional websites, Games and Mobile apps you would have developed during the course to take your career to the next level.
Create real life mobile apps and upload them to the IOS App Store and Google Play.
Use HTML5, CSS3, Flexbox, Grid & SASS to build website content and add stunning styling and decoration.
Use Javascript, jQuery & jQuery User Interface to create Interactive Websites and Games.
Use Twitter Bootstrap to produce Responsive Websites that will adapt to any device size.
Sunday, February 7, 2021
Thursday, February 4, 2021
CSIT 5th Semester Image Processing Lab | BSc. CSIT Image Processing IP [5th sem]
CSIT 5th Semester Image Processing Lab | BSc. CSIT Image Processing IP [5th sem]
Tuesday, February 2, 2021
CSIT 5th Semester Simulation And Modelling Lab | BSc. CSIT 5th Sem Simulation
CSIT 5th Semester Simulation And Modelling Lab | BSc. CSIT 5th Semester Simulation
Monday, February 1, 2021
Saturday, December 26, 2020
CSIT 1st Sem Physics Old Question Collection | BSc CSIT Old Question List (2065-2075)
CSIT 1st Sem Physics Old Question Collection |Physics BSc CSIT Old Question List (2065-2075)
Physics All Numerical Solution
Tuesday, December 15, 2020
CSIT 5th Semester Image Processing Text Book | Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing” |
CSIT 5th Semester Image Processing Text Book | Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”
DOWNLOAD FREE BOOK
Thursday, December 3, 2020
An Article on Software Development | Simple Guide Article For CSIT Freshers | CSIT 1st Semester Student Intro to Software Development
An Article on Software Development | Simple Guide Article For CSIT Freshers | CSIT 1st Semester Student Intro to Software Development
For all industries affected by software and digitization, 2019 will be a great year for software development in the US with a number of exciting developments.
Below you will find everything you need to know about the career of a software developer and how to become one. Software developers usually have a bachelor's degree in computer science, computer engineering or computer programming. While bachelor's graduates can become software developers and computer programmers, associate graduates can do a job in web development. Many students gain experience in software development during their studies by doing internships with software companies.
This type of career generally involves a lot of collaboration with different stakeholders. In general, software development is a collaborative process, and software developers must be able to work well with others who also contribute to the development, development and programming of successful software. Software developers are responsible for the sketching and creation of the code for design and programming. Developers must work in teams that work with teams so that others contribute to the design, development and programming of successful software.
Depending on the software development method, development teams may need to maintain stable communication channels. Make sure your software development team takes a software delivery approach that creates software, rather than trying to deliver it all at once.
Depending on the software development process you follow, this phase of the SDLC means creating simple wireframes to show how your software interacts, or creating full-fledged prototypes using tools like Marvel InVision to test with users. This phase is also a good time to start sprint planning through the Agile Software Development process and to break down large tasks into more actionable steps. The waterfall software development process works best when there are goals, requirements, and stacks of technology, even when they change. It is an incremental and iterative software development process, but it can work well if the goals and requirements of the technology change the way they do.
The flexibility of computer programming is essentially limitless, so it is hard to imagine what software development will look like if intelligent programs can help you interact with your code. AI research is broad-based, but in the meantime, some of the early examples of AI-supported software development are already giving us an insight into what we can expect from the future of our code, and that's great.
These six steps are known as the software development lifecycle and are summarized in so-called "six steps," which are called the "software development lifecycle" or SDLC for short. So let's start by understanding the core building blocks of the SDLC and then see how to optimize them to select the right software - development process for your team.
The process of software development lifecycle is a thorough method to control and manage software at the highest level. This is the process by which the software developed goes through needs analysis, development, testing, deployment and finally maintenance.
It is always wise to inquire about the software development process of competing software development companies. The best process for software development is for companies that need an adaptive approach, as some of them are more transformative than others and therefore need to work faster to meet customer market requirements. Such development has a lot of advantages, as it can also be used to automatically organize and plan software development projects. The software development experts also examine the feasibility of software development and understand the expectations of customers based on the data collected.
In the iterative software development process, simple shapes, new functions and functions are added through gradual product enhancements. In the iterative development process of the software, however, each version contains a version of the planned features in the release.
Programmers usually interpret the instructions of software developers and engineers and use programming languages such as C or Java to execute them. The program design created by the software developer or engineer turns into instructions that a computer can follow.
Agile recognizes that requirements can and should change during software development, and it fuels the idea that software should be developed and delivered step by step. Before Agile was created, most development projects used a kind of waterfall development process, recognizing that they used a combination of waterfall and agile development processes such as continuous integration and continuous deployment. In addition, agile is considered the best method of software development in some companies. Part of it is that it is agile in its design and implementation, but more complete.
Now software developers can use AI to write code, check it, spot errors, and even optimize the development of a project.
Much of the actual creation of software programs is done by writing code, and software developers monitor and monitor this. Software developers usually work in an office environment, but can be closely involved in certain areas of the project, including writing code. They have less formal roles than engineers and many are information technology specialists. Although it is not primarily a programmer, software code is generated in many different ways, from programming languages to databases to data structures.
Read all interesting blog article about csit and csit related topics: More Articles
Sources:
[0]: https://www.rasmussen.edu/degrees/technology/blog/what-does-software-developer-do/
[1]: https://www.forbes.com/sites/simonchandler/2020/02/05/how-ai-is-making-software-development-easier-for-companies-and-coders/
[2]: https://collegegrad.com/careers/software-developers
[3]: https://artificialintelligence-news.com/2019/11/25/opinion-ai-software-development-here/
[4]: https://plan.io/blog/software-development-process/
[5]: https://usersnap.com/blog/software-development-methodologies/
[6]: https://www.computerscience.org/careers/software-developer/
[7]: https://www.synapseindia.com/6-stages-of-software-development-process/141
[8]: https://www.goodfirms.co/directory/languages/top-software-development-companies
[9]: https://www.entrepreneur.com/article/339625
[10]: https://towardsdatascience.com/20-predictions-about-software-development-trends-in-2020-afb8b110d9a0
[11]: https://cobuildlab.com/blog/best-software-development-process/
[12]: https://www.ibm.com/topics/software-development
Machine Learning Simply The Future | CSIT Students Must Read Article about Machine Learning
Machine Learning Simply The Future | CSIT Students Must Read Article about Machine Learning
Although machine learning has been around for decades, it is becoming increasingly popular as artificial intelligence (AI) gains in importance. Machine learning (ML) has entered a new era of innovation in computer science and machine intelligence. While the use of machine learning is on the rise, companies are also developing special hardware tailored to the operation and training of machines - learning models.
One of the machines - learning algorithms used by Facebook, Google and others - is something called deep neural networks or deep learning.
Simply put, a machine-learning algorithm uses the patterns in training data to perform classifications and future predictions. Data scientists define the correlations that the algorithm is supposed to evaluate and label, and the user then applies the self-learning algorithms to uncover insights, determine relationships, and make predictions about future trends. Machine learning algorithms are often divided into two parts: training - data tagged with answers and terms that may exist that are not displayed on the training algorithm.
Machine learning is the first subset, and it is a subset of AI that is itself an AI; not all AI is machine learning and so on. Machine learning is the subject of much discussion in the field of artificial intelligence (AI) research.
Machine learning is a subset of AI that is AI, which is itself a computer program that does something intelligent. Deep learning, on the other hand, is also a subset of machine learning in the sense that it is an AI in itself.
More specifically, machine learning is an approach to data analysis that involves creating models that allow a program to learn from experience. An important distinction is that the result of a trained and accurate algorithm is not necessarily a machine-learning model (although even machine-learning mice do not use algorithm and model interchangeably). Machine learning and deep learning both go through an optimization process to find the weights that best match the model to the data.
Generalization is a concept in machine learning that tells us how well a model can work with data that has not been seen before. Machine learning is a form of lazy learning, because the generalization of training data only occurs when a query is made to the system.
Machine learning algorithms can detect patterns and correlations, meaning they are able to analyze their own ROI. One way to classify the type of problem that a machine learning algorithm solves is the type of problem it solves. So the best way to understand how machine learning works is to understand the tasks they solve, and then see how they try to solve those problems.
One aspect that distinguishes machine learning from knowledge graphs and expert systems is that it can be modified when exposed to more data. The ability to adapt to new inputs and make predictions is a crucial part of the generalization of machine learning. Machine learning is dynamic (i.e. it requires human intervention to make certain changes) and is dynamically modified when the algorithm makes its predictions more accurate. Classical machine learning is divided into two categories: classical machine learning, in which an algorithm learns from a large amount of data before making a forecast, and classical - in - training, in which it learns only from the data at the beginning of the learning process. In the case of problems with monitored machine learning, machine-learned algorithms are described as monitored machine learning algorithms, because they are designed for monitored problems with machine learning.
Machine learning is related to computer-based statistics, so a background knowledge of statistics is important to use machine learning. The first choice for those who want to learn new programming is machine learning, but I usually prefer the application to other fields such as computer science, mathematics and computer engineering.
Machine learning allows an AI to process and learn data and become smarter without the need for additional programming. The key idea behind active learning is that machine learning can achieve greater accuracy if it is allowed to select the data it learns from. Supervised machine learning facilitates training, as the results of the model can be compared with the actual labelled results. It does not require programming, but only a basic understanding of statistics and a good amount of training data.
Human bias plays a role in the collection, organization, and organization of data, while the algorithm determines how machine learning interacts with the data.
These are considerations that should be kept in mind when working with machine learning methods and analyzing the effects of the machine learning process. There are three terms that are often used interchangeably to describe software that behaves intelligently. People tend to call everything artificial intelligence, whether it's a phone that uses deep learning for facial recognition or a travel app that uses a machine learning algorithm to define the best time to buy a plane ticket. In this article, we will cover three of these approaches, as well as a number of other methods of machine learning, such as deep neural networks, machine memory, and image processing.
Sources:
[0]: https://data-flair.training/blogs/machine-learning-tutorial/
[1]: https://www.brookings.edu/research/what-is-machine-learning/
[2]: https://www.datarobot.com/wiki/machine-learning/
[3]: https://deepai.org/machine-learning-glossary-and-terms/machine-learning
[4]: https://machinelearningmastery.com/types-of-learning-in-machine-learning/
[5]: https://www.infoworld.com/article/3512245/deep-learning-vs-machine-learning-understand-the-differences.html
[6]: https://www.sciencedirect.com/topics/computer-science/machine-learning
[7]: https://www.pcmag.com/news/the-business-guide-to-machine-learning
[8]: https://steelkiwi.com/blog/what-is-machine-learning/
[9]: https://www.ibm.com/topics/machine-learning
[10]: https://pathmind.com/wiki/ai-vs-machine-learning-vs-deep-learning
[11]: https://www.digitalocean.com/community/tutorials/an-introduction-to-machine-learning
[12]: https://www.sap.com/insights/what-is-machine-learning.html
[13]: https://searchenterpriseai.techtarget.com/definition/machine-learning-ML
[14]: https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0
[15]: https://www.zdnet.com/article/what-is-machine-learning-everything-you-need-to-know/
BSc. CSIT 2nd Sem Statistics Numerical Solution | CSIT | Second Semester | Numerical Solution
BSc. CSIT 2nd Sem Statistics Numerical Solution
Find bsc csit 2nd sem Old Question here: BSc. CSIT 2nd Sem Old Question Collection
Bsc CSIT 2nd Sem Microsyllabus: BSc. CSIT 2nd Sem Microsyllabus
Tuesday, October 13, 2020
Technical Writing BSc CSIT Complete Note | bsc csit 6th sem note Technical Writing
Technical Writing BSc CSIT Complete Note | bsc csit 6th sem note Technical Writing
Software Engineering BSc CSIT Complete Note | bsc csit 6th sem note e governance
Software Engineering BSc CSIT Complete Note | bsc csit 6th sem note e governance
Compiler Design and Construction (CDC) BSc CSIT Complete Note | bsc csit 6th sem note CDC
Compiler Design and Construction (CDC) BSc CSIT Complete Note | bsc csit 6th sem note CDC
Thursday, October 8, 2020
SEO 2020 Complete SEO Training + SEO for WordPress Websites | Free SEO course for CSIT students
SEO 2020 Complete SEO Training + SEO for WordPress Websites
Course Contents :- Introduction to SEO 2020 Course
- SEO 2020 Keyword Research in SEO
- SEO 2020 Content SEO
- SEO 2020 Technical Factors in SEO
- SEO 2020 How I Got 100100 PageSpeed Insights Score from Google
- SEO 2020 Get Indexed by Search Engines Faster
- SEO 2020 Demystifying Backlinks SEO
- User Experience SEO - Future SEO Factor
- Protect against Negative SEO
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Sunday, October 4, 2020
BSc csit 6th sem micro syllabus | CSIT Micro syllabus for 6th sem
BSc csit 6th sem micro syllabus | CSIT Micro syllabus for 6th sem
CSIT 6th semester micro syllabus. 6th Sem has 6 subjects in total where 1 is elective for csit students
DOWNLOAD MICRO SYLLABUS
Tuesday, September 29, 2020
Tuesday, September 8, 2020
Compilers: Principles, Techniques, and Tools (BSc CSIT 6th Semester Compiler Design Book Pdf Free Download)
Compilers: Principles, Techniques, and Tools (BSc CSIT 6th Semester Compiler Design Book)
Monday, September 7, 2020
BSc CSIT 3rd Sem Old Questions Solutions (BSc CSIT 3rd Semester)
BSc CSIT 3rd Sem Old Questions Solutions (BSc CSIT 3rd Semester)
Saturday, July 25, 2020
BSc. CSIT course of study and Tribhuvhan University(TU) Code of Conduct
BSc CSIT Syllabus, Course of Study, Credit Hours, Code of Conduct, Microsyllabus of 2 Years
FREE DOWNLOAD
TRIBHUVAN UNIVERSITY
INSTITUTE OF SCIENCE AND TECHNOLOGY
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BACHELOR OF SCIENCE IN COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
(COURSE OF STUDY)
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EFFECTIVE FROM ACADEMIC YEAR – 2074
Tribhuvan University
Institute of Science and Technology
Course of Study
Bachelor of Science in Computer Science and Information Technology
(B.Sc. CSIT)
2017
Prepared by
Computer Science and Information Technology Subject Committee
Introduction:
The curriculum of the Bachelor of Science in Computer Science and Information Technology (B.Sc. CSIT) is structured by closely observing the courses offered at approved foreign universities, subject to the requirement that the Bachelor of Science and Information Technology (B.Sc. CSIT). Students' intake is twelve years of science schooling or equivalent to any university approved by Tribhuvan University (TU). In addition to the foundation and core Computer Science and Information Technology courses, the program offers several elective courses to fulfill the demand for high technology applications development. The foundation and core courses are designed to meet the undergraduate academic program requirement, and the service courses are designed to meet the need for fast-changing computer technology and application. Students enrolled in the four year B.Sc. CSIT program are expected to take courses in computer information systems design and implementation, foundation in the computer science theoretical model, and functional background of computer hardware. All undergraduate students are required to complete 126 credit hours of computer science courses and allied courses.
Objective:
The key aim of the B.Sc. CSIT curriculum is to provide students with comprehensive knowledge and skills on various fields of computer science and information technology including computer system design, theory, programming and implementation.
- Should have successfully completed twelve years of schooling in the science stream or equivalent form any university, board or institution.
- Should have secured a minimum of second division.
- Should have successfully passed the entrance examination conducted by Institute of Science and Technology (IOST), TU.
Course Duration:
The entire course is of eight semesters (four academic years). There is a separate semester examination after the end of each semester.
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Hours of Instruction:
a) 1Working days: 90 days in a semester
b) Class hours:
- 3 credit hour courses with theory and lab are equivalent to 3 hours theory and 3 hours lab = 6 working hours per week.
- 3 credit hours theory-only course is equivalent 3 hours theory and 2 hours tutorial = 5 working hours per week.
Evaluation Theory course should have an internal weightage of 20% and an external weightage of 80%. For the course of having lab work, the internal weightage is 20%, lab work weightage is 20% and external weightage is 60%. A student should secure a minimum of 40% in each category to pass a course. The final score in each course will be the sum of the overall weightage of all categories. There will be a separate practical examination for the 20% weightage of lab work conducted by the concerned college in the presence of an external examiner. The project work and internship are evaluated by different evaluators. To pass project work and internship, students should secure at least 40% marks in the evaluation of each evaluator and the final score will be the sum of all the evaluations. For the evaluation of the final presentation, an external examiner will be assigned from the IOST. The Grading System A student having passed his/her 8 semesters (4 years) of study will be graded as follows
- Distinction: 80 % and above ( 8 semester’s average)
- First Division: 70 % and above ( 8 semester’s average)
- Second Division: 55 % and above ( 8 semester’s average)
- Pass Division: 40 % and above ( 8 semester’s average)
Attendance Requirement:
Students are required to attend regularly all theory and practical classes and should maintain 80 percent attendance in each course separately.
Final Examination:
Institute of science and technology, Tribhuvan University, will conduct the final examination at the end of each semester. 80% weightage will be given to the final examination for theory course and 60% will be given for the course having both theory and practical.
Course Structure:
Semester I (1st Sem)
| Course Code | Course Title | Credit Hour | Full Marks |
|---|---|---|---|
| CSC109 | Introduction to Information Technology | 3 | 100 |
| CSC110 | C Programming | 3 | 100 |
| CSC111 | Digital Logic | 3 | 100 |
| CSC112 | Mathematics I | 3 | 100 |
| PHY 113 | Physics | 3 | 100 |
| TOTAL | 15 | 500 |
Semester II (2nd Sem)
| Course Code | Course Title | Credit Hour | Full Marks |
|---|---|---|---|
| CSC160 | Discrete Structure | 3 | 100 |
| CSC161 | Object Oriented Programming | 3 | 100 |
| CSC162 | Microprocessor | 3 | 100 |
| CSC163 | Mathematics II | 3 | 100 |
| STA 164 | Statistics I | 3 | 100 |
| TOTAL | 15 | 500 |
Semester III (3rd Sem)
| Course Code | Course Title | Credit Hour | Full Marks |
|---|---|---|---|
| CSC206 | Data Structure and Algorithms | 3 | 100 |
| CSC207 | Numerical Method | 3 | 100 |
| CSC208 | Computer Architecture | 3 | 100 |
| CSC209 | Computer Graphics | 3 | 100 |
| STA 210 | Statistics II | 3 | 100 |
| TOTAL | 15 | 500 |
Semester IV (4th Sem)
| Course Code | Course Title | Credit Hour | Full Marks |
|---|---|---|---|
| CSC257 | Theory of Computation | 3 | 100 |
| CSC258 | Computer Networks |
3 | 100 |
| CSC259 | Operating Systems | 3 | 100 |
| CSC260 | Database Management System | 3 | 100 |
| CSC261 | Artificial Intelligence | 3 | 100 |
| TOTAL | 15 | 500 |
Semester V (5th Sem)
| Course Code | Course Title | Credit Hour | Full Marks |
|---|---|---|---|
| CSC314 | Design and Analysis of Algorithms | 3 | 100 |
| CSC315 | System Analysis and Design | 3 | 100 |
| CSC316 | Cryptography | 3 | 100 |
| CSC317 | Simulation and Modeling | 3 | 100 |
| CSC318 | Web Technology | 3 | 100 |
| ELECTIVE 1 | 3 | 100 | |
| TOTAL | 18 | 600 |
List of Electives:
1. Multimedia Computing (CSC319)
2. Wireless Networking (CSC320)
3. Image Processing (CSC321)
4. Knowledge Management (CSC322)
5. Society and Ethics in Information Technology (CSC323)
6. Microprocessor-Based Design (CSC324)
For details of 6th, 7th and 8th semester: DOWNLOAD COURSE OF DETAIL
