BASIC was originally created in 1963 as a teaching language to simplify programming. It has influenced computer science education and raised the need for coding knowledge. R is a free statistical programming language used for data analysis, modeling, and visualization. It includes many statistical and machine learning methods. UNIX was developed in the late 1960s and became widely used, while Linux is an open-source OS inspired by UNIX. Both operate using commands in a terminal rather than a graphical user interface.
This document provides an introduction to R, an open-source programming language for statistical analysis and graphics. R was created in the 1990s at the University of Auckland and is now developed by a core team. It allows users to analyze data, create visualizations, and perform statistical modeling. The document outlines how to set up the R environment locally on Windows and Linux systems and describes key R features such as its programming language, data types, graphical capabilities, and useful third-party resources.
The R language is a project designed to create a free, open source language which can be used as a replacement for the S-PLUS language, originally developed as the S language at AT&T Bell Labs, and currently marketed by Insightful Corporation of Seattle, Washington. R is an open source implementation of S, and differs from S-plus largely in its command-line only format.
Topics Covered:
1.Introduction to R
2.Installing R
3.Why Learn R
4.The R Console
5.Basic Arithmetic and Objects
6.Program Example
7.Programming with Big Data in R
8.Big Data Strategies in R
9.Applications of R Programming
10.Companies Using R
11.What R is not so good at
12.Conclusion
Basic tutorial for R programming. this video contains lot of information about r programming like
agenda
history
SOFTWARE PARADIGM
R interface
advantages of r
drawbacks of r
This document provides an introduction to R, including what R is, how to install and use it, common mistakes, and data structures. It discusses that R was created by Ross Ihaka and Robert Gentleman and contains over 10,000 user-developed packages on topics like statistics, graphics, and data analysis. It also provides instructions on installing R from its homepage or a Italian download site, using the R console and R Studio interfaces, the workspace environment, and saving workspaces to preserve data between sessions.
R is a programming language and software environment for statistical analysis and graphics. It is widely used among statisticians and data scientists. R was created by Ross Ihaka and Robert Gentleman in the early 1990s and is currently developed by the R Core Team. Key features of R include its use as a programming language, effective data handling and storage, graphical display capabilities, and large collection of statistical and machine learning packages. R is open source, has a large user community, and is often used for statistical analysis, data mining, and creating statistical graphics.
R originated in the 1970s at Bell Labs and has since evolved significantly. It is an open-source programming language used widely for statistical analysis and graphics. While powerful, R has some drawbacks like poor performance for large datasets and a steep learning curve. However, its key advantages including being free, having a large community of users, and extensive libraries have made it a popular tool, especially for academic research.
It is one of the Best Presentation on the topic "R Programming" having interesting Slides consisting of Amazing Images & Very Useful Information. It also have Transitions & Animation which makes the Presentation more Interesting & Attractive.
Created By - Abhishek Pratap Singh (Aps)
This document provides an introduction to R, an open-source programming language for statistical analysis and graphics. R was created in the 1990s at the University of Auckland and is now developed by a core team. It allows users to analyze data, create visualizations, and perform statistical modeling. The document outlines how to set up the R environment locally on Windows and Linux systems and describes key R features such as its programming language, data types, graphical capabilities, and useful third-party resources.
The R language is a project designed to create a free, open source language which can be used as a replacement for the S-PLUS language, originally developed as the S language at AT&T Bell Labs, and currently marketed by Insightful Corporation of Seattle, Washington. R is an open source implementation of S, and differs from S-plus largely in its command-line only format.
Topics Covered:
1.Introduction to R
2.Installing R
3.Why Learn R
4.The R Console
5.Basic Arithmetic and Objects
6.Program Example
7.Programming with Big Data in R
8.Big Data Strategies in R
9.Applications of R Programming
10.Companies Using R
11.What R is not so good at
12.Conclusion
Basic tutorial for R programming. this video contains lot of information about r programming like
agenda
history
SOFTWARE PARADIGM
R interface
advantages of r
drawbacks of r
This document provides an introduction to R, including what R is, how to install and use it, common mistakes, and data structures. It discusses that R was created by Ross Ihaka and Robert Gentleman and contains over 10,000 user-developed packages on topics like statistics, graphics, and data analysis. It also provides instructions on installing R from its homepage or a Italian download site, using the R console and R Studio interfaces, the workspace environment, and saving workspaces to preserve data between sessions.
R is a programming language and software environment for statistical analysis and graphics. It is widely used among statisticians and data scientists. R was created by Ross Ihaka and Robert Gentleman in the early 1990s and is currently developed by the R Core Team. Key features of R include its use as a programming language, effective data handling and storage, graphical display capabilities, and large collection of statistical and machine learning packages. R is open source, has a large user community, and is often used for statistical analysis, data mining, and creating statistical graphics.
R originated in the 1970s at Bell Labs and has since evolved significantly. It is an open-source programming language used widely for statistical analysis and graphics. While powerful, R has some drawbacks like poor performance for large datasets and a steep learning curve. However, its key advantages including being free, having a large community of users, and extensive libraries have made it a popular tool, especially for academic research.
It is one of the Best Presentation on the topic "R Programming" having interesting Slides consisting of Amazing Images & Very Useful Information. It also have Transitions & Animation which makes the Presentation more Interesting & Attractive.
Created By - Abhishek Pratap Singh (Aps)
This document provides an overview of R programming. It discusses the history and introduction of R, how to install R and R packages, key features of R including data handling and graphics, advantages such as being free and open source, and disadvantages such as average memory performance. It also outlines some real-world applications of R programming and predicts its continued importance in fields like data science, finance, and analytics.
A short tutorial on R, basically for a starter who wants to do data mining especially text data mining.
Related codes and data will be found at the following lnik: http://textanalytics.in/wm/R%20tutorial%20(DATA2014).zip
R is a programming language and software environment for statistical analysis, graphics representation and reporting. Are You Interested to Learning R Programming in Best Institute Join Besant Technologies in Bangalore.
R is a programming language and software environment for statistical analysis, graphics, and reporting. It was created at the University of Auckland for statistical analysis and graphics. R provides tools for data analysis including statistical functions, data handling, graphical display, and importing/exporting data from other programs like Excel. Some key data types in R are vectors, lists, matrices, and data frames. R can also perform regression analysis and create graphical outputs like pie charts, bar plots, and histograms.
R is a programming language and environment for statistical analysis and graphics. It provides tools for data analysis, visualization, and machine learning. Some key features include statistical functions, graphics, probability distributions, data analysis tools, and the ability to access over 10,000 add-on packages. R can be used across platforms like Windows, Linux, and macOS. It is widely used for complex data analysis in data science and research.
R is a programming language developed as an alternative for S at AT&T Bell Laboratories. It excels at statistical computation and graphic visualization. R is free, open source, and available across platforms. It has over 3,000 packages on CRAN that extend its functionality. R has a steep learning curve and working with large datasets is limited by RAM size. Major companies use R in business.
This short text will get you up to speed in no time on creating visualizations using R's ggplot2 package. It was developed as part of a training to those who had no prior experience in R and had limited knowledge on general programming concepts. It's a must have initial guide for those exploring the field of Data Science
This document provides an introduction to R, including what R is, how it compares to other statistical software packages, its advantages and disadvantages, how to install R, and options for R editors and graphical user interfaces (GUIs). It discusses R as a language for statistical computing and graphics, compares it to packages like SAS, Stata, and SPSS in terms of cost, usage mode, and prevalence. It outlines some of R's advantages like being free and open-source software with an active user community contributing packages, and some disadvantages like the learning curve and lack of a standard GUI.
Very brief introduction to R software that I have presented at UNISZA. No R codes and No Statistical Contents. Basically for those who just heard about R software for the first time
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
This document summarizes a seminar presentation on the R programming language. It begins with an introduction to R's history and features. Key points covered include that R is a functional programming language developed for statistical analysis. It has a large number of built-in statistical functions and packages. The document then discusses R packages, graphical user interfaces, getting started with basic objects and functions, and programming features like flow control and functions. Examples are provided. Reasons for using R include its matrix calculation, data visualization and statistical analysis capabilities. Comparisons are made to other languages like Python and Java. The document concludes that R has become a high-quality open-source software for statistical computing and graphics.
R is a programming language for statistical analysis and graphics. It is an open-source language developed by statisticians to allow for easy statistical analysis and visualization of data. The document provides an overview of R, discussing its origins, functionality, uses in data science, and popular packages and IDEs used with R. Examples are given of basic R syntax for vectors, matrices, data frames, plotting, and applying functions to data.
Workshop presentation hands on r programmingNimrita Koul
This document provides an overview of the R programming language. It discusses that R is an environment for statistical computing and graphics. It includes conditionals, loops, user defined functions, and input/output facilities. The document describes how to download and install R and RStudio. It also covers key R features such as objects, classes, vectors, matrices, lists, functions, packages, graphics, and input/output.
This document provides an introduction to using R for data science and analytics. It discusses what R is, how to install R and RStudio, statistical software options, and how R can be used with other tools like Tableau, Qlik, and SAS. Examples are given of how R is used in government, telecom, insurance, finance, pharma, and by companies like ANZ bank, Bank of America, Facebook, and the Consumer Financial Protection Bureau. Key statistical concepts are also refreshed.
R programming language: conceptual overviewMaxim Litvak
This is an advanced overview of the programming language R showing the concepts and paradigms used there.
Target audience: R programmers who want to see the big picture and software engineers who have a broad experience in other technologies and want to grasp how R is designed.
This document discusses the evolution of programming languages and the emergence of object-oriented programming. It covers the major generations of programming languages from the 1950s to today, highlighting important developments such as the introduction of subroutines, block structure, data types, classes, and object-oriented frameworks. The document also examines the changing topologies and physical building blocks of programs as languages incorporated new structuring mechanisms like modules, objects, and classes. Finally, it defines the key concepts and foundations of object-oriented programming and the object model.
This document provides an overview of R programming. It discusses the history and introduction of R, how to install R and R packages, key features of R including data handling and graphics, advantages such as being free and open source, and disadvantages such as average memory performance. It also outlines some real-world applications of R programming and predicts its continued importance in fields like data science, finance, and analytics.
A short tutorial on R, basically for a starter who wants to do data mining especially text data mining.
Related codes and data will be found at the following lnik: http://textanalytics.in/wm/R%20tutorial%20(DATA2014).zip
R is a programming language and software environment for statistical analysis, graphics representation and reporting. Are You Interested to Learning R Programming in Best Institute Join Besant Technologies in Bangalore.
R is a programming language and software environment for statistical analysis, graphics, and reporting. It was created at the University of Auckland for statistical analysis and graphics. R provides tools for data analysis including statistical functions, data handling, graphical display, and importing/exporting data from other programs like Excel. Some key data types in R are vectors, lists, matrices, and data frames. R can also perform regression analysis and create graphical outputs like pie charts, bar plots, and histograms.
R is a programming language and environment for statistical analysis and graphics. It provides tools for data analysis, visualization, and machine learning. Some key features include statistical functions, graphics, probability distributions, data analysis tools, and the ability to access over 10,000 add-on packages. R can be used across platforms like Windows, Linux, and macOS. It is widely used for complex data analysis in data science and research.
R is a programming language developed as an alternative for S at AT&T Bell Laboratories. It excels at statistical computation and graphic visualization. R is free, open source, and available across platforms. It has over 3,000 packages on CRAN that extend its functionality. R has a steep learning curve and working with large datasets is limited by RAM size. Major companies use R in business.
This short text will get you up to speed in no time on creating visualizations using R's ggplot2 package. It was developed as part of a training to those who had no prior experience in R and had limited knowledge on general programming concepts. It's a must have initial guide for those exploring the field of Data Science
This document provides an introduction to R, including what R is, how it compares to other statistical software packages, its advantages and disadvantages, how to install R, and options for R editors and graphical user interfaces (GUIs). It discusses R as a language for statistical computing and graphics, compares it to packages like SAS, Stata, and SPSS in terms of cost, usage mode, and prevalence. It outlines some of R's advantages like being free and open-source software with an active user community contributing packages, and some disadvantages like the learning curve and lack of a standard GUI.
Very brief introduction to R software that I have presented at UNISZA. No R codes and No Statistical Contents. Basically for those who just heard about R software for the first time
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
This document summarizes a seminar presentation on the R programming language. It begins with an introduction to R's history and features. Key points covered include that R is a functional programming language developed for statistical analysis. It has a large number of built-in statistical functions and packages. The document then discusses R packages, graphical user interfaces, getting started with basic objects and functions, and programming features like flow control and functions. Examples are provided. Reasons for using R include its matrix calculation, data visualization and statistical analysis capabilities. Comparisons are made to other languages like Python and Java. The document concludes that R has become a high-quality open-source software for statistical computing and graphics.
R is a programming language for statistical analysis and graphics. It is an open-source language developed by statisticians to allow for easy statistical analysis and visualization of data. The document provides an overview of R, discussing its origins, functionality, uses in data science, and popular packages and IDEs used with R. Examples are given of basic R syntax for vectors, matrices, data frames, plotting, and applying functions to data.
Workshop presentation hands on r programmingNimrita Koul
This document provides an overview of the R programming language. It discusses that R is an environment for statistical computing and graphics. It includes conditionals, loops, user defined functions, and input/output facilities. The document describes how to download and install R and RStudio. It also covers key R features such as objects, classes, vectors, matrices, lists, functions, packages, graphics, and input/output.
This document provides an introduction to using R for data science and analytics. It discusses what R is, how to install R and RStudio, statistical software options, and how R can be used with other tools like Tableau, Qlik, and SAS. Examples are given of how R is used in government, telecom, insurance, finance, pharma, and by companies like ANZ bank, Bank of America, Facebook, and the Consumer Financial Protection Bureau. Key statistical concepts are also refreshed.
R programming language: conceptual overviewMaxim Litvak
This is an advanced overview of the programming language R showing the concepts and paradigms used there.
Target audience: R programmers who want to see the big picture and software engineers who have a broad experience in other technologies and want to grasp how R is designed.
This document discusses the evolution of programming languages and the emergence of object-oriented programming. It covers the major generations of programming languages from the 1950s to today, highlighting important developments such as the introduction of subroutines, block structure, data types, classes, and object-oriented frameworks. The document also examines the changing topologies and physical building blocks of programs as languages incorporated new structuring mechanisms like modules, objects, and classes. Finally, it defines the key concepts and foundations of object-oriented programming and the object model.
First generation computers used machine language and could only solve one problem at a time. Input was via punched cards and paper tape, and output was printed. Examples included the UNIVAC and ENIAC. Second generation computers introduced symbolic languages like assembly languages and early versions of COBOL and FORTRAN. Storage moved from drums to magnetic cores. Third generation computers saw the introduction of operating systems to provide a software platform for applications. Fourth generation computers featured graphical user interfaces with icons, windows, menus, pointers and desktop workspaces to make programs easier to use. Fifth generation computers involve artificial intelligence techniques like expert systems, neural networks and robotics.
The document discusses different types of computer programming languages including low-level languages like machine language and assembly language that are closer to hardware, and high-level languages like C++, Java, and Python that are easier for humans to read and write. It also covers basic programming concepts like variables, strings, statements, operators, and operands.
This document provides an overview of several programming languages including machine language, assembly language, high-level languages like FORTRAN, ALGOL, LISP, COBOL, BASIC, Pascal, ADA, SQL, Smalltalk, C, C++, C#, Python and Java. It describes the purpose and key features of each language such as their level of abstraction, syntax, data structures, and application.
The document discusses the BASIC programming language. It was one of the earliest high-level programming languages developed in the 1960s. It was designed to be simple and easy to learn, making it popular among non-experts. The language includes English keywords like INPUT and PRINT to make it accessible to those without programming experience. It has been widely used in business applications and helped launch the personal computer revolution.
The document discusses various computer programming languages including:
- Low-level languages like machine language and assembly language that are closer to hardware.
- High-level languages like C++, Java, and Python that are easier for humans to read and write but require translation.
- Early languages like FORTRAN, COBOL, BASIC that were developed for scientific, business, and educational use respectively.
Free/Open Source Software for Science & EngineeringKinshuk Sunil
This document discusses free and open source software applications that are useful for science and engineering. It provides information on the GNU operating system, popular GNU/Linux distributions, benefits of open source software including increased quality and accelerated development. It then describes several key free software applications and libraries useful for scientific computation, data analysis and visualization, including Python, NumPy/SciPy, R, LaTeX, GSL, Octave, OpenDX, and SciDAVis.
History of Computer Programming Languages.pptxAliAbbas906043
The document discusses the history and development of programming languages from the first algorithm created by Ada Lovelace in 1843 to modern languages. It outlines several important events and milestones, including the first assembly language in 1949, FORTRAN in 1957, COBOL and ALGOL in 1959, BASIC and PASCAL in the 1960s-1970s, C and SQL in 1972, Ada in the 1980s, Java and JavaScript in 1995, and Swift in 2014. The document concludes that programming languages have come a long way from early machine codes to today's high-level, readable languages and will likely continue advancing in the future.
Innovation in CS/IT via Open Source SoftwareMaurice Dawson
As costs around the world continue to rise for education, institutions must become innovative in the ways they teach and grow students. To do this effectively professors and administrative staff should push toward the utilization of Open Software (OSS) and virtual tools to enhance or supplement currently available tools. In developing countries OSS applications would allow students the ability to learn critical technological skills for success at small fraction of the cost. OSS also provides faculty members the ability to dissect source code and prepare students for low level software development. It is critical that all institutions look at alternatives in providing training and delivering educational material regardless of limitations going forward as the world continues to be more global due to the increased use of technologies everywhere. Doing this could provide a means of shortening the education gap in many countries. Through reviewing the available technology, possible implementations of these technologies, and the application of these items in graduate coursework could provide a starting point in integrating these tools into academia. When administrators or faculty debate the possibilities of OSS, gaming, and simulation tools this applied research provides a guide for changing the ability to develop students that will be competitive on a global level.
The document provides information about high level and low level programming languages. It defines low level languages as assembly language and machine language, which computers can directly understand as binary code. High level languages are closer to human language and include C++, SQL, Java, C#, FORTRAN, COBOL, C, JavaScript, PHP, and HTML. Each high level language is then briefly described in terms of its history, purpose, and basic syntax structure.
Linux is a free and open-source operating system based on the Linux kernel, which was created by Linus Torvalds in 1991. It is widely used on servers, desktops, and embedded devices. Major Linux distributions combine the Linux kernel with tools and libraries from the GNU operating system and various application software into a format that is easy to install and use. Linux has gained popularity for its security, reliability, and low cost as well as avoiding vendor lock-in.
Linux is the best-known and most-used open source operating system. As an operating system, Linux is software that sits underneath all of the other software on a computer, receiving requests from those programs and relaying these requests to the computer's hardware.
Chapter 8 - nsa Introduction to Linux.pptgadisaAdamu
Linux is an open-source operating system kernel created by Linus Torvalds. It can run on a variety of systems including servers, desktops, embedded devices, and more. Since its initial release in 1991, the Linux kernel has grown significantly with contributions from thousands of programmers. It is free to use, modify, and distribute, driving its widespread adoption for servers, embedded systems, and as an alternative to other proprietary operating systems.
Pseudo code allows programmers to define logic as plain steps to solve a problem before implementing the actual program. This helps programmers understand complex problems, define all possible solution steps completely, and get verification without output. Using pseudo code can save significant time and cost during implementation. Pseudo code is defined in natural language, making it easily understood by other programmers.
A computer is an electronic device that can store, retrieve, and process data based on a set of instructions. It works by accepting digital data as input, processing it based on programmed instructions, and generating output. Modern computers come in various sizes but all utilize hardware components like processors, memory, and storage as well as software programs and operating systems to perform tasks. While early computers were enormous, modern technology has made computers dramatically smaller and more powerful.
The document provides an overview of computers, operating systems, programming languages, the internet, and Microsoft .NET. It discusses the evolution of computers from early mainframes to personal computers and networks. It covers the development of operating systems, languages like C, C++, Java and C#, and web technologies like HTML, XML and the World Wide Web. It also summarizes Microsoft's .NET framework and how it allows applications to be developed in any .NET compatible language and run across platforms using the Common Language Runtime.
System software directly interacts with hardware and manages devices to perform background tasks for application software. It includes operating systems, compilers, linkers and loaders. Application software is developed for specific tasks like word processing, spreadsheets, web browsing. Common types of application software include office suites, web browsers, games. Programming languages have evolved from low-level machine languages to modern high-level languages that are closer to human languages like C++, Java, Python. The best language depends on the specific task.
The document provides an overview of computers, programming languages, and Microsoft .NET. It discusses the history and components of computers. It then covers the evolution of operating systems, languages like Visual Basic, C++, Java and C#, and software trends like object technology. It discusses the internet and World Wide Web. It introduces .NET and key concepts like the .NET Framework and Common Language Runtime.
A programming language is a formal language used to describe computations. It consists of syntax, semantics, and tools like compilers or interpreters. Programming languages support different paradigms like procedural, functional, object-oriented, and logic-based approaches. Variables are named locations in memory that hold values and have properties like name, scope, type, and values.
Computer Assisted Language Learning (CALL) uses computer technology to aid in language teaching and learning. It provides interactive instruction that allows learners to progress at their own pace. CALL has evolved from behaviorist approaches focused on drills to more communicative and integrative approaches. Modern CALL incorporates multimedia, authentic tasks, and opportunities for online communication and discourse. Computer Assisted Language Testing (CALT) uses computers to deliver language assessments in a more consistent, authentic, and individualized manner while reducing administrative burdens.
This document discusses Lexical Functional Grammar (LFG) and Generalized Phrase Structure Grammar (GPSG). LFG was developed in the 1970s and emphasizes analyzing phenomena in lexical and functional terms. It uses two levels of structure: c-structure, which is a tree structure, and f-structure, which captures grammatical functions. GPSG was developed in 1985 and is confined to context-free phrase structure rules. It uses immediate dominance and linear precedence rules.
Group discussion is used to evaluate candidates for admissions and jobs by having them participate in a group discussion. It allows evaluators to assess candidates' linguistic abilities, ideas, and how they work in a group. Group discussions involve 6-12 participants discussing a topic for 15-30 minutes while being monitored by panelists and moderators. Candidates are evaluated on various communication and soft skills like subject knowledge, originality, presentation, analytical ability, and leadership. The document provides guidance on how to effectively participate in a group discussion by remembering to think before speaking, backing up points, and avoiding common mistakes.
Word categories can be open or closed classes. Open classes include nouns, verbs, adjectives, and adverbs where new words can be freely created. Closed classes include prepositions, determiners, auxiliaries and other grammatical words. Words have internal structure and can contain multiple morphemes like prefixes and suffixes. The meaning of derived words is often compositional, built from the meaning of its constituent morphemes. Context provides cues to determine a word's syntactic category based on what other words or affixes it co-occurs with.
This document discusses rewrite systems and methods for parsing sentences. It provides examples of parse trees constructed using bottom-up and top-down methods. Bottom-up trees are often easier for beginners, starting with individual words and building up the tree structure. Top-down trees are quicker for experts, who start with the sentence node and fill in constituent parts working downwards. Various grammatical components are also defined, such as noun phrases, verb phrases, and prepositional phrases.
Phrase structure grammar models the internal structure of sentences in a hierarchical organization. It represents sentences as consisting of phrases, which are made up of words, which are made up of morphemes and phonemes. Phrase structure grammars use rewrite rules to break down syntactic structures into their constituent parts in a step-by-step manner. Deep structure represents the underlying meaning of a sentence, while surface structure is the actual form used. Transformational rules derive surface structure from deep structure.
The document discusses verb movement in syntax. It explains that in English, auxiliary verbs like "have" and "be" can appear in the tense slot of a sentence unless that slot is filled by another verb, whereas main verbs like "eat" appear in the verb phrase. Some languages exhibit verb movement where all verbs, not just auxiliaries, appear in the tense slot. This movement of verbs can be analyzed as the verb adjoining to the tense feature in a higher clause.
The document discusses various aspects of text analysis, editing, and processing. It defines text analysis as the computational analysis of unstructured text to extract machine-readable information. It also discusses text editing, word processing, indenting, and bibliography making. The key purpose of text analysis is to structure unstructured text data for further analysis and management. Formatting, styles, and macros are described as important functions of word processors for text manipulation and formatting.
Natural Language Processing (NLP) involves using computational techniques to analyze and understand human languages. Key techniques in NLP include sentiment analysis to classify emotions in text, text classification to categorize text into predefined tags or categories, and tokenization which breaks text into discrete words and punctuation. NLP is used to teach machines how to read and understand human languages by identifying relationships between words and entities. Other areas of NLP include parts of speech tagging, constituent structure analysis, and analysis of pronunciation, morphology, syntax, semantics, and pragmatics.
The document discusses parsers and parsing techniques. A parser breaks input data like program instructions into smaller elements by performing syntactic analysis. It builds a parse tree or abstract syntax tree from the input tokens. Parsers perform context-free syntax analysis, guide context-sensitive analysis, and construct an intermediate representation while producing error messages. Parsing adds information about a sentence's structure, constituents, and how words relate and function within a sentence. Parsing techniques include top-down and bottom-up approaches, and parsers can be improved through iterative correction on parsed text.
This document discusses various applications of computers in linguistics research activities, including file processing, databases, data processing, electronic data processing, text handling, dialect surveys, testing linguistic statements, word processors, data base management systems, spreadsheets, hypermedia, and corpus linguistics. Computers allow for speed, convenience, and accuracy when handling large amounts of linguistic data and text. They can be used to extract vocabulary, count words, create dictionaries and glossaries, and search text.
Translation technologies have evolved significantly with each industrial revolution due to advances in communication and computing. Industry 5.0 will see greater man-machine integration through collaborative robots and voice command interfaces. Neural machine translation powered by deep learning is becoming more accurate but still requires human oversight. The growth of technologies like the Internet of Things, cloud computing and artificial intelligence will drive new applications of translation in industry and global communication.
This document discusses various levels and types of stylistics including phonetic, morphological, lexical, and syntactic stylistics. It provides examples and definitions of key concepts in stylistic analysis such as rhyme, rhythm, alliteration, metaphor, metonymy, synecdoche, irony, epithet, hyperbole, oxymoron, simile, euphemism, pun, ellipsis, aposiopesis, nominative sentences, asyndeton, repetition, parallel constructions, inversion, and rhetorical questions. The document examines how these devices are used and their communicative functions in emphasizing aspects of speech and making language more expressive.
The document provides guidance on preparing and delivering effective presentations. It discusses objectives such as developing communication skills, improving research and delivery techniques, and increasing confidence. It recommends choosing a topic and key messages, creating an outline and visual aids, rehearsing, and anticipating questions. Tips are provided on posture, movement, facial expressions, voice, eye contact, and answering questions to help address nervousness and engage the audience. The overall aim is to organize content clearly and relax while delivering presentations with impact.
The document discusses strategic thinking and related concepts. It defines strategic thinking as a process that involves considering key issues in a comprehensive way and maintaining a long-term view. The document outlines several components of strategic thinking, including tools for analysis like SWOT, having a clear strategic purpose and vision, identifying values and key goals, and developing action plans. It also provides tips for strategic thinking such as prioritizing tasks, overcoming bias, improving listening and questioning skills, and understanding consequences.
Building rapport involves matching various behaviors of the other person, including their sensory modality, physiology, voice, breathing patterns, how they process information, and common experiences. Specifically, it recommends mirroring the other person's eye contact, body language, tone, speed of speaking, breathing, how much information is presented at once, and finding common interests or backgrounds to discuss. The goal is to unconsciously mimic the other person's behaviors to show that their message is understood and to create a connection.
This document classifies and describes different types of computers. It discusses analog and digital computers and how they are classified based on construction and configuration. It provides details on various types of computers like microcomputers, laptops, palmtops, mainframes, miniframes, and supercomputers. It describes their key components, uses in different applications, and examples of popular models.
The document discusses the key principles of language assessment: practicality, reliability, validity, authenticity, and washback. It defines each principle and provides examples. Practicality means a test is cost-effective, time-efficient and easy to administer. Reliability refers to a test producing consistent results. Validity concerns a test accurately measuring what it claims to measure. Authenticity refers to how well a test simulates real-world language tasks. Washback concerns a test's influence on teaching and learning. A test has positive washback if it encourages effective instruction and learning.
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2. BASIC
With a long history in the field of computer science, BASIC
(Beginner’s All-Purpose Symbolic Instruction Code)
programming has been used to simplify communication
between the programer and the computer. It was developed
in 1963 as a teaching language at Dartmouth by John G.
Kemeny and Thomas E. Kurtz and has since been widely
imitated and altered.
3. Problem solving
BASIC programming was originally designed as a programming
language used to teach people how to program, which makes it the
perfect starting point for computer science education. Once the
fundamentals mastered, expanding into Visual Basic
programming (developed by Microsoft), one of the most widely used
programming systems in the history of computer software can be
made easy.
Computational thinking is the ability to logically communicate one’s
thoughts in a structured manner. This type of thought process trains
problem solving skills that are replicated later in life, much like the
memory of a computer.
4. Computer literacy
Computer literacy would be an essential skill in the
upcoming years, and designed the language to be as
accessible and understandable as possible. BASIC has
dramatically influenced the computer science field, raising
the need for coding knowledge in pretty much every area of
our modern lives.
Programming is an international language, with various
computer codes being implemented around the world in
relation to a variety of applications and companies.
5. BASIC programming
BASIC originally used numbers at the beginning of each instruction
(or line) to tell the computer what order to process the instructions.
Lines would be numbered as 10, 20, 30, etc., which would allow
additional instructions to be placed between commands later on if
needed. "GOTO" statements enabled programs to loop back to
earlier instructions during execution. For example, line 230 of a
BASIC program may have an "if" clause that tells the computer to
jump back to line 50 if a variable is less than 10. This instruction
might look something like this:
230 IF (N < 10) THEN GOTO 50
6. R language
R is a system for statistical analyses and graphics created by Ross
Ihaka and Robert Gentleman. R is both a software and a language
considered as a dialect of the S language created by the AT&T Bell
Laboratory.
R is freely distributed under the terms of the GNU General Public
Licence, its development and distribution are carried out by several
statisticians known as the R Development Core Teams.
7. R
R possesses an extensive catalog of statistical and graphical methods.
It includes machine learning algorithms, linear regression, time
series, statistical inference to name a few. Most of the R libraries are
written in R, but for heavy computational tasks, C, C++ and Fortran
codes are preferred.
R is not only entrusted by academic, but many large companies also
use R programming language, including Uber, Google, Airbnb,
Facebook and so on.
8. What is R
It is a free and open-source programming language issued under GNU (General Public License).
It has cross-platform interoperability which means that it has distributions running on Windows,
Linux, and Mac. R code can easily be ported from one platform to another.
It uses an interpreter instead of a compiler, which makes the development of code easier.
It effectively associates different databases, and it does well in bringing in information from
Microsoft Excel, as well as, Microsoft Access, MySQL, SQLite, Oracle, etc.
It is a flexible language that bridges the gap between Software Development and Data Analysis.
It provides a wide variety of packages with the diversity of codes, functions, and features
tailored for data analysis, statistical modeling, visualization, Machine Learning, and importing
and manipulating data.
It integrates various powerful tools to communicate reports in different forms like CSV, XML,
HTML, and pdf, and also through interactive websites, with the help of R packages.
9. Data Analysis in R
Import: The first step is to import data into the R environment. It means
that the users take the data stored in files, databases, HTML tables, etc.,
and load it into an R data frame to perform data analysis on it.
Transform: In this step, first, user need to make the data tidy by making
each column a variable, and each row an observation. Once data is made
tidy, and it can be narrow down on it to find observations of users
interest, create new variables that are functions of existing variables, and
find summary statistics of the observations.
10. Understanding R program
Program: R is a clear and accessible programming tool
Transform: R is made up of a collection of libraries designed
specifically for data science
Discover: Investigate the data, refine one’s hypothesis and analyze
them
Model: R provides a wide array of tools to capture the right model
for users data
Communication: Integrate codes, graphs, and outputs to a report
with R Markdown or build Shiny apps to share with the world
11. R combinations
R is available in several forms: the sources (written mainly in C
and some routines in Fortran), essentially for Unix and Linux
machines, or some pre-compiled binaries for Windows, Linux, and
Macintosh.
The R language allows the user, for instance, to program loops to
successively analyse several data sets. It is also possible to
combine in a single program different statistical functions to
perform more complex analyses.
R users may benefit from a large number of programs written for S
and available on the internet , most of these programs can be used
directly with R.
12. R and other programs
R is an interpreted language, not a compiled one, meaning that all
commands typed on the key- board are directly executed without
requiring to build a complete program like in most computer
languages (C, Fortran, Pascal, . . . ).
13. R and others
Data science is shaping the way companies run their businesses.
Without a doubt, staying away from Artificial Intelligence and
Machine will lead the company to fail. They are plenty of tools
available in the market to perform data analysis.
14. R Studio
RStudio is a free and open-source IDE (integrated development
environment) for programming in R. It makes it easier to write scripts,
interact with objects in the R environment, access files, and make graphics
more accessible to a casual user. It is available in two versions:
RStudio Desktop edition, where a program runs locally as a regular desktop
application.
RStudio Server edition, which allows a user to access RStudio using a web
browser while it runs on a remote server.
Prepackaged distributions of RStudio Desktop are available
for Windows, macOS, and Linux.
17. UNIX
The UNIX OS was born in the late 1960s. AT&T Bell Labs released an
operating system called Unix written in C, which allows quicker
modification, acceptance, and portability.
It began as a one-man project under the leadership of Ken
Thompson of Bell Labs. It went on to become most widely used
operating systems. Unix is a proprietary operating system.
The Unix OS works on CLI (Command Line Interface), but recently,
there have been developments for GUI on Unix systems. Unix is an
OS which is popular in companies, universities big enterprises, etc.
18. Basic Commands
Commands, not mouse click
The Unix Shell differs from most other operating systems in that it has no graphical user interface. Instead, it has
what we call a command line, i.e. the cursor is waiting for you to enter a command. Right after you have logged
into your home directory you are in your home directory. To the left of the cursor there is a short text which tells
you which directory you are in right now.
Commands must be written in a certain way. A unix command has two mandatory parts: The command must
start with the name of a program, and it is completed when you press the ENTER key.
Simple command:
◦ Write date and press ENTER.
◦ In response, you should get the current date and time.
Another command:
◦ Write cal and press ENTER.
◦ The answer should be a small calendar.
19. Features of Unix OS
Multi-user, multitasking operating system
It can be used as the master control program in workstations and
servers.
Hundreds of commercial applications are available
In its heydays, UNIX was rapidly adopted and became the standard
OS in universities.
20. Limitations of Unix
The unfriendly, terse, inconsistent, and non-mnemonic user interface
Unix OS is designed for a slow computer system, so you can't expect
fast performance.
Shell interface can be treacherous because typing mistake can
destroy files.
Versions on various machines are slightly different, so it lacks
consistency.
Unix does not provide any assured hardware interrupt response
time, so it does not support real time response time systems.
21. Linux
Linux is an operating system built by Linus Torvalds at the University of Helsinki
in 1991. The name "Linux" comes from the Linux kernel. It is the software on a
computer which enables applications and the users to access the devices on the
computer to perform some specific function.
The Linux OS relays instructions from an application from the computer's
processor and sends the results back to the application via the Linux OS. It can
be installed on a different type of computers mobile phones, tablets video game
consoles, etc.
The development of Linux is one of the most prominent examples of free and
open source software collaboration. Today many companies and similar
numbers of individuals have released their own version of OS based on the Linux
Kernel.
22. Features of Linux
Support multitasking
Programs consist of one or more processes, and each process have
one or more threads
It can easily co-exists along with other Operating systems.
It can run multiple user programs
Individual accounts are protected because of appropriate
authorization
Linux is a replica of UNIX but does not use its code.
23. Limitations of Linux
There's no standard edition of Linux
Linux has patchier support for drivers which may result in
misfunctioning of the entire system.
Linux is, for new users at least, not as easy to use as Windows.
Many of the programs we are using for Windows will only run on
Linux only with the help of a complicated emulator. For example.
Microsoft Office.
Linux is best suitable for a corporate user. It's much harder to
introduce in a home setting.