This document provides an overview of the C programming language. It discusses the history of C and how it was created by Dennis Ritchie in 1972. It also outlines the typical steps involved in a program development process, including requirement specification, analysis, design, implementation, verification and testing, and maintenance. The document explains that C is commonly used for systems software, compilers, databases, operating systems, and device drivers due to its efficiency and performance. It provides examples of where C is used and the benefits it provides.
This document discusses how C# can be used in a functional programming style by leveraging features like Action<> and Func<> for representing functions, closures, and LINQ for querying data in a functional way. It provides examples of using functional techniques in C# for tasks like finding all trees in a forest and implementing an interview coding challenge. The document concludes by mentioning additional functional concepts like partial function application, currying, and asynchronous and parallel programming.
The document discusses the concept of complete programming which involves creating documentation, prototypes, code, and tests in a scientific approach and without duplication. It emphasizes writing documentation to plan stages and communicate ideas, creating functional prototypes to test use cases, writing dummy code in feature branches, and integrating tests to define problems, edge cases, and ensure quality. The full text can be found at the provided URL.
C is the base to all kind of Programming Languages. Simply the enhancement of C language is C++. C++ is a general-purpose object-oriented programming (OOPS) language and Java is a general purpose, high-level programming language. Here I Provide a complete difference between C, C++ and JAVA in a simplified manner.
The C programming language was developed at Bell Laboratories in 1972 by Dennis Ritchie. It was derived from an earlier language called B and was created for implementing the UNIX operating system. C is a structured programming language commonly used for system applications like operating systems, databases, graphics packages, and networks. It divides programs into functions and uses a top-down approach, with data moving freely between functions.
Reproducible research (and literate programming) in Rliz__is
This document discusses tools and best practices for reproducible research in R. It introduces knitr for combining R code and documentation in R Markdown (Rmd) documents. Key tips include using packrat to manage dependencies, knitr to generate reports from Rmd, and literate programming techniques like documenting code to improve readability and reuse. The document provides an anatomy of an Rmd file and examples of formatting code chunks, citations, tables, and session information. Other reproducibility tools mentioned include Jupyter notebooks, Make, and Docker containers.
This document provides an overview of the C programming language. It discusses the history of C and how it was created by Dennis Ritchie in 1972. It also outlines the typical steps involved in a program development process, including requirement specification, analysis, design, implementation, verification and testing, and maintenance. The document explains that C is commonly used for systems software, compilers, databases, operating systems, and device drivers due to its efficiency and performance. It provides examples of where C is used and the benefits it provides.
This document discusses how C# can be used in a functional programming style by leveraging features like Action<> and Func<> for representing functions, closures, and LINQ for querying data in a functional way. It provides examples of using functional techniques in C# for tasks like finding all trees in a forest and implementing an interview coding challenge. The document concludes by mentioning additional functional concepts like partial function application, currying, and asynchronous and parallel programming.
The document discusses the concept of complete programming which involves creating documentation, prototypes, code, and tests in a scientific approach and without duplication. It emphasizes writing documentation to plan stages and communicate ideas, creating functional prototypes to test use cases, writing dummy code in feature branches, and integrating tests to define problems, edge cases, and ensure quality. The full text can be found at the provided URL.
C is the base to all kind of Programming Languages. Simply the enhancement of C language is C++. C++ is a general-purpose object-oriented programming (OOPS) language and Java is a general purpose, high-level programming language. Here I Provide a complete difference between C, C++ and JAVA in a simplified manner.
The C programming language was developed at Bell Laboratories in 1972 by Dennis Ritchie. It was derived from an earlier language called B and was created for implementing the UNIX operating system. C is a structured programming language commonly used for system applications like operating systems, databases, graphics packages, and networks. It divides programs into functions and uses a top-down approach, with data moving freely between functions.
Reproducible research (and literate programming) in Rliz__is
This document discusses tools and best practices for reproducible research in R. It introduces knitr for combining R code and documentation in R Markdown (Rmd) documents. Key tips include using packrat to manage dependencies, knitr to generate reports from Rmd, and literate programming techniques like documenting code to improve readability and reuse. The document provides an anatomy of an Rmd file and examples of formatting code chunks, citations, tables, and session information. Other reproducibility tools mentioned include Jupyter notebooks, Make, and Docker containers.
There are now a couple of alternative interpreters (or engines) for the R programming language. In this presentation, I will give a gentle introduction to Renjin, which is an open-source project to implement an R interpreter in Java.The introduction should appeal a wide audience, from data scientists to (web) application developers and will cover topis such as "Why build another R interpreter?", "For whom is Renjin?", "What can you do with Renjin?", "How does Renjin compare to GNU R and the other alternative engines like pqR, FastR, and TERR or pseudo-alternatives like Microsoft R and Oracle R Distribution?", "How can I try Renjin?", and more.
This document provides an overview and introduction to R and R Studio. It outlines the objectives of understanding R as a programming language, installing R and R Studio, and an introduction to R as an object-oriented language. Instructions are provided on installing R for Windows and Mac as well as installing R Studio. An overview of the R Studio interface is given along with details on upgrading R to a specific version. The document concludes with information on R as an object-oriented language and creating and manipulating objects.
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.
R is a programming language and software environment for statistical analysis and graphics. It originated from S, a statistical programming language developed in the 1970s. R was first released in 1993 and has since grown in popularity due to its ability to run on Linux, Windows and Mac operating systems. It allows users to contribute additional packages to extend its functionality. Getting help in R can be obtained through manuals, online searches, and mailing lists. R has a command line interface but various graphical user interfaces and integrated development environments are also available. Everything in R is an object that has a class and methods, with common functions to define classes, create objects, and extract object elements.
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.
This document provides an introduction to using R for statistical analysis and data visualization. It discusses installing R and RStudio, the basic layout of RStudio, composing and executing R scripts, performing calculations and importing datasets in R. It also covers linear regression analysis, cleaning data using dplyr and tidyr packages, and includes a comprehensive exercise using a dataset of World Cup results. The goal is to equip readers with basic skills in using R for data analysis after completing the exercises in the document.
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
INT232 __ DATA SCIENCE TOOLBOX _ R PROGRAMMING.pdfVeerpalkhaira
This document provides an instruction plan for a course on Data Science Toolbox: R Programming. It outlines the course code, title, reference books, relevant websites, virtual labs, and course outcomes. The instruction plan spans 8 weeks and includes 14 lectures covering topics such as installing R, introduction to basics of R including vectors and matrices, factors, and data frames. Each lecture lists the related chapters, readings, and software as well as the learning objectives and pedagogical tools to be used such as demonstrations, case studies, and examples.
This document outlines an R crash course to teach the basics of R to beginners in a short period of time. The course will cover installing R software, scripting in R, working with spatial data in R, and linking R with other programs like SAGA GIS. The document discusses what R is, why it is useful for data analysis and popular in the statistics community, and some assumptions about the course participants and format.
R Vs Python – The most trending debate of aspiring Data Scientistsabhishekdf3
Now, it’s the time for a battle of two most demanding programming languages that is R vs Python. We will go deep in understanding the differences between the two languages. And, I assure you that you will not have any confusion left after completing this article i.e. R vs Python – the most trending debate of aspiring data scientists.
Learn more at :- https://data-flair.training/
R programming advantages and disadvantagesPrwaTech
R is a free and open-source programming language and software environment for statistical analysis, graphics, and statistical computing. It is widely used among data scientists for statistical modeling and analysis. Some key advantages of R include its large number of contributed packages, ability to handle complex data, and use for statistical analysis and machine learning. However, it also has some disadvantages like using more memory than other languages and having a steep learning curve.
This document provides an introduction and overview of R programming for statistics. It discusses how to run R sessions and functions, basic math operations and data types in R like vectors, data frames, and matrices. It also covers statistical and graphical features of R, programming features like functions, and gives examples of built-in and user-defined functions.
This document provides a training manual on better graphics in R. It begins with an overview of R and BioConductor and reviews basic R functions. It then covers creating simple and customized graphics, multi-step graphics with legends, and multi-panel layouts. The manual aims to help researchers learn visualization techniques to improve the communication of their data and results.
The C & C++ Advanced Program targets first and second year degree and final year diploma students with a basic knowledge of C programming. The goals are to learn C in-depth, including memory manipulation, files, structures, bitwise operations, and preprocessor directives, and to learn object-oriented C++ concepts like encapsulation, abstraction, inheritance, polymorphism, and STL. The syllabus covers these topics along with practical programs.
The Linux & Perl course also targets first and second year degree and final year diploma students with access to Linux. It teaches Perl scripting for text processing and modules, shell scripts, Linux commands, vi editor, Perl identifiers, scalars, lists, hashes, loops, subrout
Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)
This document discusses R and Julia for data analysis and advanced analytics. It provides an overview of R's history, how it works, performance improvements, and use in production. Julia is introduced as a new high-performance dynamic language with similarities to R but faster performance due to its just-in-time compiler and type information. Examples are given comparing the performance of Julia to other languages. The document recommends Julia for those already using C/Fortran and suggests it will be useful for R users once fully developed.
1. Reproducible research is the ability to reproduce an experiment or study by independently reproducing the entire process and obtaining the same results. This is a core principle of the scientific method.
2. Using R and RStudio aids reproducibility by encouraging researchers to structure projects systematically, automate analyses with code rather than manual steps, and connect analyses and results to written reports through tools like R Markdown.
3. Version control systems like git allow researchers to track changes, revert to previous versions of documents and code, and facilitate collaboration through online repositories like GitHub.
This document provides an introduction to data analysis and graphics in R. It covers vectors and assignment, data types including logical, integer, numeric, character, factor, complex and raw. It also discusses data structures such as atomic vectors, matrices, arrays and lists. Finally, it discusses importing data into R from files such as .RData files, text files using read.table(), CSV files and Excel files.
This document provides an outline for a course on quantitative data analysis and graphics in R. The course will cover planning a data analysis, basics of data analysis, testing for normality, choosing appropriate statistical tests, hypothesis testing, confidence intervals, and statistical significance tests both parametric and non-parametric. It will also cover the differences between hypothesis testing and confidence intervals.
There are now a couple of alternative interpreters (or engines) for the R programming language. In this presentation, I will give a gentle introduction to Renjin, which is an open-source project to implement an R interpreter in Java.The introduction should appeal a wide audience, from data scientists to (web) application developers and will cover topis such as "Why build another R interpreter?", "For whom is Renjin?", "What can you do with Renjin?", "How does Renjin compare to GNU R and the other alternative engines like pqR, FastR, and TERR or pseudo-alternatives like Microsoft R and Oracle R Distribution?", "How can I try Renjin?", and more.
This document provides an overview and introduction to R and R Studio. It outlines the objectives of understanding R as a programming language, installing R and R Studio, and an introduction to R as an object-oriented language. Instructions are provided on installing R for Windows and Mac as well as installing R Studio. An overview of the R Studio interface is given along with details on upgrading R to a specific version. The document concludes with information on R as an object-oriented language and creating and manipulating objects.
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.
R is a programming language and software environment for statistical analysis and graphics. It originated from S, a statistical programming language developed in the 1970s. R was first released in 1993 and has since grown in popularity due to its ability to run on Linux, Windows and Mac operating systems. It allows users to contribute additional packages to extend its functionality. Getting help in R can be obtained through manuals, online searches, and mailing lists. R has a command line interface but various graphical user interfaces and integrated development environments are also available. Everything in R is an object that has a class and methods, with common functions to define classes, create objects, and extract object elements.
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.
This document provides an introduction to using R for statistical analysis and data visualization. It discusses installing R and RStudio, the basic layout of RStudio, composing and executing R scripts, performing calculations and importing datasets in R. It also covers linear regression analysis, cleaning data using dplyr and tidyr packages, and includes a comprehensive exercise using a dataset of World Cup results. The goal is to equip readers with basic skills in using R for data analysis after completing the exercises in the document.
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
INT232 __ DATA SCIENCE TOOLBOX _ R PROGRAMMING.pdfVeerpalkhaira
This document provides an instruction plan for a course on Data Science Toolbox: R Programming. It outlines the course code, title, reference books, relevant websites, virtual labs, and course outcomes. The instruction plan spans 8 weeks and includes 14 lectures covering topics such as installing R, introduction to basics of R including vectors and matrices, factors, and data frames. Each lecture lists the related chapters, readings, and software as well as the learning objectives and pedagogical tools to be used such as demonstrations, case studies, and examples.
This document outlines an R crash course to teach the basics of R to beginners in a short period of time. The course will cover installing R software, scripting in R, working with spatial data in R, and linking R with other programs like SAGA GIS. The document discusses what R is, why it is useful for data analysis and popular in the statistics community, and some assumptions about the course participants and format.
R Vs Python – The most trending debate of aspiring Data Scientistsabhishekdf3
Now, it’s the time for a battle of two most demanding programming languages that is R vs Python. We will go deep in understanding the differences between the two languages. And, I assure you that you will not have any confusion left after completing this article i.e. R vs Python – the most trending debate of aspiring data scientists.
Learn more at :- https://data-flair.training/
R programming advantages and disadvantagesPrwaTech
R is a free and open-source programming language and software environment for statistical analysis, graphics, and statistical computing. It is widely used among data scientists for statistical modeling and analysis. Some key advantages of R include its large number of contributed packages, ability to handle complex data, and use for statistical analysis and machine learning. However, it also has some disadvantages like using more memory than other languages and having a steep learning curve.
This document provides an introduction and overview of R programming for statistics. It discusses how to run R sessions and functions, basic math operations and data types in R like vectors, data frames, and matrices. It also covers statistical and graphical features of R, programming features like functions, and gives examples of built-in and user-defined functions.
This document provides a training manual on better graphics in R. It begins with an overview of R and BioConductor and reviews basic R functions. It then covers creating simple and customized graphics, multi-step graphics with legends, and multi-panel layouts. The manual aims to help researchers learn visualization techniques to improve the communication of their data and results.
The C & C++ Advanced Program targets first and second year degree and final year diploma students with a basic knowledge of C programming. The goals are to learn C in-depth, including memory manipulation, files, structures, bitwise operations, and preprocessor directives, and to learn object-oriented C++ concepts like encapsulation, abstraction, inheritance, polymorphism, and STL. The syllabus covers these topics along with practical programs.
The Linux & Perl course also targets first and second year degree and final year diploma students with access to Linux. It teaches Perl scripting for text processing and modules, shell scripts, Linux commands, vi editor, Perl identifiers, scalars, lists, hashes, loops, subrout
Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)
This document discusses R and Julia for data analysis and advanced analytics. It provides an overview of R's history, how it works, performance improvements, and use in production. Julia is introduced as a new high-performance dynamic language with similarities to R but faster performance due to its just-in-time compiler and type information. Examples are given comparing the performance of Julia to other languages. The document recommends Julia for those already using C/Fortran and suggests it will be useful for R users once fully developed.
1. Reproducible research is the ability to reproduce an experiment or study by independently reproducing the entire process and obtaining the same results. This is a core principle of the scientific method.
2. Using R and RStudio aids reproducibility by encouraging researchers to structure projects systematically, automate analyses with code rather than manual steps, and connect analyses and results to written reports through tools like R Markdown.
3. Version control systems like git allow researchers to track changes, revert to previous versions of documents and code, and facilitate collaboration through online repositories like GitHub.
This document provides an introduction to data analysis and graphics in R. It covers vectors and assignment, data types including logical, integer, numeric, character, factor, complex and raw. It also discusses data structures such as atomic vectors, matrices, arrays and lists. Finally, it discusses importing data into R from files such as .RData files, text files using read.table(), CSV files and Excel files.
This document provides an outline for a course on quantitative data analysis and graphics in R. The course will cover planning a data analysis, basics of data analysis, testing for normality, choosing appropriate statistical tests, hypothesis testing, confidence intervals, and statistical significance tests both parametric and non-parametric. It will also cover the differences between hypothesis testing and confidence intervals.
This document provides an introduction and overview of graphics and plotting in R. It discusses high level and low level plotting functions, interacting with graphics, and modifying plots. It also covers plotting different variable types including dichotomous, categorical, ordinal, and continuous variables. Examples are provided for various plot types including histograms, bar plots, dot plots, boxplots, and more.
This document provides an introduction and overview of summarizing data in R. It discusses numerical summaries for different variable types including discrete, continuous, dichotomous, categorical, and ordinal variables. Measures of central tendency like mean, median and mode are covered as well as measures of dispersion. Skewness and kurtosis are also discussed. Examples of calculating these summaries for sample datasets are provided.
This document provides an overview of data entry, management, and manipulation in R. It discusses how to create datasets using various functions like c(), matrix(), data.frame(), and list(). It also covers understanding dataset properties, importing data, creating new variables, and subsetting datasets. Useful functions for working with datasets include mode(), length(), dim(), names(), and attributes(). The document shows examples of entering data using these different methods.
R is a free and open-source programming language and software environment for statistical analysis and graphics. RStudio is a popular integrated development environment (IDE) for R that provides a convenient graphical user interface. This document introduces R and RStudio, covering how to install them, their basic layout and features, and how to get help when working with R. Key functions and concepts discussed include loading and installing packages, working directories, and calling functions.
This document introduces level three of a data analysis tutorial series, which focuses on statistics fundamentals. The level aims to provide a good statistics foundation by covering descriptive statistics, exploratory data analysis, inferential statistics, categorical analysis, time series analysis, and survival analysis. Students will learn how to describe and make inferences from data, and the reasoning behind statistical calculations and assumptions to enable sound interpretation of results.
This document provides an introduction to version control systems using Git and GitHub. It begins with an overview of why version control is important and the evolution of version control systems from local to centralized to distributed. It then discusses installing and setting up Git, initializing and tracking files in a Git repository, committing changes, and ignoring files that should not be tracked via a .gitignore file. The goal is for students to understand the basics of Git and GitHub and be able to version control files and collaborate on projects.
A needs analysis involves comparing current conditions to desired goals to understand performance problems. It can be extensive, using large sample sizes for general understanding, or intensive, using smaller samples for in-depth cause-and-effect analysis. Performing a needs analysis involves gap analysis, identifying priorities, outlining a methodology, gathering and analyzing both quantitative and qualitative data, presenting findings, and making conclusions and recommendations. An example needs assessment addressed gender-based violence in schools in Africa through stakeholder interviews, performances, photo voices, drawings, and documentaries to develop an action plan.
This document discusses result-based monitoring and evaluation (M&E). It defines monitoring as the systematic collection of data on indicators, and evaluation as the objective assessment of a project or program's design, implementation and results. The purpose of M&E is to assess progress, determine relevance and fulfillment of objectives, and enhance transparency and accountability. Key aspects of result-based M&E covered include logical frameworks, methods/tools like rapid appraisal and impact evaluation, and essential actions to build an effective result-based M&E system like formulating goals and indicators to measure outcomes.
This document provides an introduction to regular expressions (regex) in R. It discusses literal regex which match text exactly, and metacharacters which have special meanings like ., *, ?, etc. It also covers character classes [ ], anchors ^ and $, quantifiers like ?, *, +, {}, alternations |, and capturing groups () in regex. The document uses examples of matching file names and dates to illustrate regex patterns and their uses in text matching and replacement.
This document provides instructions for a case study on web scraping Olympics data from Wikipedia pages using R. It discusses importing the raw HTML data from the URLs using readLines(), then exploring the structure and content of the imported data. The document emphasizes using base R functions instead of packages for this task. It then provides an overview of HTML to help understand how to locate and extract the desired data tables from the raw HTML.
This document describes several web scraping functions:
1. A web scraper function that extracts data from an HTML document as a character object based on CSS selectors.
2. A function to check if an object is a valid HTML element.
3. A tag counter function that counts the number of tags in a string.
4. Closing tag locator functions that find the closing tags for given opening tags.
5. A content extractor function that extracts HTML elements based on tag positions and can remove tags or construct a data frame.
6. A table constructor function that creates a data frame from an HTML table.
7. A content remover function that removes unwanted content from extractor
This document provides guidance on solving coding problems in R. It discusses identifying and defining problems, and where and how to get help. For problems where the necessary functions are unknown, it recommends gaining foundational R skills through tutorials and practice. For known functions producing errors, warnings or unexpected output, it demonstrates defining the specific problem and potential causes. Sources of help discussed include R's internal documentation, manuals, FAQs, and external resources like web searches and mailing lists. The document also provides an example of solving a statistical mode problem in R.
Plot() is the main plotting function in base R. It is a generic function that dispatches different methods depending on the class of the first argument. When called, plot() follows 8 steps: 1) Open a new plotting window, 2) Set the plotting coordinates, 3) Evaluate pre-plot expressions, 4) Make the actual plot, 5) Evaluate post-plot expressions, 6) Add axes, 7) Add a frame, and 8) Add annotations. The class of the first argument determines which plotting method is used, and additional arguments can customize the plot output.
This document provides an overview of working with dates and times in R. It discusses recognizing date-time objects in R, getting the current date and time, and creating date-time objects using the POSIXct and POSIXlt classes. Methods for converting character and numeric data to date-time objects are presented, along with extracting parts of date-time objects and performing computations. The goal is to introduce the reader to key date and time functionality in base R.
This document discusses indexing and manipulating data objects in R. It covers indexing one-dimensional objects like vectors and lists, as well as two-dimensional objects like matrices and data frames. Binary operators for comparisons like equality and inequality are also described. The key topics are:
1) Indexing values in data objects using integers, characters, or logical values and discussing one-dimensional vs multi-dimensional objects.
2) Comparing values within and between vectors using binary operators like equality ("==") and inequality ("!="). These operate element-wise on vectors.
3) Checking conditions on data using functions like "all()", "any()", and "which()" to subset objects based on logical criteria.
This document discusses key concepts related to files in R including file names, formats, paths, encodings, and types. It describes text files as human-readable files organized in lines with different extensions for different programs. Binary files contain machine-readable 1s and 0s. Paths locate files in a directory hierarchy using components like parent directories denoted by "..". Common encodings include ASCII for English and UTF-8 for multiple languages. R supports text, binary, and delimited files like CSVs that separate values with commas.
This document discusses importing and exporting data in R. It covers importing data from local files, networks, and databases using both the graphical user interface (GUI) and command line. The key aspects to consider when importing data are the encoding, headers, row names, separators, decimals, quotations, comments, and missing values. Delimited files like CSVs can be imported using read.table() and its wrapper functions, while other file types require packages like foreign and haven. Data can also be exported from R using base functions or packages. The document provides examples of importing delimited text files from a local directory and webpage.
This document discusses R data types and objects. It covers the basic data types in R: logical, integer, real/double, string/character, complex, and raw. The most common data structures are vectors, matrices, arrays, data frames, and lists. Vectors can be atomic, containing one data type, or generic lists, containing multiple data types. The document demonstrates how to create vectors using the c() function or colon operator, and how to name vectors by assigning them to an object. It also discusses the basic properties of vectors like their type, length, dimensions, and classes.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
1. Prelude to Level Two
Hellen Gakuruh
January 19, 2017
Nitty Gritty of R
Welcome to level 2, in this level we delve into R programming. The assumption is that you
have basic knowledge of R and can make a function call.
In this session, just like it's predecessor (Level One), the approach would be non-text book
and more on concept building with an aim of making the transition to programming as easy
as possible.
Level two is meant to impart some programming skills for those interested in going beyond
R's functions. The skills learnt in this level are all geared towards developing user defined
functions which can be packaged and shared on GitHub and/or CRAN. This level will also
introduce a growing issue, that is, reproducible analysis. Therefore, if your goal is to learn
how to use R to do basic analysis using R's functions, then you can skip the second level and
only refer to it as need be.
What we will cover:
• SessionEleven: Looping System in R
• SessionTwelve: Environments
• SessionThirteen: Introduction to function development
• SessionFourteen: Reproducible Analysis in R (Rmarkdown and Shiny)
• SessionFifteen: Package Development