Learn the built-in mathematical functions in R. This tutorial is part of the Working With Data module of the R Programming course offered by r-squared.
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
Learn the basics of data visualization in R. In this module, we explore the Graphics package and learn to build basic plots in R. In addition, learn to add title, axis labels and range. Modify the color, font and font size. Add text annotations and combine multiple plots. Finally, learn how to save the plots in different formats.
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
Learn the basics of data visualization in R. In this module, we explore the Graphics package and learn to build basic plots in R. In addition, learn to add title, axis labels and range. Modify the color, font and font size. Add text annotations and combine multiple plots. Finally, learn how to save the plots in different formats.
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Statistics And Probability Tutorial | Statistics And Probability for Data Sci...Edureka!
YouTube Link: https://youtu.be/XcLO4f1i4Yo
** Data Science Certification using R: https://www.edureka.co/data-science **
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language.
Learn to manipulate strings in R using the built in R functions. This tutorial is part of the Working With Data module of the R Programming Course offered by r-squared.
In this tutorial, we learn to create variables in R. Followed by that, we explore the different data types including numeric, integer, character, logical and date/time.
This is a presentation on Arrays, one of the most important topics on Data Structures and algorithms. Anyone who is new to DSA or wants to have a theoretical understanding of the same can refer to it :D
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
A project to create at least two predictive Machine Learning models to analyze a business situation.
Description of Business Situation - The hiring managers of Pas de Poissen sought the guidance of a consulting firm to determine which of the nationality of the foreign workforce, entering Canada, would have the highest probability that a judge would approve their appeal to remain, and subsequently be employable in the country.
Establishing a model to best determine which candidates to hire provided exceptional cost saving opportunities. In the past, if the company was informed that one of their new foreign national workers was not granted an appeal, and was actively on a fishing deployment, at times lasting for over 45 days, the trawler was forced to return to port. A vessel having to return equated to missed opportunistic revenue, as it could no longer fish, and unexpected fuel expenses to return to homeport. Furthermore, the penalty for knowing employing an illegal foreign worker was harsh from both the Canadian and U.S fisheries enforcement agencies.
Deliverables -
A description of the business problem we are addressing
How and where we obtained the data, and the steps we went through to insure that it was "clean"
A summary of modeling steps, with reference to the predictive models in the project file
Assessment of the accuracy of models, with reference to project file results
Our interpretation of the results of our analysis
What we learnt, and how might it inform the business situation that we chose to analyze
Source: Rattle Library
Name: “Green: Refugee Appeal”
Predictive Models : "Forest Model" and "Boosting Model"
Wind flow simulations on forested zone have been performed with Computational Fluid Dynamics (CFD) software meteodyn WT, which allows introducing a custom forest canopy model. The influence of parameter changes on results is investigated. The calibration of model parameters is done by minimizing the error between the CFD results and the vertical wind profiles given by the European standard Eurocode 1 (EC1), applied to standard terrains for high roughness cases. The calibrated model shows good coherence with EC1. To check the validity of the forest modeling in the real case, CFD simulation has been performed on a site with heterogeneous forest covering. The computed wind characteristics are then compared to met mast measurement. The comparison shows good agreement on wind shear and turbulence intensity between the simulation results and the measured data.
Statistics And Probability Tutorial | Statistics And Probability for Data Sci...Edureka!
YouTube Link: https://youtu.be/XcLO4f1i4Yo
** Data Science Certification using R: https://www.edureka.co/data-science **
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language.
Learn to manipulate strings in R using the built in R functions. This tutorial is part of the Working With Data module of the R Programming Course offered by r-squared.
In this tutorial, we learn to create variables in R. Followed by that, we explore the different data types including numeric, integer, character, logical and date/time.
This is a presentation on Arrays, one of the most important topics on Data Structures and algorithms. Anyone who is new to DSA or wants to have a theoretical understanding of the same can refer to it :D
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
A project to create at least two predictive Machine Learning models to analyze a business situation.
Description of Business Situation - The hiring managers of Pas de Poissen sought the guidance of a consulting firm to determine which of the nationality of the foreign workforce, entering Canada, would have the highest probability that a judge would approve their appeal to remain, and subsequently be employable in the country.
Establishing a model to best determine which candidates to hire provided exceptional cost saving opportunities. In the past, if the company was informed that one of their new foreign national workers was not granted an appeal, and was actively on a fishing deployment, at times lasting for over 45 days, the trawler was forced to return to port. A vessel having to return equated to missed opportunistic revenue, as it could no longer fish, and unexpected fuel expenses to return to homeport. Furthermore, the penalty for knowing employing an illegal foreign worker was harsh from both the Canadian and U.S fisheries enforcement agencies.
Deliverables -
A description of the business problem we are addressing
How and where we obtained the data, and the steps we went through to insure that it was "clean"
A summary of modeling steps, with reference to the predictive models in the project file
Assessment of the accuracy of models, with reference to project file results
Our interpretation of the results of our analysis
What we learnt, and how might it inform the business situation that we chose to analyze
Source: Rattle Library
Name: “Green: Refugee Appeal”
Predictive Models : "Forest Model" and "Boosting Model"
Wind flow simulations on forested zone have been performed with Computational Fluid Dynamics (CFD) software meteodyn WT, which allows introducing a custom forest canopy model. The influence of parameter changes on results is investigated. The calibration of model parameters is done by minimizing the error between the CFD results and the vertical wind profiles given by the European standard Eurocode 1 (EC1), applied to standard terrains for high roughness cases. The calibrated model shows good coherence with EC1. To check the validity of the forest modeling in the real case, CFD simulation has been performed on a site with heterogeneous forest covering. The computed wind characteristics are then compared to met mast measurement. The comparison shows good agreement on wind shear and turbulence intensity between the simulation results and the measured data.
With data analysis showing up in domains as varied as baseball, evidence-based medicine, predicting recidivism and child support lapses, judging wine quality, credit scoring, supermarket scanner data analysis, and “genius” recommendation engines, “business analytics” is part of the zeitgeist. This is a good moment for actuaries to remember that their discipline is arguably the first – and a quarter of a millennium old – example of business analytics at work. Today, the widespread availability of sophisticated open-source statistical computing and data visualization environments provides the actuarial profession with an unprecedented opportunity to deepen its expertise as well as broaden its horizons, living up to its potential as a profession of creative and flexible data scientists.
This session will include an overview of the R statistical computing environment as well as a sequence of brief case studies of actuarial analyses in R. Case studies will include examples from loss distribution analysis, ratemaking, loss reserving, and predictive modeling.
Learn to manipulate numbers in R using the built in numeric functions. This tutorial is part of the Working With Data module of the R Programming course offered by r-squared.
Learn to compare objects in R using built-in comparison functions. This tutorial is part of the Working With Data module of the R Programming course offered by r-squared.
Overview of a few ways to group and summarize data in R using sample airfare data from DOT/BTS's O&D Survey.
Starts with naive approach with subset() & loops, shows base R's tapply() & aggregate(), highlights doBy and plyr packages.
Presented at the March 2011 meeting of the Greater Boston useR Group.
A comprehensive introduction to handling date and time data in R. Get an introduction to date and time manipulation in R. Learn to create, transform, extract and operate on date/time objects.
Learn the grammar of data manipulation using dplyr. You will work through a case study to explore the dplyr verbs such as filter, select, mutate, arrange, summarize, group_by etc.
Learn to write readable code with pipes using the magrittr package. You will learn about the forward operator (%>%), exposition operator (%$%) and the assignment operator (%<>%).
tibbles are an alternative for dataframes. You will learn how tibbles are different from dataframes, why you should use them, how to create and modify them.
Learn how to install & update R packages from CRAN, GitHub, Bioconductor etc. You wlll also learn to install specific versions of a package from CRAN or GitHub.
A brief introduction to the R ecosystem for absolute beginners. You will learn about the history and capabilities of R as a modern language for data science.
In this tutorial, we learn to access MySQL database from R using the RMySQL package. The tutorial covers everything from creating tables, appending data to removing tables from the database.
In this tutorial, we learn to create dynamic documents using R Markdown. It enables us to create beautiful reports and presentations that are fully reproducible.
In this tutorial, we learn to create univariate bar plots using the Graphics package in R. We also learn to modify graphical parameters associated with the bar plot.
In this tutorial, we explore the most basic data structure in R, the vector. We cover everything from creating vectors to subsetting them in different ways.
Data Visualization With R: Learn To Combine Multiple GraphsRsquared Academy
In this tutorial, we learn to combine multiple graphs into a single frame using the par() and layout() functions. We also compare the differences between the two functions.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
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.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
3. r-squared
Slide 3
Working With Data
www.r-squared.in/rprogramming
✓ Data Types
✓ Data Structures
✓ Data Creation
✓ Data Info
✓ Data Subsetting
✓ Comparing R Objects
✓ Importing Data
✓ Exporting Data
✓ Data Transformation
✓ Numeric Functions
✓ String Functions
✓ Mathematical Functions
4. r-squared
In this unit, we will explore the following mathematical functions:
Slide 4
Mathematical Functions
www.r-squared.in/rprogramming
● Arithmetic Operators
● Column/ Row Operators
● Cumulative Operators
● Sampling
● Set Operations
● Logarithm
8. r-squared
Slide 8
Column & Row Operations
www.r-squared.in/rprogramming
Operator Description
colSums Sum of column values
rowSums Sum of row values
colMeans Mean of column values
rowMeans Mean of row values
9. r-squared
Slide 9
Column & Row Operations
www.r-squared.in/rprogramming
Examples
> # example 1
> m
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
> colSums(m) # sum of columns
[1] 6 15 24
> rowSums(m) # sum of rows
[1] 12 15 18
> colMeans(m) # mean of columns
[1] 2 5 8
> rowMeans(m) # mean of rows
[1] 4 5 6
23. r-squared
In the next module, we will explore selection statements in R:
Slide 23
Next Steps...
www.r-squared.in/rprogramming
● if()
● if else()
● ifelse()
● switch()
24. r-squared
Slide 24
Connect With Us
www.r-squared.in/rprogramming
Visit r-squared for tutorials
on:
● R Programming
● Business Analytics
● Data Visualization
● Web Applications
● Package Development
● Git & GitHub