This presentation educates you about R - data types in detail with data type syntax, the data types are - Vectors, Lists, Matrices, Arrays, Factors, Data Frames.
For more topics stay tuned with Learnbay.
This presentation educated you about R - Factors with example syntax and demo program of Factors in Data Frame, Changing the Order of Levels and Generating Factor Levels.
For more topics stay tuned with Learnbay.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
This presentation educates you about R - data types in detail with data type syntax, the data types are - Vectors, Lists, Matrices, Arrays, Factors, Data Frames.
For more topics stay tuned with Learnbay.
This presentation educated you about R - Factors with example syntax and demo program of Factors in Data Frame, Changing the Order of Levels and Generating Factor Levels.
For more topics stay tuned with Learnbay.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
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.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
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.
This presentation provides a brief introduction to data types and objects in R. I've not covered 'array' in the presentation, which is a multi-dimensional object [More general than matrix].
Data Science, Statistical Analysis and R... Learn what those mean, how they can help you find answers to your questions and complement the existing toolsets and processes you are currently using to make sense of data. We will explore R and the RStudio development environment, installing and using R packages, basic and essential data structures and data types, plotting graphics, manipulating data frames and how to connect R and SQL Server.
Scenario of E library in the 21st century by
Dr. Gururaj S. Hadagali
Assistant Professor
Dept. of Library and Information Science
Karnatak University, Dharwad
.Introduction
.Pre -Requisites to form a contract
.What contract means ?
.Who are competent to contract
.Free consent
.Classification of contracts
.Conclusion
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
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).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
3. R - objects
In R programming language,
• Variables are not declared as some data types
• Variables are assigned with R – objects
• The data type of R-object becomes the data
type of the variable.
NOTE: -> everything is just R - object
4. Basic Data Types in R
R Programming works with various data types,
–Scalars
–Vectors
–Matrices
–Factors
–Data frames
–Lists
6. VECTORS
• A vector is a one-dimensional array.
• We can create a vector with all the basic data
type we learnt before.
• The simplest way to build a vector in R, is to
use the ‘ c ‘ command.
Ex: num_vec <- c(1, 2, 3, 4)
chr_vec <- c(“a”, ”b”, ”c”)
7. MATRIX
• A matrix is a 2-dimensional array that has m
number of rows and n number of columns.
• In other words, matrix is a combination of two or
more vectors with the same data type.
NOTE: In R, more than two-dimensional arrays
can also be created
Semantic:
matrix( data, nrow, ncol, byrow = TRUE/FALSE)
8. FACTORS
• Factor is a variable in R which take on a limited
number of different values; such variables are
often referred to as categorical variables.
• In other words, R stores categorical variables into
a factor.
Semantic:
factor(x = character(), levels, labels = levels,
ordered = is.ordered(x) )
9. Data frames
• A data frame is a list of vectors which are of
equal length.
• A matrix contains only one type of data, while
a data frame accepts different data types
(numeric, character, factor, etc.).
Semantic:
data.frame( df, stringsAsFactors = TRUE)
10. Lists
• A list store many kinds of object in the order expected.
• It can include matrices, vectors data frames or lists.
• A list is similar; we can store a collection of objects and
use them when we need them.
Semantic:
list( element_1, element_2, ... )
arguments: -element_1: store any type of R object -...:
pass as many objects as specifying.
Each object needs to be separated by a comma.