A brief introduction to data visualisation using R. It contains both basic and advanced visualisation techniques with sample codes. The datasets being used are mostly available with RStudio.
1. The document discusses matrices and their uses. It provides examples of matrices and defines them as rectangular arrangements of numbers, expressions, or symbols arranged in rows and columns.
2. Matrices have various real-world applications including surveys, population data, gross domestic products, robotics, graphics, message encoding, and dimensional works. They are used in tools like Google search algorithms and seismic mapping.
3. The history of matrices dates back to ancient times but the term was introduced in 1850. An important ancient Chinese text from 300 BC to 200 AD provides the first example of using matrices to solve simultaneous linear equations.
Linear algebra is used in many applications including search engine ranking, error correcting codes, graphics, facial recognition, signal analysis, prediction, computer gaming, and quantum computing. It was used in the original Google ranking algorithm and remains important for search today. Linear algebra also allows encoding of data for error correction and is fundamental to representing and projecting 3D graphics onto 2D screens. Facial recognition systems use principal component analysis from linear algebra to identify faces.
Matrices are rectangular arrangements of numbers or expressions that are organized into rows and columns. They have many applications in fields like physics, computer science, mathematics, and engineering. Specifically, matrices are used to model electrical circuits, for image projection and page ranking algorithms, in matrix calculus, for encrypting messages, in seismic surveys, representing population data, calculating GDP, and programming robot movements. Matrices play a key role in solving problems across many domains through their representation of relationships between variables.
Matrices are used extensively in computer applications related to graphics and image processing. Matrices represent images as a collection of coordinate points, and changing the values in the matrix allows images to be transformed through operations like scaling, rotation, and distortion. Matrices are also used to encrypt and decrypt codes and messages. Overall, matrices play a vital role in computer applications by enabling graphical representations and transformations that would otherwise be very complicated to achieve.
Application of matrices in real life and matrixDarshDobariya
The document provides an overview of matrices, including:
- A brief history of matrices dating back to ancient times.
- Different types of matrices like row, column, null, square, diagonal, and more.
- Applications of matrices in fields like computer graphics, cryptography, wireless communication, robotics, and chemistry. Matrices are used to represent transformations, encode/decode messages, model wireless signals, program robot movements, and balance chemical equations.
- The document contains examples of matrix usage in graphics, cryptography, wireless communication, robotics, and chemistry.
This presentation introduces discrete mathematics and its applications in computer science. It discusses several topics in discrete mathematics including sets, graphs, probability theory, number theory, trees, and topology. It also lists the group members and states that discrete mathematics deals with discrete, separated objects and is important for theoretical computer science, information theory, mathematical logic, and other areas of computing.
Linear algebra provides concepts that are crucial to many areas of computer science, including graphics, machine learning, computer vision, and more. It is used for internet searches, network modeling, graphs, bioinformatics, scientific computing, data mining, and cryptography. Matrix computations, which are operations on matrices, are important for computer programming in areas like graphics, modeling, and business logic. Linear algebra is also helpful for designing network models and solving problems that arise in scientific computing and other disciplines.
1. The document discusses matrices and their uses. It provides examples of matrices and defines them as rectangular arrangements of numbers, expressions, or symbols arranged in rows and columns.
2. Matrices have various real-world applications including surveys, population data, gross domestic products, robotics, graphics, message encoding, and dimensional works. They are used in tools like Google search algorithms and seismic mapping.
3. The history of matrices dates back to ancient times but the term was introduced in 1850. An important ancient Chinese text from 300 BC to 200 AD provides the first example of using matrices to solve simultaneous linear equations.
Linear algebra is used in many applications including search engine ranking, error correcting codes, graphics, facial recognition, signal analysis, prediction, computer gaming, and quantum computing. It was used in the original Google ranking algorithm and remains important for search today. Linear algebra also allows encoding of data for error correction and is fundamental to representing and projecting 3D graphics onto 2D screens. Facial recognition systems use principal component analysis from linear algebra to identify faces.
Matrices are rectangular arrangements of numbers or expressions that are organized into rows and columns. They have many applications in fields like physics, computer science, mathematics, and engineering. Specifically, matrices are used to model electrical circuits, for image projection and page ranking algorithms, in matrix calculus, for encrypting messages, in seismic surveys, representing population data, calculating GDP, and programming robot movements. Matrices play a key role in solving problems across many domains through their representation of relationships between variables.
Matrices are used extensively in computer applications related to graphics and image processing. Matrices represent images as a collection of coordinate points, and changing the values in the matrix allows images to be transformed through operations like scaling, rotation, and distortion. Matrices are also used to encrypt and decrypt codes and messages. Overall, matrices play a vital role in computer applications by enabling graphical representations and transformations that would otherwise be very complicated to achieve.
Application of matrices in real life and matrixDarshDobariya
The document provides an overview of matrices, including:
- A brief history of matrices dating back to ancient times.
- Different types of matrices like row, column, null, square, diagonal, and more.
- Applications of matrices in fields like computer graphics, cryptography, wireless communication, robotics, and chemistry. Matrices are used to represent transformations, encode/decode messages, model wireless signals, program robot movements, and balance chemical equations.
- The document contains examples of matrix usage in graphics, cryptography, wireless communication, robotics, and chemistry.
This presentation introduces discrete mathematics and its applications in computer science. It discusses several topics in discrete mathematics including sets, graphs, probability theory, number theory, trees, and topology. It also lists the group members and states that discrete mathematics deals with discrete, separated objects and is important for theoretical computer science, information theory, mathematical logic, and other areas of computing.
Linear algebra provides concepts that are crucial to many areas of computer science, including graphics, machine learning, computer vision, and more. It is used for internet searches, network modeling, graphs, bioinformatics, scientific computing, data mining, and cryptography. Matrix computations, which are operations on matrices, are important for computer programming in areas like graphics, modeling, and business logic. Linear algebra is also helpful for designing network models and solving problems that arise in scientific computing and other disciplines.
Design of suitable Magnitude Comparator Architecture for Big Data Analyticsrahulmonikasharma
In today’s digital era increasing use of portable devices forces electronic designer to concentrate on high speed and low power dissipation. As magnitude comparator is very basic arithmetic unit, to cope up with high speed and optimum power for big data, we need suitable magnitude comparator architecture, so this Paper presents different types of magnitude comparator architecture such as serial, parallel and tree structure. Proposed 64 bit magnitude comparator is designed with 1.8v voltage supply using in standard CMOS 180nm Technology using Cadence Virtuoso EDA tool. Simulation of all three architecture have been done using SPECTRE VIRTUOSO ADE tool.
Matrices have various applications in real life. They are used in physics to study electrical circuits, quantum mechanics, and optics. Programmers also use matrices and inverse matrices for coding and encrypting messages. In dimensional work, matrices help project 3D images onto 2D screens to create realistic motions. Matrices are applied in economics to calculate GDP and efficiently measure goods production. They also help organizations like scientists record experiment data. Google search uses matrices in its page rank algorithm to rank search results.
This document discusses matrices and their uses. It defines what a matrix is and provides examples of different types of matrices like row matrices, column matrices, null matrices, identity matrices, diagonal matrices, triangular matrices, and transpose matrices. It then discusses some applications of matrices like in cryptography for encrypting messages, in electrical circuits, quantum mechanics, optics, robotics, automation, economics, and more. Matrices are useful for tasks like plotting graphs, scientific studies, page ranking algorithms, image projection, representing real world data, and calculating gross domestic products.
Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation, and presentation of data.
Some scholars pinpoint the origin of statistics to 1663, with the publication of Natural and Political Observations upon the Bills of Mortality by John Graunt. Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data, hence its stat- etymology. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences.
The document proposes using microaggregation techniques to generate k-anonymous datasets that satisfy t-closeness, addressing limitations of existing approaches using generalization and suppression. It presents three microaggregation-based algorithms to reconcile privacy and utility goals: one merges clusters as needed for t-closeness, while two modify the microaggregation process to directly consider t-closeness. Microaggregation preserves data utility better than generalization by maintaining granularity and numbers' continuous nature, and handles outliers less disruptively. The algorithms are empirically evaluated for generating privacy-preserving datasets.
What is matrix? Matrix in physics. Matrix in computer science. Matrix in encryption. Matrix in others sector. geology surveys,robot movement,scientific experiment.
Applications of matrices in Real\Daily lifeSami Ullah
Matrices are used in a wide variety of applications in real life. They are used in physics for electrical circuits and quantum mechanics. Stochastic matrices are used in page rank algorithms like Google search. Matrices are also used for encryption in computer applications and coding messages. They allow for secure transmission of data online and for banks. Matrices are applied in fields like geology, statistics, science, economics, robotics, and by scientists recording experimental data. They provide a way to represent and analyze real world data.
Matrices are rectangular arrangements of numbers, expressions, or symbols organized into rows and columns. They are used across many fields including physics for electrical circuits and optics calculations, computing for 3D graphics and encryption, geology for seismic surveys and data analysis, animation for 3D modeling and transformations, statistics and economics for presenting real-world data and calculating GDP, and other fields like data storage, robotics, analytical concepts, and software design. Matrices have wide applications in science, technology, engineering and mathematics.
Exploring optimizations for dynamic PageRank algorithm based on GPU : V4Subhajit Sahu
This is my comprehensive viva report version 4.
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Graph is a generic data structure and is a superset of lists, and trees. Binary search on sorted lists can be interpreted as a balanced binary tree search. Database tables can be thought of as indexed lists, and table joins represent relations between columns. This can be modeled as graphs instead. Assignment of registers to variables (by compiler), and assignment of available channels to a radio transmitter and also graph problems. Finding shortest path between two points, and sorting web pages in order of importance are also graphs problems. Neural networks are graphs too. Interaction between messenger molecules in the body, and interaction between people on social media, also modeled as graphs.
The document discusses various types of graphs used in statistics including bar graphs, pie charts, and pictographs. It provides examples and definitions of each type of graph as well as their uses and advantages. It explains that bar graphs can be used to compare data between groups, pie charts show proportional relationships, and pictographs represent data using pictures. The document also provides examples of different variations of pie charts such as polar area, multi-level, and exploded pie charts.
Graph Tea: Simulating Tool for Graph Theory & AlgorithmsIJMTST Journal
Simulation in teaching has recently entered the field of education. It is used at different levels of instruction.
The teacher is trained practically and also imparted theoretical learning. In Computer Science, Graph theory
is the fundamental mathematics required for better understanding Data Structures. To Teach Graph theory &
Algorithms, We introduced Simulation as an innovative teaching methodology. Students can understand in a
better manner by using simulation. Graph Tea is one of such simulation tool for Graph Theory & Algorithms.
In this paper, we simulated Tree Traversal Techniques like Breadth First Search (BFS), Depth First Search
(DFS) and minimal cost spanning tree algorithms like Prims.
one of the areas of discrete mathematics is graph theory. From a pure mathematics viewpoint, graph theory studies the pairwise relationships between objects. Those objects are vertices. Graph theory is frequently applied to analysing relationships between objects. It is a natural extension of graph theory to apply that mathematical tool to the evaluation of forensic evidence. In fact the literature reveals several, limited, forensic applications of graph theory. The current paper describes a more broad based application of graph theory to the problem of evaluation relationships in forensic investigation. The process takes standard graph theory and identifies entities in the investigation as vertices with the connections between the various entities as edges. Those entities can be suspects, victims, computer system, or any entity relevant to the investigation. Regardless of the nature of the entity, all entities are represented as vertices, and the relationship between them is represented as edges connecting the vertices. This allows the mathematical modelling of the events in question and facilitates analysis of the data.
This document provides an overview of unsupervised learning techniques, specifically clustering algorithms. It discusses the differences between supervised and unsupervised learning, the goal of clustering to group similar observations, and provides examples of K-Means and hierarchical clustering. For K-Means clustering, it outlines the basic steps of randomly assigning clusters, calculating centroids, and repeatedly reassigning points until clusters stabilize. It also discusses selecting the optimal number of clusters K and presents pros and cons of clustering techniques.
Interpolation is a statistical method by which related known values are used to estimate an unknown price or potential yield of a security. Interpolation is a method of estimating an unknown price or yield of a security. This is achieved by using other related known values that are located in sequence with the unknown value.
Link: https://en.wikipedia.org/wiki/Interpolation
The aim of this project is to discover the topics of scientific papers published by researches of DEMS (Department of Economics, Management and Statistic) for the University of Milano-Bicocca.
The document provides an introduction to QQ plots and explains how they can be used to check if collected data is normally distributed. It defines what a quantile is, explaining that a QQ plot, or quantile-quantile plot, plots the quantiles of the collected data against the quantiles of a normal distribution to see if the data follows a normal pattern. The document also includes a brief example QQ plot and explains how to interpret it to assess normality.
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.
Mat189: Cluster Analysis with NBA Sports DataKathleneNgo
The document discusses using cluster analysis techniques like K-Means and spectral clustering on NBA player statistics data. It begins by introducing machine learning concepts like supervised vs. unsupervised learning and definitions of clustering criteria. It then describes preprocessing the 27-dimensional player data into 2 dimensions using linear discriminant analysis (LDA) and principal component analysis (PCA) for visualization. K-Means clustering is applied to the LDA-reduced data, identifying distinct player groups. Spectral clustering will also be applied using PCA for comparison. The goal is to categorize players and determine the best athletes without prior basketball knowledge.
The document provides an overview of dimensionality reduction techniques. It discusses linear dimensionality reduction methods like principal component analysis (PCA) as well as non-linear dimensionality reduction techniques. For non-linear dimensionality reduction, it describes the concept of manifolds and manifold learning. Specific manifold learning algorithms covered include Isomap, locally linear embedding (LLE), and applications of manifold learning.
Data Visualization using different python libraries.pptxHamzaAli998966
This document discusses data visualization using Python libraries like Pandas, NumPy, and Matplotlib. It covers various types of charts that can be created like line charts, bar charts, and histograms to visualize different aspects of stock market data. Descriptive statistics are calculated on the stock data and various visualizations are created to analyze trends in closing prices, moving averages, daily returns, and correlations between stocks. Finally, it discusses predicting future closing stock prices of Apple using an LSTM model.
Design of suitable Magnitude Comparator Architecture for Big Data Analyticsrahulmonikasharma
In today’s digital era increasing use of portable devices forces electronic designer to concentrate on high speed and low power dissipation. As magnitude comparator is very basic arithmetic unit, to cope up with high speed and optimum power for big data, we need suitable magnitude comparator architecture, so this Paper presents different types of magnitude comparator architecture such as serial, parallel and tree structure. Proposed 64 bit magnitude comparator is designed with 1.8v voltage supply using in standard CMOS 180nm Technology using Cadence Virtuoso EDA tool. Simulation of all three architecture have been done using SPECTRE VIRTUOSO ADE tool.
Matrices have various applications in real life. They are used in physics to study electrical circuits, quantum mechanics, and optics. Programmers also use matrices and inverse matrices for coding and encrypting messages. In dimensional work, matrices help project 3D images onto 2D screens to create realistic motions. Matrices are applied in economics to calculate GDP and efficiently measure goods production. They also help organizations like scientists record experiment data. Google search uses matrices in its page rank algorithm to rank search results.
This document discusses matrices and their uses. It defines what a matrix is and provides examples of different types of matrices like row matrices, column matrices, null matrices, identity matrices, diagonal matrices, triangular matrices, and transpose matrices. It then discusses some applications of matrices like in cryptography for encrypting messages, in electrical circuits, quantum mechanics, optics, robotics, automation, economics, and more. Matrices are useful for tasks like plotting graphs, scientific studies, page ranking algorithms, image projection, representing real world data, and calculating gross domestic products.
Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation, and presentation of data.
Some scholars pinpoint the origin of statistics to 1663, with the publication of Natural and Political Observations upon the Bills of Mortality by John Graunt. Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data, hence its stat- etymology. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences.
The document proposes using microaggregation techniques to generate k-anonymous datasets that satisfy t-closeness, addressing limitations of existing approaches using generalization and suppression. It presents three microaggregation-based algorithms to reconcile privacy and utility goals: one merges clusters as needed for t-closeness, while two modify the microaggregation process to directly consider t-closeness. Microaggregation preserves data utility better than generalization by maintaining granularity and numbers' continuous nature, and handles outliers less disruptively. The algorithms are empirically evaluated for generating privacy-preserving datasets.
What is matrix? Matrix in physics. Matrix in computer science. Matrix in encryption. Matrix in others sector. geology surveys,robot movement,scientific experiment.
Applications of matrices in Real\Daily lifeSami Ullah
Matrices are used in a wide variety of applications in real life. They are used in physics for electrical circuits and quantum mechanics. Stochastic matrices are used in page rank algorithms like Google search. Matrices are also used for encryption in computer applications and coding messages. They allow for secure transmission of data online and for banks. Matrices are applied in fields like geology, statistics, science, economics, robotics, and by scientists recording experimental data. They provide a way to represent and analyze real world data.
Matrices are rectangular arrangements of numbers, expressions, or symbols organized into rows and columns. They are used across many fields including physics for electrical circuits and optics calculations, computing for 3D graphics and encryption, geology for seismic surveys and data analysis, animation for 3D modeling and transformations, statistics and economics for presenting real-world data and calculating GDP, and other fields like data storage, robotics, analytical concepts, and software design. Matrices have wide applications in science, technology, engineering and mathematics.
Exploring optimizations for dynamic PageRank algorithm based on GPU : V4Subhajit Sahu
This is my comprehensive viva report version 4.
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Graph is a generic data structure and is a superset of lists, and trees. Binary search on sorted lists can be interpreted as a balanced binary tree search. Database tables can be thought of as indexed lists, and table joins represent relations between columns. This can be modeled as graphs instead. Assignment of registers to variables (by compiler), and assignment of available channels to a radio transmitter and also graph problems. Finding shortest path between two points, and sorting web pages in order of importance are also graphs problems. Neural networks are graphs too. Interaction between messenger molecules in the body, and interaction between people on social media, also modeled as graphs.
The document discusses various types of graphs used in statistics including bar graphs, pie charts, and pictographs. It provides examples and definitions of each type of graph as well as their uses and advantages. It explains that bar graphs can be used to compare data between groups, pie charts show proportional relationships, and pictographs represent data using pictures. The document also provides examples of different variations of pie charts such as polar area, multi-level, and exploded pie charts.
Graph Tea: Simulating Tool for Graph Theory & AlgorithmsIJMTST Journal
Simulation in teaching has recently entered the field of education. It is used at different levels of instruction.
The teacher is trained practically and also imparted theoretical learning. In Computer Science, Graph theory
is the fundamental mathematics required for better understanding Data Structures. To Teach Graph theory &
Algorithms, We introduced Simulation as an innovative teaching methodology. Students can understand in a
better manner by using simulation. Graph Tea is one of such simulation tool for Graph Theory & Algorithms.
In this paper, we simulated Tree Traversal Techniques like Breadth First Search (BFS), Depth First Search
(DFS) and minimal cost spanning tree algorithms like Prims.
one of the areas of discrete mathematics is graph theory. From a pure mathematics viewpoint, graph theory studies the pairwise relationships between objects. Those objects are vertices. Graph theory is frequently applied to analysing relationships between objects. It is a natural extension of graph theory to apply that mathematical tool to the evaluation of forensic evidence. In fact the literature reveals several, limited, forensic applications of graph theory. The current paper describes a more broad based application of graph theory to the problem of evaluation relationships in forensic investigation. The process takes standard graph theory and identifies entities in the investigation as vertices with the connections between the various entities as edges. Those entities can be suspects, victims, computer system, or any entity relevant to the investigation. Regardless of the nature of the entity, all entities are represented as vertices, and the relationship between them is represented as edges connecting the vertices. This allows the mathematical modelling of the events in question and facilitates analysis of the data.
This document provides an overview of unsupervised learning techniques, specifically clustering algorithms. It discusses the differences between supervised and unsupervised learning, the goal of clustering to group similar observations, and provides examples of K-Means and hierarchical clustering. For K-Means clustering, it outlines the basic steps of randomly assigning clusters, calculating centroids, and repeatedly reassigning points until clusters stabilize. It also discusses selecting the optimal number of clusters K and presents pros and cons of clustering techniques.
Interpolation is a statistical method by which related known values are used to estimate an unknown price or potential yield of a security. Interpolation is a method of estimating an unknown price or yield of a security. This is achieved by using other related known values that are located in sequence with the unknown value.
Link: https://en.wikipedia.org/wiki/Interpolation
The aim of this project is to discover the topics of scientific papers published by researches of DEMS (Department of Economics, Management and Statistic) for the University of Milano-Bicocca.
The document provides an introduction to QQ plots and explains how they can be used to check if collected data is normally distributed. It defines what a quantile is, explaining that a QQ plot, or quantile-quantile plot, plots the quantiles of the collected data against the quantiles of a normal distribution to see if the data follows a normal pattern. The document also includes a brief example QQ plot and explains how to interpret it to assess normality.
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.
Mat189: Cluster Analysis with NBA Sports DataKathleneNgo
The document discusses using cluster analysis techniques like K-Means and spectral clustering on NBA player statistics data. It begins by introducing machine learning concepts like supervised vs. unsupervised learning and definitions of clustering criteria. It then describes preprocessing the 27-dimensional player data into 2 dimensions using linear discriminant analysis (LDA) and principal component analysis (PCA) for visualization. K-Means clustering is applied to the LDA-reduced data, identifying distinct player groups. Spectral clustering will also be applied using PCA for comparison. The goal is to categorize players and determine the best athletes without prior basketball knowledge.
The document provides an overview of dimensionality reduction techniques. It discusses linear dimensionality reduction methods like principal component analysis (PCA) as well as non-linear dimensionality reduction techniques. For non-linear dimensionality reduction, it describes the concept of manifolds and manifold learning. Specific manifold learning algorithms covered include Isomap, locally linear embedding (LLE), and applications of manifold learning.
Data Visualization using different python libraries.pptxHamzaAli998966
This document discusses data visualization using Python libraries like Pandas, NumPy, and Matplotlib. It covers various types of charts that can be created like line charts, bar charts, and histograms to visualize different aspects of stock market data. Descriptive statistics are calculated on the stock data and various visualizations are created to analyze trends in closing prices, moving averages, daily returns, and correlations between stocks. Finally, it discusses predicting future closing stock prices of Apple using an LSTM model.
You Don't Have to Be a Data Scientist to Do Data ScienceCarmen Mardiros
This document discusses how data analysts can start doing data science without needing advanced degrees. It argues that data science skills can increase an analyst's confidence, productivity, and ability to add value. While some myths exist that data science requires specific credentials or advanced technical skills, the document advocates learning techniques like resampling, faceted visualization, and feature engineering through online resources to gain conceptual understanding and provide value. Starting with top-down approaches and learning as you apply techniques is recommended over believing data science is only for those with certain backgrounds.
This document discusses different types of graphs and charts, their purposes and guidelines for use. It defines the key difference between graphs and charts, with graphs representing relationships between objects and charts representing data through symbols. Common chart types are described like line charts to show changes over time, bar charts to compare categories, and pie charts to show proportions of a whole. The document provides examples and guidelines for effective graph and chart creation.
The document discusses Spark streaming and machine learning concepts like logistic regression, linear regression, and clustering algorithms. It provides code examples in Scala and Python showing how to perform binary classification on streaming data using Spark MLlib. Links and documentation are referenced for setting up streaming machine learning pipelines to train models on streaming data in real-time and make predictions.
Data Science as a Career and Intro to RAnshik Bansal
This document discusses data science as a career option and provides an overview of the roles of data analyst, data scientist, and data engineer. It notes that data analysts solve problems using existing tools and manage data quality, while data scientists are responsible for undirected research and strategic planning. Data engineers compile and install database systems. The document also outlines the typical salaries for each role and discusses the growing demand for data science skills. It provides recommendations for learning tools and resources to pursue a career in data science.
This document introduces data exploration and reduction techniques in XLMiner, including principal component analysis and cluster analysis. Principal component analysis transforms correlated variables into uncorrelated principal components to reduce data size while maintaining variability. Cluster analysis assigns objects to clusters to group similar objects together and different objects apart. The document demonstrates how to perform k-means clustering and hierarchical clustering in XLMiner.
This document introduces data exploration and reduction techniques in XLMiner, including principal component analysis and cluster analysis. Principal component analysis transforms correlated variables into uncorrelated principal components to reduce data size while maintaining variability. Cluster analysis assigns objects to clusters to group similar objects together and different objects apart. The document demonstrates how to perform k-means clustering and hierarchical clustering in XLMiner.
Graph Algorithms, Sparse Algebra, and the GraphBLAS with Janice McMahonChristopher Conlan
This talk will provide a very brief overview of graph algorithms and their expression using sparse linear algebra, followed by a high-level description of the GraphBLAS library and its usage.
Graphs are among the most important abstract data types in computer science, and the algorithms that operate on them are critical to modern life. Algorithms on graphs are applied in many ways in today's world—from Web rankings to metabolic networks, from finite element meshes to semantic graphs. Graphs have been shown to be powerful tools for modeling these complex problems because of their simplicity and generality. GraphBLAS is an API specification that defines standard building blocks for graph algorithms in the language of linear algebra. Graph algorithms have long taken advantage of the idea that a graph can be represented as a matrix, and graph operations can be performed as linear transformations and other linear algebraic operations on sparse matrices. For example, matrix-vector multiplication can be used to perform a step in a breadth-first search. The GraphBLAS specification (and the various libraries that implement it) provides data structures and functions to compute these linear algebraic operations. In particular, the GraphBLAS specifies sparse matrix objects which map well to graphs where vertices are likely connected to relatively few neighbors (i.e. the degree of a vertex is significantly smaller than the total number of vertices in the graph). The benefits of this approach are reduced algorithmic complexity, ease of implementation, and improved performance.
This slidecast takes an informal approach to image processing using Matlab environment.
Very little math is involved to keep things simple. But the full essence is only felt with the math involved.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
This document discusses various data visualization techniques for exploring and understanding data. It introduces basic visualization techniques like bar charts, histograms, distribution plots, box plots, scatter plots, pair plots, and heatmaps that can be created using Matplotlib and Seaborn libraries. Each technique is explained along with its purpose and how it can provide insights during exploratory data analysis. The goal of descriptive analytics and these visualizations is to help comprehend large datasets through summarization and statistical measures.
Introduction to Datamining Concept and TechniquesSơn Còm Nhom
This document provides an introduction to data mining techniques. It discusses data mining concepts like data preprocessing, analysis, and visualization. For data preprocessing, it describes techniques like similarity measures, down sampling, and dimension reduction. For data analysis, it explains clustering, classification, and regression methods. Specifically, it gives examples of k-means clustering and support vector machine classification. The goal of data mining is to retrieve hidden knowledge and rules from data.
Application of discrete mathematics in ITShahidAbbas52
This document discusses discrete mathematics and its applications. It begins with defining discrete mathematics and providing examples of its different fields like graphs, networks, and logic. It then discusses various real-world applications of discrete mathematics in areas like computers, encryption, Google Maps, and scheduling. Discrete mathematical concepts like graphs, algorithms, and logic are widely used in fields like computer science, engineering, operations research, and social sciences.
Chapter 1 Introduction to Data Structures and Algorithms.pdfAxmedcarb
Data structures provide an efficient way to store and organize data in a computer so that it can be used efficiently. They allow programmers to handle data in an enhanced way, improving software performance. There are linear data structures like arrays and linked lists, and non-linear structures like trees and graphs. Common operations on data structures include insertion, deletion, searching, sorting, and merging. Asymptotic analysis is used to define the time complexity of algorithms in the average, best, and worst cases.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
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
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
2. About Me:-
I am Baijayanti Chakraborty, a Post Graduate student from Great Lakes Institute
of Management. I am doing PG in Business Analytics and Business Intelligence.
You can find me on:
1.LinkedIN: https://www.linkedin.com/in/baijayanti-chakraborty/
2.Twitter: twitter.com/baijayantic
3.Mail: baijayantichakraborty96@gmail.com
4.Github: https://github.com/baijayantichakraborty
5.Kaggel: https://www.kaggle.com/baijayanti94
3. Today’s Spot Of Interest
❖ What is visualization and why do we need it !!!!
❖ Basic Visualizations
❖ Advanced Visualizations
4. What is visualization and why do we need it !!!
Data visualization is an art of how to turn numbers into useful knowledge. We all know that when we see images its easy to
understand than when reading a lot of information.
Let’s consider the below example: Over here is a snip from the IRIS dataset which is already present in R. It’s quite difficult
to comprehend anything from the huge lot of data and hence to make it easy for understanding we will be using visualization
techniques.
Th
8. Selecting the right kindof chart !!!
There are four basic presentation types:
1. Comparison
2. Composition
3. Distribution
4. Relationship
To determine which amongst these types is best suited for your
data at hand we should be able to answer the below questions :-
● How many variables do you want to show in a single
chart?
● How many data points will you display for each variable?
● Will you display values over a period of time, or among
items or groups?
11. Histogram
Histogram is basically a plot that breaks the data into bins (or breaks) and shows frequency distribution of these bins.
12. Bar/Line Chart
● Bar charts are recommended when you want to plot a categorical variable or a combination of continuous and categorical
variable.
● Line Charts are commonly preferred when we are to analyse a trend spread over a time period.
14. Boxplots
Box Plots are used to plot a combination of categorical and continuous variables. This plot is useful for visualizing the spread of the
data and detect outliers. It shows five statistically significant numbers- the minimum, the 25th percentile, the median, the 75th
percentile and the maximum.Example for boxplot creation using the below code :
20. Some advanced packages of visualisation in R are :-
● Lattice Graphs :- Lattice package is essentially an improvement upon the R Graphics package and is used to
visualize multivariate data. Some kinds of visualisations with lattice package are :-
1.Kernal Density Plots
22. ● ggplot2 :- this package is one of the most widely used visualisation packages in R. It enables the users to create
sophisticated visualisations with little code using the Grammar of Graphics.
● Plotly is an R package that creates interactive web-based graphs via the open source JavaScript graphing library
plotly.js. It can easily translate the ‘ggplot2’ graphs to web-based versions also.
23. Adavanced Scatter Plots
Besides the basic version of scatterplots we can also create them using the “ggplot2” library.
The below codes give a taste of the same.
25. HeatMaps
Heat Map uses intensity (density) of colors to display relationship between two or three or many variables
in a two dimensional image. It allows us to explore two dimensions as the axis and the third dimension by
intensity of color.
The colour of the bars in the heat map is
dependent on the cyl parameter of the dataset.
The dataset used here is mtcars. It’s an inbuilt
dataset.
26. HeatMaps contd….
Using the library “plotly”, the heatmaps can be made interactive in nature. The below code gives
an insight as to how we can use plotly.
27. Correlogram
Correlogram is used to test the level of correlation among the variable available in the data
set. The cells of the matrix can be shaded or colored to show the correlation value.
28. Correlogram contd...
It is possible to use “ggplot2” aesthetics on the chart, for instance to color each category. We can use a new library “GGally”
and see how different variations are made to the simple correlogram.
29. Correlogram contd….
Change the type of plot used on each part of the correlogram. This is done with the upper and lower argument.
30. Area Chart
Area chart is used to show continuity across a variable or data set. It is very much same as line chart and is commonly used
for time series plots. Alternatively, it is also used to plot continuous variables and analyze the underlying trends.
31. 3D Plots
● To create a 3D plot using R can be done
with the help of scatterplot3d package.
● scaterplot3d is very simple to use and it
can be easily extended by adding
supplementary points or regression
planes into an already generated
graphics.
33. Quick Information
For quick references you can easily check the cheatsheet side of Rstudio:
https://rstudio.com/resources/cheatsheets/
References :-
1. https://rstudio.com/resources/cheatsheets/
2. https://www.slant.co/topics/2354/~best-data-visualization-tools-for-massive-datasets
3. https://policyviz.com/product/core-principles-of-data-visualization-cheatsheet/
4. https://eazybi.com/blog/data_visualization_and_chart_types/
5. https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
6. https://towardsdatascience.com/a-guide-to-data-visualisation-in-r-for-beginners-ef6d41a34174?#0689
This is like a million dollar question because before we start with any kind of analysis with data we need to know about the insights from the data.These relations among the various variables in the data needs to be understood and what better could it be than by understanding them with visual effects.An outlier is an observation that lies an abnormal distance from other values in a random sample from a population.
For a very proper understanding of datasets we need to know which type of chart should be used when….
1. Used for continuous variables
2.It breaks the data into bins and shows frequency distribution of these bins
3.We can always change the bin size and see the effect it has on visualization.
brewer.pal makes the color palettes from ColorBrewer available as R palettes.
Boxplots are also used to detect the outliers present in the dataset.
Outlier detection and removal is an essential step of successful data exploration.
We can find the median , and also treat the outliers.
By using the ~ sign, we can visualize how the spread (of Sepal Length) is across various categories ( of Species). In the last two graphs we have seen the example of color palettes. A color palette is a group of colors that is used to make the graph more appealing and helping create visual distinctions in the data.
Lattice enables the use of trellis graphs. Trellis graphs exhibit the relationship between variables which are dependent on one or more variables.
The Grammar of Graphics is a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers.The popularity of ggplot2 has increased tremendously in recent years since it makes it possible to create graphs that contain both univariate and multivariate data in a very simple manner.
Advanced visualisations include graphs like heatcharts,geographical maps,3D charts etc.which can be easily made by using visualisation tools like tableau etc.
Darker the color, higher the correlation between variables. Positive correlations are displayed in blue and negative correlations in red color. Color intensity is proportional to the correlation value.
GGally extends ggplot2 by adding several functions to reduce the complexity of combining geoms with transformed data. Some of these functions include a pairwise plot matrix, a scatterplot plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.