Variance And Standard Deviation
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The document introduces the concepts of variance and standard deviation. Variance is a statistical measure that quantifies how far observations in a data set spread out from the mean or average value. It is calculated by taking the average of the squared differences from the mean. Standard deviation is the square root of variance and represents how dispersed the values in a data set are from the mean. The document provides examples of computing variance and standard deviation to illustrate these statistical concepts.
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Variance & standard deviation
This document defines variance and standard deviation and provides formulas and examples to calculate them. It states that variance is the average squared deviation from the mean and measures how far data points are from the average. Standard deviation tells how clustered data is around the mean and is the square root of the variance. It provides step-by-step instructions to find variance and standard deviation, including calculating the mean, deviations from the mean, summing the squared deviations, and taking the square root. Worked examples are shown to find the variance and standard deviation of students' test scores and people's heights in a room.
Standard deviation
The document discusses the conceptual definition of standard deviation. Standard deviation represents the root average of the squared deviations of scores from the mean. It explains that to calculate standard deviation, each score's deviation from the mean is squared, those squared deviations are averaged, and then the square root of the average is taken to determine the standard deviation in the original units of measurement.
Standard Deviation and Variance
The document discusses how to calculate standard deviation and variance for both ungrouped and grouped data. It provides step-by-step instructions for finding the mean, deviations from the mean, summing the squared deviations, and using these values to calculate standard deviation and variance through standard formulas. Standard deviation measures how spread out numbers are from the mean, while variance is the square of the standard deviation.
Variance and standard deviation
This document discusses variance and standard deviation. It defines variance as the average squared deviation from the mean of a data set. Standard deviation measures how spread out numbers are from the mean and is calculated by taking the square root of the variance. The document provides step-by-step instructions for calculating both variance and standard deviation, including examples using test score data.
MEAN DEVIATION
The mean deviation is a measure of how spread out values are from the average. It is calculated by:
1) Finding the mean of all values.
2) Calculating the distance between each value and the mean.
3) Taking the average of those distances. This provides the mean deviation, which tells us how far on average values are from the central mean. Examples show calculating mean deviation for both grouped and ungrouped data sets.
Standard deviation (3)
The document discusses how to calculate variance and standard deviation. It provides the formulas and steps to find variance as the average squared deviation from the mean. Standard deviation is defined as the square root of variance and measures how dispersed data are from the mean, with a larger standard deviation indicating more variation. Examples are worked through to demonstrate calculating variance and standard deviation for different data sets.
Statistics-Measures of dispersions
This document discusses various measures of dispersion in statistics including range, mean deviation, variance, and standard deviation. It provides definitions and formulas for calculating each measure along with examples using both ungrouped and grouped frequency distribution data. Box-and-whisker plots are also introduced as a graphical method to display the five number summary of a data set including minimum, quartiles, and maximum values.
Coefficient of variation
The document defines and provides examples for calculating the coefficient of variation, which is a measure used to compare the dispersion of data sets. It gives the formula for coefficient of variation as the standard deviation divided by the mean, expressed as a percentage. Two examples are shown comparing the stability of prices between two cities and production between two manufacturing plants, with the data set having the lower coefficient of variation considered more consistent or stable.
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Variance & standard deviation
This document defines variance and standard deviation and provides formulas and examples to calculate them. It states that variance is the average squared deviation from the mean and measures how far data points are from the average. Standard deviation tells how clustered data is around the mean and is the square root of the variance. It provides step-by-step instructions to find variance and standard deviation, including calculating the mean, deviations from the mean, summing the squared deviations, and taking the square root. Worked examples are shown to find the variance and standard deviation of students' test scores and people's heights in a room.
Standard deviation
The document discusses the conceptual definition of standard deviation. Standard deviation represents the root average of the squared deviations of scores from the mean. It explains that to calculate standard deviation, each score's deviation from the mean is squared, those squared deviations are averaged, and then the square root of the average is taken to determine the standard deviation in the original units of measurement.
Standard Deviation and Variance
The document discusses how to calculate standard deviation and variance for both ungrouped and grouped data. It provides step-by-step instructions for finding the mean, deviations from the mean, summing the squared deviations, and using these values to calculate standard deviation and variance through standard formulas. Standard deviation measures how spread out numbers are from the mean, while variance is the square of the standard deviation.
Variance and standard deviation
This document discusses variance and standard deviation. It defines variance as the average squared deviation from the mean of a data set. Standard deviation measures how spread out numbers are from the mean and is calculated by taking the square root of the variance. The document provides step-by-step instructions for calculating both variance and standard deviation, including examples using test score data.
MEAN DEVIATION
The mean deviation is a measure of how spread out values are from the average. It is calculated by:
1) Finding the mean of all values.
2) Calculating the distance between each value and the mean.
3) Taking the average of those distances. This provides the mean deviation, which tells us how far on average values are from the central mean. Examples show calculating mean deviation for both grouped and ungrouped data sets.
Standard deviation (3)
The document discusses how to calculate variance and standard deviation. It provides the formulas and steps to find variance as the average squared deviation from the mean. Standard deviation is defined as the square root of variance and measures how dispersed data are from the mean, with a larger standard deviation indicating more variation. Examples are worked through to demonstrate calculating variance and standard deviation for different data sets.
Statistics-Measures of dispersions
This document discusses various measures of dispersion in statistics including range, mean deviation, variance, and standard deviation. It provides definitions and formulas for calculating each measure along with examples using both ungrouped and grouped frequency distribution data. Box-and-whisker plots are also introduced as a graphical method to display the five number summary of a data set including minimum, quartiles, and maximum values.
Coefficient of variation
The document defines and provides examples for calculating the coefficient of variation, which is a measure used to compare the dispersion of data sets. It gives the formula for coefficient of variation as the standard deviation divided by the mean, expressed as a percentage. Two examples are shown comparing the stability of prices between two cities and production between two manufacturing plants, with the data set having the lower coefficient of variation considered more consistent or stable.
Normal distribution
The document discusses the normal distribution and its key characteristics. It defines the normal distribution as a bell-shaped curve that is symmetric around the mean. The normal distribution is determined by its mean and standard deviation. Some common applications of the normal distribution include measuring heights, weights, IQ scores, and test results. The standard normal distribution has a mean of 0 and standard deviation of 1. Z-scores indicate how many standard deviations above or below the mean a data point lies.
Arithmetic mean
This document provides information about various measures of central tendency including arithmetic mean, median, mode, and quartiles. It defines each measure and provides formulas and examples for calculating them for different types of data series, including individual, discrete, frequency distribution, and cumulative frequency series. Formulas are given for calculating the arithmetic mean, median, quartiles, and mode of a data set, along with examples worked out step-by-step. Advantages and disadvantages of each measure are also discussed.
Median & mode
This document provides information about various statistical measures of central tendency including the median, mode, and quartiles. It defines each measure and provides examples of how to calculate them from both grouped and ungrouped data sets. Formulas are given for calculating the median, quartiles, deciles, and percentiles for grouped data. The mode is defined as the value that occurs most frequently in a data set, and a formula is provided for calculating it from grouped frequency distributions.
Measures of Variability
This document discusses measures of variability, which refer to how spread out a set of data is. Variability is measured using the standard deviation and variance. The standard deviation measures how far data points are from the mean, while the variance is the average of the squared deviations from the mean. To calculate the standard deviation, you take the square root of the variance. This provides a measure of variability that is on the same scale as the original data. The standard deviation and variance are widely used statistical measures for quantifying the spread of a data set.
Normal Probability Distribution
The document discusses properties of normal distributions and the standard normal distribution. It provides examples of finding probabilities and values associated with normal distributions. The key points are:
- Normal distributions are continuous and bell-shaped. The mean, median and mode are equal.
- The standard normal distribution has a mean of 0 and standard deviation of 1.
- Probabilities under the normal curve can be found using z-scores and the standard normal table.
- Values like z-scores can be determined by finding the corresponding cumulative area in the standard normal table.
Descriptive statistics
This document discusses measures of central tendency and dispersion used in descriptive statistics. It defines the mode, median, and mean as measures of central tendency and how to calculate each for both raw and grouped data. It also discusses properties of each measure. For measures of dispersion, it defines the range, interquartile range, variance and standard deviation, providing formulas and examples of calculating each for a set of test score data. It concludes with notes on rounding rules and homework questions.
MEAN DEVIATION VTU
This document defines mean or average deviation and provides information about calculating it. Mean deviation is the average of the absolute differences between each value in a data set and the mean. It is easier to understand and calculate than other measures of variation. Mean deviation is less affected by extreme observations than other measures. It is frequently used to analyze the distribution of personal wealth by considering both the rich and poor. The document outlines the steps to calculate mean deviation for individual, discrete, and continuous data series.
Measures of dispersion
Measures of Dispersion: Absolute and Relative Measures of Dispersion, their computation & properties.
Normal distribution
The normal distribution is a continuous probability distribution that is symmetric and bell-shaped. It is defined by its mean and standard deviation. Many real-world variables are approximately normally distributed. The standard normal distribution refers to a normal distribution with a mean of 0 and standard deviation of 1. The normal distribution is commonly used to model variables and calculate probabilities related to areas under the normal curve.
Measures of central tendency ppt
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
Biostatistics Measures of dispersion
The document discusses various measures of dispersion for data, including range, mean deviation, and coefficient of mean deviation. Range is defined as the difference between the highest and lowest values in a data set. Mean deviation is the average of the deviations from the mean. Coefficient of mean deviation is a relative measure of dispersion calculated by dividing the mean deviation by the mean. Examples are provided to demonstrate calculating these measures for both ungrouped and grouped data. The measures are used to analyze variation in data sets from fields like medicine, weather, and business.
Normal distribution
The document discusses the normal distribution, which produces a symmetrical bell-shaped curve. It has two key parameters - the mean and standard deviation. According to the empirical rule, about 68% of values in a normal distribution fall within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations. The normal distribution is commonly used to model naturally occurring phenomena that tend to cluster around an average value, such as heights or test scores.
VARIANCE
Topic: Variance
Student Name: Sonia Khan
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
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This document discusses various measures of dispersion used in statistics to quantify how spread out or varied a set of data values are. It defines dispersion as the state of being dispersed or spread out, and explains that measures of dispersion help interpret the variability in data by showing how squeezed or scattered the values are. The document then describes several common measures of absolute and relative dispersion, including range, quartile deviation, mean deviation, standard deviation, and coefficient of variation. For each measure, it provides a definition and formula to calculate it from a raw data set.
Measures of central tendency
Measures of central tendency describe the middle or center of a data set and include the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the number of values. The median is the middle number in a data set arranged in order. The mode is the value that occurs most frequently. These measures are used to understand the typical or common values in a data set.
Percentile
Percentiles are used to indicate the relative position of a score in a group of scores. They divide the scores into 100 equal parts. The 50th percentile, also called the median, is the score below which 50% of scores fall. Percentiles are calculated using a formula that determines the value of the observation corresponding to the percentile rank based on the total number of observations. For example, the 10th percentile is the score below which 10% of scores fall. Percentiles are useful for comparing individual test scores to the overall score distribution.
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Normal Curve
The document discusses the normal curve and standard scores. It defines the normal curve as a continuous probability distribution that is bell-shaped and symmetric. It was developed by Gauss and Pearson. The normal curve can be divided into areas defined by standard deviations from the mean. Standard scores are raw scores converted to other scales, including z-scores, t-scores, and stanines. Z-scores indicate the distance from the mean in standard deviations. T-scores are on a scale of 50 plus or minus 10. Stanines use a nine-point scale with a mean of 5 and standard deviation of 2.
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This document provides information about various statistical measures of central tendency including the median, mode, and quartiles. It defines each measure and provides examples of how to calculate them from both grouped and ungrouped data sets. Formulas are given for calculating the median, quartiles, deciles, and percentiles for grouped data. The mode is defined as the value that occurs most frequently in a data set, and a formula is provided for calculating it from grouped frequency distributions.
Measures of Variability
This document discusses measures of variability, which refer to how spread out a set of data is. Variability is measured using the standard deviation and variance. The standard deviation measures how far data points are from the mean, while the variance is the average of the squared deviations from the mean. To calculate the standard deviation, you take the square root of the variance. This provides a measure of variability that is on the same scale as the original data. The standard deviation and variance are widely used statistical measures for quantifying the spread of a data set.
Normal Probability Distribution
The document discusses properties of normal distributions and the standard normal distribution. It provides examples of finding probabilities and values associated with normal distributions. The key points are:
- Normal distributions are continuous and bell-shaped. The mean, median and mode are equal.
- The standard normal distribution has a mean of 0 and standard deviation of 1.
- Probabilities under the normal curve can be found using z-scores and the standard normal table.
- Values like z-scores can be determined by finding the corresponding cumulative area in the standard normal table.
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This document discusses measures of central tendency and dispersion used in descriptive statistics. It defines the mode, median, and mean as measures of central tendency and how to calculate each for both raw and grouped data. It also discusses properties of each measure. For measures of dispersion, it defines the range, interquartile range, variance and standard deviation, providing formulas and examples of calculating each for a set of test score data. It concludes with notes on rounding rules and homework questions.
MEAN DEVIATION VTU
This document defines mean or average deviation and provides information about calculating it. Mean deviation is the average of the absolute differences between each value in a data set and the mean. It is easier to understand and calculate than other measures of variation. Mean deviation is less affected by extreme observations than other measures. It is frequently used to analyze the distribution of personal wealth by considering both the rich and poor. The document outlines the steps to calculate mean deviation for individual, discrete, and continuous data series.
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Normal distribution
The normal distribution is a continuous probability distribution that is symmetric and bell-shaped. It is defined by its mean and standard deviation. Many real-world variables are approximately normally distributed. The standard normal distribution refers to a normal distribution with a mean of 0 and standard deviation of 1. The normal distribution is commonly used to model variables and calculate probabilities related to areas under the normal curve.
Measures of central tendency ppt
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
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The document discusses various measures of dispersion for data, including range, mean deviation, and coefficient of mean deviation. Range is defined as the difference between the highest and lowest values in a data set. Mean deviation is the average of the deviations from the mean. Coefficient of mean deviation is a relative measure of dispersion calculated by dividing the mean deviation by the mean. Examples are provided to demonstrate calculating these measures for both ungrouped and grouped data. The measures are used to analyze variation in data sets from fields like medicine, weather, and business.
Normal distribution
The document discusses the normal distribution, which produces a symmetrical bell-shaped curve. It has two key parameters - the mean and standard deviation. According to the empirical rule, about 68% of values in a normal distribution fall within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations. The normal distribution is commonly used to model naturally occurring phenomena that tend to cluster around an average value, such as heights or test scores.
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Topic: Variance
Student Name: Sonia Khan
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Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
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This document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is defined as the sum of all values divided by the total number of observations. It can be calculated using direct or shortcut methods for discrete, continuous, and weighted data. The geometric and harmonic means are also defined. The median is the middle value when data is arranged in order. Formulas are provided for calculating the median from discrete and continuous distributions. Examples are included to demonstrate calculating each measure of central tendency.
Measures of Dispersion - Thiyagu
This document discusses various measures of dispersion used in statistics to quantify how spread out or varied a set of data values are. It defines dispersion as the state of being dispersed or spread out, and explains that measures of dispersion help interpret the variability in data by showing how squeezed or scattered the values are. The document then describes several common measures of absolute and relative dispersion, including range, quartile deviation, mean deviation, standard deviation, and coefficient of variation. For each measure, it provides a definition and formula to calculate it from a raw data set.
Measures of central tendency
Measures of central tendency describe the middle or center of a data set and include the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the number of values. The median is the middle number in a data set arranged in order. The mode is the value that occurs most frequently. These measures are used to understand the typical or common values in a data set.
Percentile
Percentiles are used to indicate the relative position of a score in a group of scores. They divide the scores into 100 equal parts. The 50th percentile, also called the median, is the score below which 50% of scores fall. Percentiles are calculated using a formula that determines the value of the observation corresponding to the percentile rank based on the total number of observations. For example, the 10th percentile is the score below which 10% of scores fall. Percentiles are useful for comparing individual test scores to the overall score distribution.
Descriptive statistics
This document discusses descriptive statistics used in research. It defines descriptive statistics as procedures used to organize, interpret, and communicate numeric data. Key aspects covered include frequency distributions, measures of central tendency (mode, median, mean), measures of variability, bivariate descriptive statistics using contingency tables and correlation, and describing risk to facilitate evidence-based decision making. The overall purpose of descriptive statistics is to synthesize and summarize quantitative data for analysis in research.
Normal distribution
The document discusses the normal distribution and its key properties: bell-shaped and symmetrical around the mean, extending from negative to positive infinity with an area under the curve of 1. Approximately 95% and 99.9% of the distribution lies within 2 and 3 standard deviations of the mean, respectively. It also discusses how to calculate probabilities using the standard normal distribution where the mean is 0 and standard deviation is 1, and how to standardize other normal distributions.
Normal Curve
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Operating System Used by Users in day-to-day life.pptx
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Finale of the Year: Apply for Next One!
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Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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Variance And Standard Deviation
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- 8. Lets join the bars... If we expand our selection to more observations or full population, above graph can be more smooth and can have bell shape….. LEARNING Made Simple
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- 21. LearningMadeSimpleTM Learning Made Simple is initiative taken by few people earlier related to the Corporate World whose aim is to provide quality education and training to those who wish to excel in their career path. Learning Made Simple is an association who works on a no profit no loss basis and provide training and development in the era of Financial Planning, Financial Management, AMFI, NCFM, Management Concepts, Soft Skills, Leadership, Personality Development, Goal Setting, Marketing Management, Behavioral Science, NLP and various other segments. You can get in touch with us by sending email on kaushal@thefinancialplanners.in
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