Prelude
PART (A) TYPES OF GRAPHS
Line graphs
Pie charts
Bar graph
Scatter plot
Stem and plot
Histogram
Frequency polygon
Frequency curve
Cumulative frequency or ogives
PART (B) FLOW CHART
PART (C) LOG AND SEMILOG GRAPH
Graphs(Biostatistics and Research Methodology) B.pharmacy(8th sem.)Pranjal Saxena
This slides contains the description about the Graphs(Histograms, Pie-Chart, Cubic Graph, Response surface Plot, Counter surface plot ) mainly Histograms with advantages, disadvantages and examples, Pie-chart with advantages, disadvantages and examples, Cubic Graph with examples, Response surface plot and Counter plot with examples and uses.
Prelude
PART (A) TYPES OF GRAPHS
Line graphs
Pie charts
Bar graph
Scatter plot
Stem and plot
Histogram
Frequency polygon
Frequency curve
Cumulative frequency or ogives
PART (B) FLOW CHART
PART (C) LOG AND SEMILOG GRAPH
Graphs(Biostatistics and Research Methodology) B.pharmacy(8th sem.)Pranjal Saxena
This slides contains the description about the Graphs(Histograms, Pie-Chart, Cubic Graph, Response surface Plot, Counter surface plot ) mainly Histograms with advantages, disadvantages and examples, Pie-chart with advantages, disadvantages and examples, Cubic Graph with examples, Response surface plot and Counter plot with examples and uses.
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
The use of data visualization to tell effectivegentlemoro
Data usually represents unprocessed numbers, pictures or statements; information is typically the result of analyzing or processing the data. Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed and analyzed. These days, data are often summarized, organized, and analyzed with statistical packages or graphics software. Data must be prepared in such a way they are properly recognized by the program being used.No matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
6. Histogram Compound Bar Graph (2) Simple Bar Graph Compound Bar Graph (1)
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9. http://geographyfieldwork.com/DataPresentationPieCharts.htm The pie chart is useful to show the total data divided into proportions. It often has good visual impact but can it is difficult to read the data accurately, particularly if there are several categories. The segments should be drawn from the largest first and the smallest last unless there is an "others" category in which case that should be last regardless of its size. Segments should be shaded in different colours and a suitable key or labels added. The raw data and percentage figures can be added to the key if appropriate. Proportional Pies use the concepts of pie graphs and proportional symbols together. The diameter of each pie is proportional to the total. This method integrates data together and involves a spatial element when plotted on a suitable base map. With some thought "death by pie chart" can be avoided by using this more interesting alternative technique to present data. Notice the need for two keys explaining the size and division of the circles. Proportional Pie Graphs(located on a base map) Proportional Pie Chart Pie Chart
10. Line graph Line graphs show changes over time. All the points are joined up and the axes should normally begin at zero. Rates of change are shown well, although careful thought to the scale should be given. Unsuitable if there are only a few data points.
11. Scatter graph Scatter plots are used to show a relationship between two data sets. The dependent data should be placed on the horizontal (x) axis. The points should not be joined up but a line of best fit showing the general trend is useful where there is an obvious correlation. http://geographyfieldwork.com/DataPresentationScatterGraphs.htm
12. 1. Measures of Central Tendancy When there is a lot of data it can be useful to find an average to summarise it, particularly when comparisons between data sets are desirable. (+) It is very quick to calculate. (+) It is not affected by extreme values. (-) It can only be identified if the individual values are known. (-) The result cannot be used for further mathematical processing The most frequently occurring number in a set of data values. Mode (+) It is not affected by extreme values. (-) It cannot be used for further mathematical processing. The median is best quoted with reference to the interquartile range. The central value in a series of ranked values. If there is an even number of values, the median is the mid-point between the two centrally placed values. Median (+) It takes into consideration all the data. (+) The result can be used for further mathematical processing. (-) It can be misleading if there are a small number of very high or very low values which may distort the mean. The mean is best quoted with reference to the standard deviation. All the data values are added together and then the total is divided by the number of values in the data set. Mean Evaluation Method Measure
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14. . (+) The best way to measure the spread of data around the central value as it involves all the data. (+) Allows useful comparisons of the distribution of values in a data set to be made. (+) Gives results that can be used in further mathematical calculations for further analysis. (-) Reasonably complicated to calculate, although calculators and spreadsheets can help. The standard deviation indicates the degree of clustering of each data value about the mean. It is calculated by measuring the difference (deviation) of each value from the mean; these results are then squared and then added together. This total is divided by the number of values in the data set, and finally the square root is taken from this result. A low SD value indicates that the data is clustered around the mean, whereas a high value indicates that the data is widely spaced with some much higher and lower figures than the mean value. Standard Deviation (+) Although it is more complicated than the range, it is still quite simple to calculate. (+) The result represents the spread of the middle 50% of values and is therefore more representative of the entire data set. (+) Extreme values are not considered and so the result is unlikely to be skewed. (-) Not all the data is considered. The interquartile range is the difference between the 25th and 75th percentiles. The higher the interquartile range, the greater the spread of values around the median. Interquartile Range (+) Quick and easy to calculate. (-) A crude measure as it only considers extreme values and doesn't make any reference to any other values. The difference between the highest and lowest value. Regularly used when describing climate figures. Range Evaluation Method Measure