Data handling Presentation with solved examplesrithikkapoor7
This is a presentation on data handling. It contains information about bar graphs, pictographs, tally charts and more. I hope it helps you students. Please like and comment, it gives tons of inspiration.
Data handling Presentation with solved examplesrithikkapoor7
This is a presentation on data handling. It contains information about bar graphs, pictographs, tally charts and more. I hope it helps you students. Please like and comment, it gives tons of inspiration.
done by : ( ABCD'S &G )
alaa ba-jafar
abrar alshahranii
sahab filfilan
nada alharbi
shahd rajab
Ghadeer suwaimil
I hope that you enjoy and you benefit❤
average a number expressing the central or typical value in a set of data, in particular the mode, median, or (most commonly) the mean, which is calculated by dividing the sum of the values in the set by their number.
done by : ( ABCD'S &G )
alaa ba-jafar
abrar alshahranii
sahab filfilan
nada alharbi
shahd rajab
Ghadeer suwaimil
I hope that you enjoy and you benefit❤
average a number expressing the central or typical value in a set of data, in particular the mode, median, or (most commonly) the mean, which is calculated by dividing the sum of the values in the set by their number.
Presentations focused on materials and documentation that should be saved in order to prepare data file from a survey for secondary use. Some hints were given on how to label items, code missing values, organize folder structure etc. Additionally to clean dataset, documentation on data level, following internationally accepted DDI specification, could be prepared using Colectica for Excel or Nesstar Publisher.
Event was one of Foster Cessda training events for doctoral students.
Related link: https://www.fosteropenscience.eu/project/index.php?option=com_content&view=category&layout=blog&id=23&Itemid=104
Abstract: https://www.fosteropenscience.eu/event/research-data-management-and-open-data-0
This details on specimen collection and handling base on the presentation made by Lusubilo malakibungu,muhsin jabir and mwendesha mathias BMLS3 at Muhimbili University.
This PowerPoint presentation is about organizing and presenting data. It will show you which types of graphs to use when presenting your data. Feel free to save and share it! Please do like my PPT THANKS!!
All pictures and descriptions are from Google (I can't find the links).
note: this presentation doesn't have complete information about organizing and presenting data and only shows important pieces of information about organizing and presenting data and the common graphs that are used in data presentation.
This PowerPoint presentation is about organizing and presenting data. It will show you which types of graphs to use when presenting your data. Feel free to save and share it! Please do like my PPT THANKS!!
All pictures and descriptions are from Google (I can't find the links).
It helps to you understand about statistics and helps in acquiring knowledge and helps to analysing the answers , and in the present generation helps to study about statistics
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. INDEX
What is data handling?
The types of graphs
Bar graph
The axis
Pie chart
Water fall graph
Line graph
Area graph
Pictograph
Tally chart
By Rayna Rocha VI J
2. Data handling is the process of ensuring that research data is stored,
archived or disposed off in a safe and secure manner during and after
the conclusion of a research project. This includes the development of
policies and procedures to manage data handled electronically as
well as through non-electronic means .
3. There are eight main graphs. Their names are
Bar graphs
Pie charts
Tally charts
Area graphs
5. This is a type of chart, which contains labelled horizontal or vertical
bars showing a piece of information and an axis. The numbers along
the side of bar graph compose the axis. This is also called as a
histogram; Bar Graph is useful when there is a numerical comparison.
6. There are two types of axis present in a bar graph. The line which is
horizontal is known as the X Axis. The line which is vertical is known
as the Y Axis.
7. A pie chart is divided into sectors, illustrating numerical proportion. In
a pie chart, the arc length of each sector, is proportional to the
quantity it represents.
8. A waterfall chart is a form of data visualisation that helps in
understanding the cumulative effect of sequentially introduced positive
or negative values. The waterfall chart is also known as a flying bricks
chart or Mario chart due to the apparent suspension of columns (bricks)
in mid-air. Often in finance, it will be referred to as a bridge.
9. A line chart or line graph is a type of chart which displays information
as a series of data points called 'markers' connected by straight line
segments. It is a basic type of chart common in many fields.
10. An area chart or area graph displays graphically quantitive data. It is
based on the line chart. The area between axis and line are commonly
emphasized with colours, textures and hatchings. Commonly one
compares with an area chart two or more quantities.
11. A picture that visually helps us to understand data is called a
pictograph. A pictograph represents data in the form of pictures,
objects or parts of objects.
12. This chart is not used with numbers but instead of lines. We can make
five a time. Like this-