This document provides information about different types of graphs, including line graphs and bar graphs. It defines a line graph as a diagram that connects points on an x-y plane to show the relationship between two variables. Bar graphs use vertical or horizontal bars to show comparisons between categories. The document explains the key parts of line graphs and bar graphs, such as the title, labels, scales, points and bars. It also provides steps to construct a line graph using sample data on daily earnings. For bar graphs, it outlines how to create bars of uniform width and height according to a chosen scale. An example bar graph shows the number of children in different activities.
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.
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- Definition of Angles
- Parts of Angles
- Protractor
- Kinds of Angles
- Measuring Angles
The Assignment on the last slide is for them to have a background on the next lesson.
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* Soft copy of the WHOLE ppt slides with effects
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- Definition of Angles
- Parts of Angles
- Protractor
- Kinds of Angles
- Measuring Angles
The Assignment on the last slide is for them to have a background on the next lesson.
In this video Data Graphics has been discussed. How the data can be presented with the help of different line graph, poly graph, bar diagram, histogram and Scatter plot and semi logarithmic plot/graph.
Portion completed:
1.DATA GRAPHICS
2. REPRESENTATION OF DATA
3. line graph,
4. poly graph,
5. bar diagram,
6. histogram
7. Pie diagram
8. Wind rose and star diagram
9. Flow Charts
10. Simple Bar Diagram
11. Line and Bar Graph
12. Multiple Bar Diagram
13. Compound Bar Diagram
14. Pie Diagram
15. Scatter plot
16. Semi-log plot
Chart and graphs in R programming language CHANDAN KUMAR
This slide contains basics of charts and graphs in R programming language. I also focused on practical knowledge so I tried to give maximum example to understand the concepts.
Human Resources and Economic DevelopmentAyesha Arshad
INTRODUCTION TO HUMAN RESOURCES & ECONOMIC DEVELOPMENT
INDICATORS OF HUMAN RESOURCES
IMPORTANCE OF HR DEVELOPMENT
COMPONENTS OF HUMAN RESOURCE DEVELOPMENT
SOCIAL/ NON-ECONOMIC FACTORS OF ECONOMIC GROWTH
What Is Meant by Business Ethics?
Why Is It Important in Research?
Ethical Principles
Ethical Issues
Areas Of Scientific Dishonesty
Ethical Decision Making in Research
Models Of Management Ethics
Why do Managers Behave Ethically?
Conclusion
Presentation on:
Void Agreement, Contract of Agency, Contract of sale of goods, Performance of the contract of sale and Nature of negotiable instrument
Analysis Marketing Mix And STP via:
1. Marketing.
2. Marketing Mix - Meaning And Its Element.
3. Idol Wear Intro.
4. Idol Wear Inc.'s Marketing Mix (4ps/Product, Place, Promotion, Price).
5. Segmentation, Targeting, And Positioning
6. Main Factors That Can Affect The Buying Decisions Of The Target Customers For Idol Wear’s Products.
7. Conclusion.
Recruitment Process of Marketing ExecutiveAyesha Arshad
Marketing Executive Recruitment Process including
1. Job Title
2. Job Description
3. Develop an Ad
4. Develop an Application Form
5. Recruitment Source
6. Screening of Applications (specify Preferences)
7. Select the method of interview
8. Qualification of Panel members
9. Design 10 Questions for Interview
10. Salary Package for the new employees
11. Method of Training For New Employees
12. Job evaluation method
13. Tool use for appraisal
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.
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.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. LINE GRAPH-BAR GRAPH
2
0
1
2
3
4
5
6
Category 1 Category 2 Category 3 Category 4
Chart Title
Series 1 Series 2 Series 3
0
2
4
6
8
10
12
14
Category 1 Category 2 Category 3 Category 4
Chart Title
Series 1 Series 2 Series 3
3. CONTENTS
Graphs
Types of graphs
Line graph
Parts of Line Graph
How to Create a Line Graph?
Example
Bar graph
Parts of Bar Graph
How to Create Bar Graph?
Example
3
4. GRAPHS
A diagram showing the relation
between variable quantities,
typically of two variables, each
measured along one of a pair of
axes at right angles.
4
6. LINE GRAPH
A line graph is a diagram that shows a line
joining several points, or a line that shows the
relationship between the points.
A line graph can be taken as x-y plane, where
there will be an independent variable and a
dependent variable and it specifies how the
two variables are related to each other and
vary with respect to one another.
Mostly, the independent variable is taken on
the x-axis while the dependent variable on the
y-axis 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Category 1 Category 2 Category 3 Category 4
Axis
Title
Axis Title
Chart Title
Series 3
Series 2
Series 1
6
7. PARTS OF LINE GRAPH
Title
The title of the graph tells us what the graph is all about.
Labels
The horizontal label across the bottom and the vertical
label along the side tells us what kinds of data is being
shown.
Scales
The horizontal scale across the bottom and the vertical
scale along the side tell us how much or how many.
Points
The points or dots on the graph represents the x, y
coordinates or ordered pairs.
Lines
The line segments connecting the points give estimated
values between the points
7
8. MAKE A LINE GRAPH
Steps to make a line graph:
1.Use the data from the table to choose an
appropriate scale.
2.Draw and label the scale on the vertical and
horizontal axis.
3.List the each item and locate the points on
the graph.
4.Connect the points with line segments
8
9. EXAMPLE:
Day Mon Tues Wed Thurs Fri
Earnings 300 450 200 400 650
Q. The table shows the daily earnings of a store for five days.
a) Construct a line graph for the frequency table.
b) On which days were the earnings above $ 400.
Solution:
a)
b) The earnings were above $ 400 on Tuesday and Friday.
9
10. BAR GRAPH
A bar graph is a chart that
uses either horizontal or vertical bars
to show comparisons among
categories.
0 2 4 6 8 10 12 14
Category 1
Category 2
Category 3
Category 4
Axis Title
Axis
Title
Chart Title
Series 1 Series 2 Series 3
10
11. PARTS OF BAR GRAPH
Title
The title of the graph tells us what the graph is all
about.
Labels
The horizontal label across the bottom and the
vertical label along the side tells us what kinds of
data is being shown.
Scales
The horizontal scale across the bottom and the
vertical scale along the side tell us how much or
how many.
Bars
Represents the data amount for each group
showing comparison between among categories.
11
12. HOW TO CREATE BAR GRAPH?
Steps in construction of bar graphs:
On a graph, draw two lines perpendicular to each
other, intersecting at 0.
The horizontal line is x-axis and vertical line is y-axis.
Along the horizontal axis, choose the uniform width
of bars and uniform gap between the bars and write
the names of the data items whose values are to be
marked.
Along the vertical axis, choose a suitable scale in
order to determine the heights of the bars for the
given values. (Frequency is taken along y-axis).
Calculate the heights of the bars according to the
scale chosen and draw the bars.
12
13. EXAMPLE:
Let us observe the following data of the bar
graph.
The following data gives the information of the
number of children involved in different
activities.
13
Activities Dance Music Art Cricket Football
No. of
Children
30 40 25 20 53
0
10
20
30
40
50
60
Dance Music Art Cricket Football
No.
of
students
Activities