This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
Data Mining: Concepts and Techniques — Chapter 2 —Salah Amean
the presentation contains the following :
-Data Objects and Attribute Types.
-Basic Statistical Descriptions of Data.
-Data Visualization.
-Measuring Data Similarity and Dissimilarity.
-Summary.
A deep dive in data visualization covering some handful tools like Advance excel, Tableau, Qliksense etc.
You can add more content like discussing Google API, Perception and cognition theory,some more readable formats for data visualization and its framework.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skil...Simplilearn
In this presentation, we will decode the basic differences between data scientist, data analyst and data engineer, based on the roles and responsibilities, skill sets required, salary and the companies hiring them. Although all these three professions belong to the Data Science industry and deal with data, there are some differences that separate them. Every person who is aspiring to be a data professional needs to understand these three career options to select the right one for themselves. Now, let us get started and demystify the difference between these three professions.
We will distinguish these three professions using the parameters mentioned below:
1. Job description
2. Skillset
3. Salary
4. Roles and responsibilities
5. Companies hiring
This Master’s Program provides training in the skills required to become a certified data scientist. You’ll learn the most in-demand technologies such as Data Science on R, SAS, Python, Big Data on Hadoop and implement concepts such as data exploration, regression models, hypothesis testing, Hadoop, and Spark.
Why be a Data Scientist?
Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
Simplilearn's Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures, tools and analytics, and many more. The courseware also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualisation to model building and tuning. These skills will help you prepare for the role of a Data Scientist.
Who should take this course?
The data science role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:
IT professionals
Analytics Managers
Business Analysts
Banking and Finance professionals
Marketing Managers
Supply Chain Network Managers
Those new to the data analytics domain
Students in UG/ PG Analytics Programs
Learn more at https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training
Data visualization is the graphical representation of data to facilitate understanding and analysis.
Visit Here: https://nareshit.com/data-science-online-training/
contact us: online@nareshit.com|+91-8179191999
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
Data Mining: Concepts and Techniques — Chapter 2 —Salah Amean
the presentation contains the following :
-Data Objects and Attribute Types.
-Basic Statistical Descriptions of Data.
-Data Visualization.
-Measuring Data Similarity and Dissimilarity.
-Summary.
A deep dive in data visualization covering some handful tools like Advance excel, Tableau, Qliksense etc.
You can add more content like discussing Google API, Perception and cognition theory,some more readable formats for data visualization and its framework.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skil...Simplilearn
In this presentation, we will decode the basic differences between data scientist, data analyst and data engineer, based on the roles and responsibilities, skill sets required, salary and the companies hiring them. Although all these three professions belong to the Data Science industry and deal with data, there are some differences that separate them. Every person who is aspiring to be a data professional needs to understand these three career options to select the right one for themselves. Now, let us get started and demystify the difference between these three professions.
We will distinguish these three professions using the parameters mentioned below:
1. Job description
2. Skillset
3. Salary
4. Roles and responsibilities
5. Companies hiring
This Master’s Program provides training in the skills required to become a certified data scientist. You’ll learn the most in-demand technologies such as Data Science on R, SAS, Python, Big Data on Hadoop and implement concepts such as data exploration, regression models, hypothesis testing, Hadoop, and Spark.
Why be a Data Scientist?
Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
Simplilearn's Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures, tools and analytics, and many more. The courseware also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualisation to model building and tuning. These skills will help you prepare for the role of a Data Scientist.
Who should take this course?
The data science role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:
IT professionals
Analytics Managers
Business Analysts
Banking and Finance professionals
Marketing Managers
Supply Chain Network Managers
Those new to the data analytics domain
Students in UG/ PG Analytics Programs
Learn more at https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training
Data visualization is the graphical representation of data to facilitate understanding and analysis.
Visit Here: https://nareshit.com/data-science-online-training/
contact us: online@nareshit.com|+91-8179191999
Different Types of Data Visualization in Data Science - NareshITTejaswiniNareshIT
Data visualization is the graphical representation of data to facilitate understanding and analysis. There are various types of data visualization techniques, each suited for different types of data and insights
VISIT: https://nareshit.com/data-science-online-training/
Data Compilation and Tabulation
• Data coding is preferred before going on site for survey. This enables uniformity of data collection among all surveyors and helps in speedy data compilation.
• Survey data is then compiled/ tabulated under various required study parameters/ categories like income category, age groups, hourly water supply, O/D survey, traffic volume, etc.
• These tables are then ready for further graphical representation and analysis.
Graphical presentation of data: pie chart, line chart, bar chart, pyramid graphs, histograms, Lorenz curve, scalogram, sociogram
Data analysis- Quantitative and Qualitative
Land suitability Analysis
Population- Economic Analysis
• Relationship between human capital and city’s economy
• Relationship between humans and resource consumption
Population pattern and its analysis
Density : Density is an objective and quantitative measure referring to a spatial fact that is typically calculated from the ratio of persons or housing units per surface unit.
Residential and non-residential population
Curious about the different types of chart? This presentation demonstrates the variety of charts and their purpose. All these charts have been created using Chartblocks online chart building tool.
Top 8 Different Types Of Charts In Statistics And Their UsesStat Analytica
Are you confused about various Types Of Charts In Statistics? In this blog, you will get to learn about the various Types Of Charts In Statistics in detail.
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.
Visualization idioms helps in making our work more presentable by adding graphs and charts to it. These helps in expressing our views and also helps the viewers to understand the text more easily.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Unlocking the Power of Bloom's Digital Taxonomy in Education
In this presentation, we dive deep into the fascinating world of Bloom's Digital Taxonomy and its significance in modern education.
🌐 The digital age has transformed the way we learn, and it's essential to adapt our teaching methods accordingly. Join us as we explore:
🔍 Traditional Bloom's Taxonomy: We'll start by revisiting the foundational concepts of Bloom's Taxonomy and its hierarchy of cognitive skills.
💡 The Need for Digital Bloom's Taxonomy: Discover the challenges and opportunities posed by digital learning and why updating Bloom's Taxonomy is crucial.
🔄 The Revised Bloom's Digital Taxonomy: Get an in-depth look at the revised model designed specifically for the digital era. We'll break down each cognitive process and its application in the digital context.
📱 Practical Examples: Explore real-world examples of how educators and learners can leverage Bloom's Digital Taxonomy to enhance digital learning experiences.
🚀 Benefits and Impact: Learn about the tangible benefits of implementing this approach, from increased engagement to improved critical thinking skills.
Whether you're an educator, student, or simply curious about the future of education, this video is packed with insights and inspiration to help you embrace the exciting possibilities of Bloom's Digital Taxonomy. Don't forget to like, share, and subscribe for more educational content! 🎓🌟
#Education #BloomsDigitalTaxonomy #DigitalLearning #TeachingInnovation
Artificial Intelligence (AI) in Education.pdfThiyagu K
Artificial intelligence (AI) is rapidly transforming the education industry. AI-powered tools and applications are being used to personalize learning, provide real-time feedback, and automate tasks, freeing up teachers to focus on more creative and strategic work. This presentation explores the many ways that AI is being used in education today, and how it is poised to revolutionize the way we learn and teach.
This presentation is intended for anyone interested in learning more about the role of AI in education. The target audience includes educators, students, parents, policymakers, and anyone else who is curious about how AI is changing the way we learn.
Classroom of the Future: 7 Most Powerful Shifts .pdfThiyagu K
This is the slide presentation highlight the Classroom of the Future: 7 Most Powerful Shifts. Specially this slides explains the shiftfrom Today’s Learning to Tomorrow’s Learning.
Looking to improve your PowerPoint game? Then this presentation is for you! In this PPT, we'll share some valuable PowerPoint presentation tips to help you create engaging and effective presentations.
We'll cover everything from choosing the right fonts and colors to using images and videos to make your slides more dynamic. You'll also learn how to structure your presentation and create a flow that keeps your audience engaged from beginning to end.
Additionally, we'll provide some tips for how to rehearse and practice your presentation, as well as how to effectively deliver it to your audience. Whether you're a student, business professional, or just looking to improve your presentation skills, this video has something for everyone.
So, if you want to take your PowerPoint presentations to the next level, be sure to watch this ppt and start implementing these tips today!
Chat GPT is an advanced language model that has revolutionized the field of education. This cutting-edge technology is transforming the way students learn and interact with the world around them. With Chat GPT, students can now have access to personalized learning experiences, instant feedback, and a wealth of knowledge that was once unimaginable.
This SlideShare presentation will explore the various ways Chat GPT is changing the face of education. From intelligent tutoring systems to virtual assistants, this technology is creating a new era of learning that is more personalized, efficient, and engaging than ever before. We'll look at some real-world examples of how Chat GPT is being used in education today, and how it is transforming the classroom experience for both students and teachers.
The presentation will also delve into some of the potential benefits and challenges of using Chat GPT in education. We'll discuss how this technology can help bridge the learning gap for students with disabilities or learning difficulties, and how it can make education more accessible to students in remote or underserved areas.
Finally, the presentation will provide some practical tips and advice for educators who want to incorporate Chat GPT into their teaching practice. From choosing the right technology to developing effective lesson plans, we'll cover everything you need to know to get started with this game-changing tool.
Whether you're a teacher, a student, or simply interested in the future of education, this SlideShare presentation is for you. Join us as we explore the world of Chat GPT and discover how this technology is transforming education for the better.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2. Data visualization is the
process of creating graphical
representations of information.
This process helps the presenter
communicate data in a way that’s
easy for the viewer to interpret
and draw conclusions.
2
3. Data Visualization Techniques
• Pie Chart
• Bar Chart
• Histogram
• Gantt Chart
• Heat Map
• Box and Whisker Plot
• Waterfall Chart
• Area Chart
• Pictogram Chart
• Timeline
• Highlight Table
• Bullet Graph
• Choropleth Map
• Word Cloud
• Network Diagram
• Correlation Matrices
3
4. Pie Chart
Pie charts are ideal for
illustrating proportions, or part-
to-whole comparisons.
4
5. Bar Chart / Bar Graph
In this type of visualization, one axis of the
chart shows the categories being compared,
and the other, a measured value.
The length of the bar indicates how each
group measures according to the value.
5
6. Histogram
Histograms illustrate the distribution of data over a continuous
interval or defined period.
These visualizations are helpful in identifying where values are
concentrated, as well as where there are gaps or unusual values.
Histograms are especially useful for showing the frequency of a
particular occurrence.
6
7. Gantt Chart
Gantt charts are particularly common in project
management, as they’re useful in illustrating a project
timeline or progression of tasks.
In this type of chart, tasks to be performed are listed
on the vertical axis and time intervals on the horizontal
axis. Horizontal bars in the body of the chart represent
the duration of each activity.
7
8. Heat Map
A heat map is a type of visualization used to show
differences in data through variations in color.
These charts use color to communicate values in a way that
makes it easy for the viewer to quickly identify trends. Having
a clear legend is necessary in order for a user to successfully
read and interpret a heatmap.
8
9. A Box and Whisker Plot
A box and whisker plot, or box plot, provides a visual
summary of data through its quartiles.
First, a box is drawn from the first quartile to the
third of the data set. A line within the box represents
the median. “Whiskers,” or lines, are then drawn
extending from the box to the minimum (lower extreme)
and maximum (upper extreme). Outliers are represented
by individual points that are in-line with the whiskers.
This type of chart is helpful in quickly identifying
whether or not the data is symmetrical or skewed.
9
10. Waterfall Chart
Waterfall chart is a visual representation that
illustrates how a value changes as it’s influenced by
different factors, such as time.
The main goal of this chart is to show the viewer how
a value has grown or declined over a defined period.
For example, waterfall charts are popular for showing
spending or earnings over time.
10
11. Area Chart
An area chart, or area graph, is a variation on a
basic line graph in which the area underneath the
line is shaded to represent the total value of each
data point.
When several data series must be compared on
the same graph, stacked area charts are used.
11
12. Scatter Plot
A scatter plot displays data for two variables as
represented by points plotted against the horizontal and
vertical axis.
This type of data visualization is useful in illustrating
the relationships that exist between variables and can be
used to identify trends or correlations in data.
12
13. Pictogram Chart
Pictogram charts, or pictograph charts, are
particularly useful for presenting simple data in a
more visual and engaging way.
These charts use icons to visualize data, with
each icon representing a different value or category.
13
14. Timeline
Timelines are the most effective way to visualize a
sequence of events in chronological order.
Timelines are used to communicate time-related
information and display historical data.
14
15. Highlight Table
By highlighting cells in the table with color, you can make it
easier for viewers to quickly spot trends and patterns in the
data.
These visualizations are useful for comparing categorical data.
15
16. Bullet Graph
A bullet graph is a variation of a bar graph that can act as
an alternative to dashboard gauges to represent performance
data.
In a bullet graph, the darker horizontal bar in the middle of
the chart represents the actual value, while the vertical line
represents a comparative value, or target.
If the horizontal bar
passes the vertical line, the
target for that metric has
been surpassed.
Additionally, the
segmented colored sections
behind the horizontal bar
represent range scores, such
as “poor,” “fair,” or “good.”
16
17. Choropleth Maps
A choropleth map uses color, shading, and other patterns to
visualize numerical values across geographic regions. These
visualizations use a progression of color (or shading) on a
spectrum to distinguish high values from low.
Choropleth maps allow
viewers to see how a variable
changes from one region to the
next.
17
18. Word Cloud
A word cloud, or tag cloud, is a visual
representation of text data in which the size of the
word is proportional to its frequency.
The more often a specific word appears in a dataset,
the larger it appears in the visualization. In addition
to size, words often appear bolder or follow a specific
color scheme depending on their frequency.
18
19. Network Diagram
Network diagrams are a type of data visualization that
represent relationships between qualitative data points.
These visualizations are composed of nodes and links,
also called edges. Nodes are singular data points that are
connected to other nodes through edges, which show the
relationship between multiple nodes.
19
20. Correlation Matrix
A correlation matrix is a table that shows correlation
coefficients between variables.
Each cell represents the relationship between two variables,
and a color scale is used to communicate whether the
variables are correlated and to what extent.
20
21. Other Data Visualisation
• Bubble clouds
• Cartograms
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close
charts
• Polar areas
https://online.hbs.edu/blog/post/data-visualization-techniques
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Treemaps
• Wedge stack graphs
• Violin plots
21