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Fifth Elephant 2014 talk - Crafting Visual Stories with Data
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Amit Kapoor
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Talk at Fifth Elephant 2014 on the principles behind crafting visual stories with data.
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Data visualization has enabled us to compress data and express them visually in many interesting new ways. It is often cited that we are trying to tell stories through them. Is that really the case? How can we ensure that the audience is able to retain, recall and retell our data-driven stories. Using examples and videos learning from different storytelling mediums, I walk through why stories are important and what we can learn about how stories work from these mediums. I then detail out a framework built on the See the data | Show the Visual | Tell the Story | Engage the Audience paradigm to convert the data in to a data-visual-story. This slide deck was used in Bangalore Meetup - Crafting Visual Stories with Data - in March 2014 @ InMobi's Bangalore Office.
Crafting Visual Stories with Data
Crafting Visual Stories with Data
Amit Kapoor
This research explored the visual framing of climate control in The New York Times through three cycles of media history. Although no peer-reviewed study has explored this specific topic, a wealth of prior communication articles on both the visual and textual aspects of climate change and geoengineering in the media was mined in order to discover the frames present. Once the visual frames of climate control (war, fix, people, and impacts) were revealed a content analysis was conducted in order to see which frame elements were most and least frequent considering the images of climate control. When combining all three cycles the frame with the highest overall mean was the fix frame (M=1.7517, SD=1.34128) indicating that it is the most occurring climate control frame per image. The frame with the lowest overall mean was the war frame (M=.5137, SD=1.02544). Frame frequency from cycle to cycle was relatively constant since only the impacts frame had a significant mean difference between cycle one and cycle two (M= .72453, p= .042). This initial analysis did not provide support for Downs issue-attention cycle theory. Although when the frame element frequencies were graphed three spikes were separated by three valleys considering climate control imagery in The New York Times through iv about one and half centuries. This information can go towards making correlations with: events, exposure to certain stimuli, and judging effectiveness of communication strategies over time. The discussion considered whether currently the war and fix frames could be too small in order to produce effective communication with a distrustful public. Also the recent people frame increase correlates with non-acceptance regarding climate change considering Republicans.
The Visual Framing of the Three Cycles of Climate Control in The New York Tim...
The Visual Framing of the Three Cycles of Climate Control in The New York Tim...
Jason Lee Thompson
The task of “data profiling”—assessing the overall content and quality of a data set—is a core aspect of the analytic experience. Traditionally, profiling was a fairly cut-and-dried task: load the raw numbers into a stat package, run some basic descriptive statistics, and report the output in a summary file or perhaps a simple data visualization. However, data volumes can be so large today that traditional tools and methods for computing descriptive statistics become intractable; even with scalable infrastructure like Hadoop, aggressive optimization and statistical approximation techniques must be used. In this talk Sean will cover technical challenges in keeping data profiling agile in the Big Data era. He will discuss both research results and real-world best practices used by analysts in the field, including methods for sampling, summarizing and sketching data, and the pros and cons of using these various approaches. Sean is Trifacta’s Chief Technical Officer. He completed his Ph.D. at Stanford University, where his research focused on user interfaces for database systems. At Stanford, Sean led development of new tools for data transformation and discovery, such as Data Wrangler. He previously worked as a data analyst at Citadel Investment Group.
Sean Kandel - Data profiling: Assessing the overall content and quality of a ...
Sean Kandel - Data profiling: Assessing the overall content and quality of a ...
huguk
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Can big data find the next big thing?
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Vendor-neutral presentation about the common functionality provided by data profiling tools, which can help automate some of the work needed to begin your preliminary data analysis.
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Adventures in Data Profiling
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Recommandé
Data visualization has enabled us to compress data and express them visually in many interesting new ways. It is often cited that we are trying to tell stories through them. Is that really the case? How can we ensure that the audience is able to retain, recall and retell our data-driven stories. Using examples and videos learning from different storytelling mediums, I walk through why stories are important and what we can learn about how stories work from these mediums. I then detail out a framework built on the See the data | Show the Visual | Tell the Story | Engage the Audience paradigm to convert the data in to a data-visual-story. This slide deck was used in Bangalore Meetup - Crafting Visual Stories with Data - in March 2014 @ InMobi's Bangalore Office.
Crafting Visual Stories with Data
Crafting Visual Stories with Data
Amit Kapoor
This research explored the visual framing of climate control in The New York Times through three cycles of media history. Although no peer-reviewed study has explored this specific topic, a wealth of prior communication articles on both the visual and textual aspects of climate change and geoengineering in the media was mined in order to discover the frames present. Once the visual frames of climate control (war, fix, people, and impacts) were revealed a content analysis was conducted in order to see which frame elements were most and least frequent considering the images of climate control. When combining all three cycles the frame with the highest overall mean was the fix frame (M=1.7517, SD=1.34128) indicating that it is the most occurring climate control frame per image. The frame with the lowest overall mean was the war frame (M=.5137, SD=1.02544). Frame frequency from cycle to cycle was relatively constant since only the impacts frame had a significant mean difference between cycle one and cycle two (M= .72453, p= .042). This initial analysis did not provide support for Downs issue-attention cycle theory. Although when the frame element frequencies were graphed three spikes were separated by three valleys considering climate control imagery in The New York Times through iv about one and half centuries. This information can go towards making correlations with: events, exposure to certain stimuli, and judging effectiveness of communication strategies over time. The discussion considered whether currently the war and fix frames could be too small in order to produce effective communication with a distrustful public. Also the recent people frame increase correlates with non-acceptance regarding climate change considering Republicans.
The Visual Framing of the Three Cycles of Climate Control in The New York Tim...
The Visual Framing of the Three Cycles of Climate Control in The New York Tim...
Jason Lee Thompson
The task of “data profiling”—assessing the overall content and quality of a data set—is a core aspect of the analytic experience. Traditionally, profiling was a fairly cut-and-dried task: load the raw numbers into a stat package, run some basic descriptive statistics, and report the output in a summary file or perhaps a simple data visualization. However, data volumes can be so large today that traditional tools and methods for computing descriptive statistics become intractable; even with scalable infrastructure like Hadoop, aggressive optimization and statistical approximation techniques must be used. In this talk Sean will cover technical challenges in keeping data profiling agile in the Big Data era. He will discuss both research results and real-world best practices used by analysts in the field, including methods for sampling, summarizing and sketching data, and the pros and cons of using these various approaches. Sean is Trifacta’s Chief Technical Officer. He completed his Ph.D. at Stanford University, where his research focused on user interfaces for database systems. At Stanford, Sean led development of new tools for data transformation and discovery, such as Data Wrangler. He previously worked as a data analyst at Citadel Investment Group.
Sean Kandel - Data profiling: Assessing the overall content and quality of a ...
Sean Kandel - Data profiling: Assessing the overall content and quality of a ...
huguk
Presentation by Peter Fontaine, CBO's Assistant Director for Budget Analysis, to a Global Network of Parliamentary Budget Offices Community Meeting Sponsored by the World Bank Institute
Telling Visual Stories About Data
Telling Visual Stories About Data
Congressional Budget Office
Seduced by the allure of big data and analytics, many companies are overlooking the true power of integrating deep human understanding to drive transformational action. This short presentation shows how companies that combine leading edge analytics with deep customer understanding will be able to unlock significant competitive advantage.
Can big data find the next big thing?
Can big data find the next big thing?
Clear
Vendor-neutral presentation about the common functionality provided by data profiling tools, which can help automate some of the work needed to begin your preliminary data analysis.
Adventures in Data Profiling
Adventures in Data Profiling
Jim Harris
An analysis of the changing cost competitiveness of the world’s top 25 export economies.
The Shifting Economics of Global Manufacturing
The Shifting Economics of Global Manufacturing
Boston Consulting Group
data profiling
Data profiling-best-practices
Data profiling-best-practices
Blaise Cheuteu
Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies. Data profiling deserves a fresh look for two reasons: First, the area itself is neither established nor defined in any principled way, despite significant research activity on individual parts in the past. Second, current data profiling techniques hardly scale beyond what can only be called small data. Finally, more and more data beyond the traditional relational databases are being created and beg to be profiled. The talk proposes new research directions and challenges, including interactive and incremental profiling and profiling heterogeneous and non-relational data. Speaker: Felix Naumann studied mathematics, economy, and computer sciences at the University of Technology in Berlin. After receiving his diploma (MA) in 1997 he joined the graduate school "Distributed Information Systems" at Humboldt University of Berlin. He completed his PhD thesis on "Quality-driven Query Answering" in 2000. In 2001 and 2002 he worked at the IBM Almaden Research Center on topics around data integration. From 2003 - 2006 he was assistant professor for information integration at the Humboldt-University of Berlin. Since then he holds the chair for information systems at the Hasso Plattner Institute at the University of Potsdam in Germany.
Big Data Profiling
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eXascale Infolab
BCG's 2015 analysis of the changing cost competitiveness of the world’s top 25 export economies.
The Shifting Economics of Global Manufacturing
The Shifting Economics of Global Manufacturing
Boston Consulting Group
You are a designer, or a coder, or a manager. Maybe you are even a unicorn. But you are not a data scientist. Still, you want to get more out of the mountain of data you have about your site or app to create a better user experience. No problem. Learn a process of data thinking that will help you to analyze, visualize, and really use data about your website or app without all the bothersome math and python programming.
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Be Data Informed Without Being a Data Scientist
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I thought using infographics would be a great way for me to add something different to my blog, but I didn't know how to get started. I saw a post about using PowerPoint and gave it a shot. It took less than 30 minutes.
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How I Created Easy Infographics Using MS PowerPoint
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Infographics shapes and flat style flowchart diagrams. Ideas how to change text slides into strongly visual and simple infographics slide. Get inspired by those examples of company history timelines, flow arrows steps illustrated by icons.
Infographics Shapes TimeLines PPT Flow Diagrams - infodiagram part2
Infographics Shapes TimeLines PPT Flow Diagrams - infodiagram part2
Peter Zvirinsky
People consume information on the web differently than traditional media. In an era of information overload we’ve become “grazers” of content, skimming digital channels for nuggets of information. More than ever, it’s important for individuals and organizations to be able to present ideas in a manner that can be quickly consumed, understood and remembered. The Power of Infographics is a presentation that digs into visual thinking, how organizations can learn to present their ideas visually and how infographics can be used to help achieve some common business objectives.
The Power of Infographics
The Power of Infographics
Mark Smiciklas
Infographics shapes and diagrams - graphical inspirations if you want to present a text bullet point slide in an attractive visual way. Examples of infographics diagrams shapes for linear lists, e.g. presentation agenda or table of content, shapes for ordered lists (numbered items) and central item with subitems (by puzzle pieces, circles, pentagram). Diagrams are in modern UI flat graphical style, editable in PowerPoint.
Infographics Text Lists Powerpoint diagrams
Infographics Text Lists Powerpoint diagrams
Peter Zvirinsky
What is Infographics? There are lot of phrase around the term Information graphics like "Infographics." "Data visualization." "Information design. & “Communication Design" We're talking about any graphic that displays and explains information, whether that be data or words. When we use the term "data visualization," we're using it as a general term used to describe data presented in a visual way. Why Infographics? Infographics are important because they change the way people find and experience stories especially now, when more and more infographics are being used to augment editorial content on the web. Infographics create a new way of seeing the world of data, and they help communicate complex ideas in a clear and beautiful way.
Its all about Infographics
Its all about Infographics
Aditya Krishna
Infographics of key data values and KPIs - inspirative of creative slide design templates. Flat style diagrams and geometric shapes. Inspiration how to present a text formal data in an unique visual way. Graphics is in modern metro UI graphical style, easily redoable and editable in PowerPoint.
Infographics Key Data KPI presentation slides
Infographics Key Data KPI presentation slides
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BCG Matrix of Nestle
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Creating better models is a critical component to building a good data science product. It is relatively easy to build a first-cut machine-learning model, but what does it take to build a reasonably good or state-of-the-art model? Ensemble models—which help exploit the power of computing in searching the solution space. Ensemble methods aren’t new. They form the basis for some extremely powerful machine learning algorithms like random forests and gradient boosting machines. The key point about ensemble is that consensus from diverse models are more reliable than a single source. Amit and Bargava discusses various strategies to build ensemble models, demonstrating how to combine model outputs from various base models (logistic regression, support vector machines, decision trees, etc.) to create a stronger, better model output. Using an example they covers bagging, boosting, voting and stacking to explore where ensemble models can consistently produce better results when compared against the best-performing single models.
The Power of Ensembles in Machine Learning
The Power of Ensembles in Machine Learning
Amit Kapoor
Data science is a process of abstraction. In order to explain or to predict a real phenomena, the process starts with acquiring and refining the data. It then moves between the three layers of abstraction: transformations (data abstraction), visualizations (visual abstraction), and modeling (symbolic abstraction). All three layers of abstraction together build a truer (or closer) representation of the real phenomena. Data visualization (data-vis) helps us to understand the portrait and the shape of the data. The science of data-vis for exploratory data analysis is well developed for both static graphics (scatter-plot matrices, glyph-based approaches, geometric transforms like parallel coordinates) and interactive graphics (layering, brushing and linking, projections and tours). (For more information, see Amit Kapoor’s Strata + Hadoop World Singapore talk, Visualizing Multidimensional Data.) Though visualization is used in data science to understand the shape of the data, it’s not widely used for statistical models, which are evaluated based on numerical summaries. Amit Kapoor demonstrates extending visualization to the statistical model (model-vis), which aids in understanding the shape of the model, the impact of parameters and input data on the model, the fit of the model, and where it can be improved. Model visualization can help us to understand the shape of the model and compare it to the shape of the data. It allows us to see the fit of the model and understand where the fit can be improved. It also allows us to better understand the parameters in the model and how the model changes when the parameters change as well as how the parameters changes when the input data changes. The science and tools for model-vis are still very underdeveloped. Amit looks at practical examples of doing model-vis in regression (linear, lasso), classification (logistic, trees, LDA), and clustering (hierarchical) problems that can help us better understand the model. This includes exploring model-vis approaches that: Visualize the model in data space as opposed to data in model space - Visualize the entire space of models - Visualize the same model with varying tuning parameters - Visualize the same model with different input datasets - Visualize the process of model fitting as opposed to final result Integrating these approaches for model-vis as a part of model evaluation strengthens a data scientist’s understanding of the model and leads to better model building, complementing data-vis for fitting better models as well as communicating the insight from the data science process.
Model Visualisation
Model Visualisation
Amit Kapoor
The ever increasing computational capacity has enabled us to acquire, process and analyze larger data-sets and information. However, the human memory and attention required to use this data is more limited and has remained relatively constant. Data visualization can enable us to compress data and encode it visually in ways that allows us to aid perceptual and cognitive understanding. However, data visualisation alone is not enough and often we need to try to tell stories through data. Storytelling with data can enable us to move from analysis to synthesis, from numbers to visuals, and from an argument to a story. Operating at this intersection of data, visual and story can help persuade not only through logos (logic) but also through pathos (empathy) and ethos (credibility). In trying to tell compelling data stories, we can empower our selves to engage, communicate and persuade a large and diverse audience. In this talk, I discuss ‘why’ stories work and what we can learn about the art of storytelling from other mediums like oral storytelling, written stories, pictures, comics and movies. I will summarise basic principles that can help us in our crafting journey, as we take the data through the layers of abstraction. The focus would be on unpacking the seven dimensions of creating an engaging data story - Abstraction (data patterns), Representation (visual encoding), Framing & Transition (perspective, focus), Messaging (verbal, text annotation), Flow (arrangement) and Interactivity. Further, creating data stories is a cross disciplinary activity that requires us to operate at the intersection of a visual designer, data scientist and storyteller. It is both a science and an art. So how does one realistically learn these multitude of skills needed to get good at it. I will also discuss ideas about the possible path that practitioners could adopt to learn this craft through sustained practice. ## About the Speaker Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. He uses storytelling and data visualization as tools for improving communication, persuasion and leadership. He conducts workshops and trainings for corporates, non-profits, colleges, and individuals at narrativeVIZ Consulting. He also teaches storytelling with data as invited guest faculty in academia, both in management context at IIM Bangalore and IIM Ahmedabad and in design context at NID, Bangalore. His background is in strategy consulting in using data-driven stories to drive change across organizations and businesses. He has 15 years of management consulting experience, first with AT Kearney in India, then with Booz & Company in Europe and more recently with startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at amitkaps.com and tweet him at @amitkaps
Storytelling with Data - Approach | Skills
Storytelling with Data - Approach | Skills
Amit Kapoor
Visualising is essential for data science process because it allows as to look at the portrait of our data and develop new hypotheses about our problem. However, visualisation does not scale very well as we are limited by the number of pixels in the our screen (at least for static graphics). This deck talks about the approach - Bin - Summarize - Smooth approach to visualise big data which has been developed by Hadley Wickham and then implemented in an R package in Bigvis.
Visualising Big Data
Visualising Big Data
Amit Kapoor
Data visualisation is a cross disciplinary activity that requires us to operate at the intersection of a visual designer, data scientist and storyteller. It is both a science and an art. So how does one realistically learn these multitude of skills needed to get good at the craft of data visualisation. In this slide deck, I discuss ideas about the possible path that a beginner could adopt to learn this craft through sustained practice.
Learning the Craft of Data Visualisation
Learning the Craft of Data Visualisation
Amit Kapoor
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. This talk was given at the Strata + Hadoop World Conference @ Singapore 2015 and at Fifth Elephant conference @ Bangalore, 2015
Visualising Multi Dimensional Data
Visualising Multi Dimensional Data
Amit Kapoor
A guide to help you navigate the tool landscape for learning and making data visualisation.
Tools & Resources for Data Visualisation
Tools & Resources for Data Visualisation
Amit Kapoor
Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data. I take your through a short tour of the science and the art of visualization and storytelling. Then give you an introduction through examples and exemplar on the four different layers in a data-story: See - Show - Tell - Engage. Used in the session on Business Analytics and Intelligence at IIM Bangalore in July 2014.
Storytelling with Data - See | Show | Tell | Engage
Storytelling with Data - See | Show | Tell | Engage
Amit Kapoor
Contenu connexe
En vedette
Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies. Data profiling deserves a fresh look for two reasons: First, the area itself is neither established nor defined in any principled way, despite significant research activity on individual parts in the past. Second, current data profiling techniques hardly scale beyond what can only be called small data. Finally, more and more data beyond the traditional relational databases are being created and beg to be profiled. The talk proposes new research directions and challenges, including interactive and incremental profiling and profiling heterogeneous and non-relational data. Speaker: Felix Naumann studied mathematics, economy, and computer sciences at the University of Technology in Berlin. After receiving his diploma (MA) in 1997 he joined the graduate school "Distributed Information Systems" at Humboldt University of Berlin. He completed his PhD thesis on "Quality-driven Query Answering" in 2000. In 2001 and 2002 he worked at the IBM Almaden Research Center on topics around data integration. From 2003 - 2006 he was assistant professor for information integration at the Humboldt-University of Berlin. Since then he holds the chair for information systems at the Hasso Plattner Institute at the University of Potsdam in Germany.
Big Data Profiling
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eXascale Infolab
BCG's 2015 analysis of the changing cost competitiveness of the world’s top 25 export economies.
The Shifting Economics of Global Manufacturing
The Shifting Economics of Global Manufacturing
Boston Consulting Group
You are a designer, or a coder, or a manager. Maybe you are even a unicorn. But you are not a data scientist. Still, you want to get more out of the mountain of data you have about your site or app to create a better user experience. No problem. Learn a process of data thinking that will help you to analyze, visualize, and really use data about your website or app without all the bothersome math and python programming.
Be Data Informed Without Being a Data Scientist
Be Data Informed Without Being a Data Scientist
Pamela Pavliscak
Finance for non financial managers ppt by paramesh a
Finance for non financial managers ppt by paramesh a
Alisetti Paramesh. ACMA,CGMA
I thought using infographics would be a great way for me to add something different to my blog, but I didn't know how to get started. I saw a post about using PowerPoint and gave it a shot. It took less than 30 minutes.
How I Created Easy Infographics Using MS PowerPoint
How I Created Easy Infographics Using MS PowerPoint
Kimberly Gauthier
Infographics shapes and flat style flowchart diagrams. Ideas how to change text slides into strongly visual and simple infographics slide. Get inspired by those examples of company history timelines, flow arrows steps illustrated by icons.
Infographics Shapes TimeLines PPT Flow Diagrams - infodiagram part2
Infographics Shapes TimeLines PPT Flow Diagrams - infodiagram part2
Peter Zvirinsky
People consume information on the web differently than traditional media. In an era of information overload we’ve become “grazers” of content, skimming digital channels for nuggets of information. More than ever, it’s important for individuals and organizations to be able to present ideas in a manner that can be quickly consumed, understood and remembered. The Power of Infographics is a presentation that digs into visual thinking, how organizations can learn to present their ideas visually and how infographics can be used to help achieve some common business objectives.
The Power of Infographics
The Power of Infographics
Mark Smiciklas
Infographics shapes and diagrams - graphical inspirations if you want to present a text bullet point slide in an attractive visual way. Examples of infographics diagrams shapes for linear lists, e.g. presentation agenda or table of content, shapes for ordered lists (numbered items) and central item with subitems (by puzzle pieces, circles, pentagram). Diagrams are in modern UI flat graphical style, editable in PowerPoint.
Infographics Text Lists Powerpoint diagrams
Infographics Text Lists Powerpoint diagrams
Peter Zvirinsky
What is Infographics? There are lot of phrase around the term Information graphics like "Infographics." "Data visualization." "Information design. & “Communication Design" We're talking about any graphic that displays and explains information, whether that be data or words. When we use the term "data visualization," we're using it as a general term used to describe data presented in a visual way. Why Infographics? Infographics are important because they change the way people find and experience stories especially now, when more and more infographics are being used to augment editorial content on the web. Infographics create a new way of seeing the world of data, and they help communicate complex ideas in a clear and beautiful way.
Its all about Infographics
Its all about Infographics
Aditya Krishna
Infographics of key data values and KPIs - inspirative of creative slide design templates. Flat style diagrams and geometric shapes. Inspiration how to present a text formal data in an unique visual way. Graphics is in modern metro UI graphical style, easily redoable and editable in PowerPoint.
Infographics Key Data KPI presentation slides
Infographics Key Data KPI presentation slides
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BCG Matrix of Nestle
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Data quality and data profiling
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En vedette
(12)
Big Data Profiling
Big Data Profiling
The Shifting Economics of Global Manufacturing
The Shifting Economics of Global Manufacturing
Be Data Informed Without Being a Data Scientist
Be Data Informed Without Being a Data Scientist
Finance for non financial managers ppt by paramesh a
Finance for non financial managers ppt by paramesh a
How I Created Easy Infographics Using MS PowerPoint
How I Created Easy Infographics Using MS PowerPoint
Infographics Shapes TimeLines PPT Flow Diagrams - infodiagram part2
Infographics Shapes TimeLines PPT Flow Diagrams - infodiagram part2
The Power of Infographics
The Power of Infographics
Infographics Text Lists Powerpoint diagrams
Infographics Text Lists Powerpoint diagrams
Its all about Infographics
Its all about Infographics
Infographics Key Data KPI presentation slides
Infographics Key Data KPI presentation slides
BCG Matrix of Nestle
BCG Matrix of Nestle
Data quality and data profiling
Data quality and data profiling
Plus de Amit Kapoor
My personal usage of Python Visualisation Libraries for Data Science
Python Visualisation for Data Science
Python Visualisation for Data Science
Amit Kapoor
How to take your first steps in doing text generation with Deep Learning using an example case.
Deep Learning for NLP
Deep Learning for NLP
Amit Kapoor
Creating better models is a critical component to building a good data science product. It is relatively easy to build a first-cut machine-learning model, but what does it take to build a reasonably good or state-of-the-art model? Ensemble models—which help exploit the power of computing in searching the solution space. Ensemble methods aren’t new. They form the basis for some extremely powerful machine learning algorithms like random forests and gradient boosting machines. The key point about ensemble is that consensus from diverse models are more reliable than a single source. Amit and Bargava discusses various strategies to build ensemble models, demonstrating how to combine model outputs from various base models (logistic regression, support vector machines, decision trees, etc.) to create a stronger, better model output. Using an example they covers bagging, boosting, voting and stacking to explore where ensemble models can consistently produce better results when compared against the best-performing single models.
The Power of Ensembles in Machine Learning
The Power of Ensembles in Machine Learning
Amit Kapoor
Data science is a process of abstraction. In order to explain or to predict a real phenomena, the process starts with acquiring and refining the data. It then moves between the three layers of abstraction: transformations (data abstraction), visualizations (visual abstraction), and modeling (symbolic abstraction). All three layers of abstraction together build a truer (or closer) representation of the real phenomena. Data visualization (data-vis) helps us to understand the portrait and the shape of the data. The science of data-vis for exploratory data analysis is well developed for both static graphics (scatter-plot matrices, glyph-based approaches, geometric transforms like parallel coordinates) and interactive graphics (layering, brushing and linking, projections and tours). (For more information, see Amit Kapoor’s Strata + Hadoop World Singapore talk, Visualizing Multidimensional Data.) Though visualization is used in data science to understand the shape of the data, it’s not widely used for statistical models, which are evaluated based on numerical summaries. Amit Kapoor demonstrates extending visualization to the statistical model (model-vis), which aids in understanding the shape of the model, the impact of parameters and input data on the model, the fit of the model, and where it can be improved. Model visualization can help us to understand the shape of the model and compare it to the shape of the data. It allows us to see the fit of the model and understand where the fit can be improved. It also allows us to better understand the parameters in the model and how the model changes when the parameters change as well as how the parameters changes when the input data changes. The science and tools for model-vis are still very underdeveloped. Amit looks at practical examples of doing model-vis in regression (linear, lasso), classification (logistic, trees, LDA), and clustering (hierarchical) problems that can help us better understand the model. This includes exploring model-vis approaches that: Visualize the model in data space as opposed to data in model space - Visualize the entire space of models - Visualize the same model with varying tuning parameters - Visualize the same model with different input datasets - Visualize the process of model fitting as opposed to final result Integrating these approaches for model-vis as a part of model evaluation strengthens a data scientist’s understanding of the model and leads to better model building, complementing data-vis for fitting better models as well as communicating the insight from the data science process.
Model Visualisation
Model Visualisation
Amit Kapoor
The ever increasing computational capacity has enabled us to acquire, process and analyze larger data-sets and information. However, the human memory and attention required to use this data is more limited and has remained relatively constant. Data visualization can enable us to compress data and encode it visually in ways that allows us to aid perceptual and cognitive understanding. However, data visualisation alone is not enough and often we need to try to tell stories through data. Storytelling with data can enable us to move from analysis to synthesis, from numbers to visuals, and from an argument to a story. Operating at this intersection of data, visual and story can help persuade not only through logos (logic) but also through pathos (empathy) and ethos (credibility). In trying to tell compelling data stories, we can empower our selves to engage, communicate and persuade a large and diverse audience. In this talk, I discuss ‘why’ stories work and what we can learn about the art of storytelling from other mediums like oral storytelling, written stories, pictures, comics and movies. I will summarise basic principles that can help us in our crafting journey, as we take the data through the layers of abstraction. The focus would be on unpacking the seven dimensions of creating an engaging data story - Abstraction (data patterns), Representation (visual encoding), Framing & Transition (perspective, focus), Messaging (verbal, text annotation), Flow (arrangement) and Interactivity. Further, creating data stories is a cross disciplinary activity that requires us to operate at the intersection of a visual designer, data scientist and storyteller. It is both a science and an art. So how does one realistically learn these multitude of skills needed to get good at it. I will also discuss ideas about the possible path that practitioners could adopt to learn this craft through sustained practice. ## About the Speaker Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. He uses storytelling and data visualization as tools for improving communication, persuasion and leadership. He conducts workshops and trainings for corporates, non-profits, colleges, and individuals at narrativeVIZ Consulting. He also teaches storytelling with data as invited guest faculty in academia, both in management context at IIM Bangalore and IIM Ahmedabad and in design context at NID, Bangalore. His background is in strategy consulting in using data-driven stories to drive change across organizations and businesses. He has 15 years of management consulting experience, first with AT Kearney in India, then with Booz & Company in Europe and more recently with startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at amitkaps.com and tweet him at @amitkaps
Storytelling with Data - Approach | Skills
Storytelling with Data - Approach | Skills
Amit Kapoor
Visualising is essential for data science process because it allows as to look at the portrait of our data and develop new hypotheses about our problem. However, visualisation does not scale very well as we are limited by the number of pixels in the our screen (at least for static graphics). This deck talks about the approach - Bin - Summarize - Smooth approach to visualise big data which has been developed by Hadley Wickham and then implemented in an R package in Bigvis.
Visualising Big Data
Visualising Big Data
Amit Kapoor
Data visualisation is a cross disciplinary activity that requires us to operate at the intersection of a visual designer, data scientist and storyteller. It is both a science and an art. So how does one realistically learn these multitude of skills needed to get good at the craft of data visualisation. In this slide deck, I discuss ideas about the possible path that a beginner could adopt to learn this craft through sustained practice.
Learning the Craft of Data Visualisation
Learning the Craft of Data Visualisation
Amit Kapoor
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. This talk was given at the Strata + Hadoop World Conference @ Singapore 2015 and at Fifth Elephant conference @ Bangalore, 2015
Visualising Multi Dimensional Data
Visualising Multi Dimensional Data
Amit Kapoor
A guide to help you navigate the tool landscape for learning and making data visualisation.
Tools & Resources for Data Visualisation
Tools & Resources for Data Visualisation
Amit Kapoor
Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data. I take your through a short tour of the science and the art of visualization and storytelling. Then give you an introduction through examples and exemplar on the four different layers in a data-story: See - Show - Tell - Engage. Used in the session on Business Analytics and Intelligence at IIM Bangalore in July 2014.
Storytelling with Data - See | Show | Tell | Engage
Storytelling with Data - See | Show | Tell | Engage
Amit Kapoor
A short session I conducted at IIM Bangalore on sharing my experience on the what, how and why of business process improvement in strategy and supply chain. Credit for the concepts (and charts) go to my time spent at Booz & Company in Europe doing most of this work.
Business Process Improvement - A Strategic and Supply Chain Perspective
Business Process Improvement - A Strategic and Supply Chain Perspective
Amit Kapoor
Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data. Online journalism has already started to show the path but we need to develop the science of data-story to get them widely adopted in business. We used a case study based approach to map the four different layers in a data-story: See - Show - Tell - Engage. We developed a framework of key dimensions within each of these layers and then rigorously analyzed 25 carefully selected case-studies to see which elements were being employed in the data-story. We focused not only on the dimensions of data abstraction and visual representation, framing and transition but also on the dimensions of story structure, point-of-view, relatability and engagement through emotions, takeaways and interaction. Our framework indicates generic strategies that can be used for effective data-stories using exploratory and explanatory visualization in personal and presentation context.
What makes a data-story work?
What makes a data-story work?
Amit Kapoor
What is Strategy? Strategy is a very young concept. Lets explore a little more about strategy and then go down the journey of understanding how to think like a strategist.
What is Strategy - Thinking like a Strategist
What is Strategy - Thinking like a Strategist
Amit Kapoor
We explore the use of Story Spine approach to bring together the traditional power of oral storytelling with the emerging strength of data visualisations
Telling Stories with Data - Using Story Spine
Telling Stories with Data - Using Story Spine
Amit Kapoor
A session on structuring your own story and using storytelling in modern corporate settings. @Azim Premji University Sept 2012
Story Structure and Modern Storytelling
Story Structure and Modern Storytelling
Amit Kapoor
Presentation on the business challenges of using Big Data in Retail with a bit of storytelling on why this is not new! @The Fifth Elephant conference in Bangalore on 28 July 2012 See the notes for more talking points.
Targeting the Moment of Truth - Using Big Data in Retail
Targeting the Moment of Truth - Using Big Data in Retail
Amit Kapoor
Storytelling - Gutenberg
Storytelling - Gutenberg
Amit Kapoor
This was a talk I gave in IIM Bangalore in March 2012 to Business Analytics professionals on the analytics of consulting. It covers the hypothesis driven approach to problem solving that is the core of solving problems and walks through the entire lifecycle of a management consulting assignment.
Analytics in Consulting
Analytics in Consulting
Amit Kapoor
A perspective talk on Retail Pricing I have given at Indian Institute of Management - Bangalore in 2011 and 2012
Retail Pricing Perspective
Retail Pricing Perspective
Amit Kapoor
Plus de Amit Kapoor
(19)
Python Visualisation for Data Science
Python Visualisation for Data Science
Deep Learning for NLP
Deep Learning for NLP
The Power of Ensembles in Machine Learning
The Power of Ensembles in Machine Learning
Model Visualisation
Model Visualisation
Storytelling with Data - Approach | Skills
Storytelling with Data - Approach | Skills
Visualising Big Data
Visualising Big Data
Learning the Craft of Data Visualisation
Learning the Craft of Data Visualisation
Visualising Multi Dimensional Data
Visualising Multi Dimensional Data
Tools & Resources for Data Visualisation
Tools & Resources for Data Visualisation
Storytelling with Data - See | Show | Tell | Engage
Storytelling with Data - See | Show | Tell | Engage
Business Process Improvement - A Strategic and Supply Chain Perspective
Business Process Improvement - A Strategic and Supply Chain Perspective
What makes a data-story work?
What makes a data-story work?
What is Strategy - Thinking like a Strategist
What is Strategy - Thinking like a Strategist
Telling Stories with Data - Using Story Spine
Telling Stories with Data - Using Story Spine
Story Structure and Modern Storytelling
Story Structure and Modern Storytelling
Targeting the Moment of Truth - Using Big Data in Retail
Targeting the Moment of Truth - Using Big Data in Retail
Storytelling - Gutenberg
Storytelling - Gutenberg
Analytics in Consulting
Analytics in Consulting
Retail Pricing Perspective
Retail Pricing Perspective
Dernier
Federal Constitution of the Swiss Confederation
SR-101-01012024-EN.docx Federal Constitution of the Swiss Confederation
SR-101-01012024-EN.docx Federal Constitution of the Swiss Confederation
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ranjankumarbehera14
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怎样办理圣路易斯大学毕业证(SLU毕业证书)成绩单学校原版复制
怎样办理圣路易斯大学毕业证(SLU毕业证书)成绩单学校原版复制
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怎样办理伦敦大学城市学院毕业证(CITY毕业证书)成绩单学校原版复制
怎样办理伦敦大学城市学院毕业证(CITY毕业证书)成绩单学校原版复制
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Sequential and reinforcement learning for demand side management by Margaux B...
Sequential and reinforcement learning for demand side management by Margaux B...
Paris Women in Machine Learning and Data Science
Data analysis tasks.
Data Analyst Tasks to do the internship.pdf
Data Analyst Tasks to do the internship.pdf
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This presentation talks about how the power of Generative AI can be utilize to revolutionize the Business Intelligence and Reporting. Challenges with the traditional BI approach, benefits of using Generative AI, use cases for BI and reporting, Implementation considerations, and future outlook.
Harnessing the Power of GenAI for BI and Reporting.pptx
Harnessing the Power of GenAI for BI and Reporting.pptx
Paras Gupta
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Switzerland Constitution 2002.pdf.........
Switzerland Constitution 2002.pdf.........
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如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
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My MIT paper laying out the framework for Digital Transformation
Digital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham Ware
Graham Ware
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Jual Cytotec Asli Obat Aborsi No. 1 Paling Manjur
Jual Cytotec Asli Obat Aborsi No. 1 Paling Manjur
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怎样办理旧金山城市学院毕业证(CCSF毕业证书)成绩单学校原版复制
怎样办理旧金山城市学院毕业证(CCSF毕业证书)成绩单学校原版复制
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SR-101-01012024-EN.docx Federal Constitution of the Swiss Confederation
SR-101-01012024-EN.docx Federal Constitution of the Swiss Confederation
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Lecture_2_Deep_Learning_Overview-newone1
Lecture_2_Deep_Learning_Overview-newone1
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怎样办理圣路易斯大学毕业证(SLU毕业证书)成绩单学校原版复制
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Data Analyst Tasks to do the internship.pdf
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Harnessing the Power of GenAI for BI and Reporting.pptx
5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed
5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed
Switzerland Constitution 2002.pdf.........
Switzerland Constitution 2002.pdf.........
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
Digital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham Ware
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...
Jual Cytotec Asli Obat Aborsi No. 1 Paling Manjur
Jual Cytotec Asli Obat Aborsi No. 1 Paling Manjur
怎样办理旧金山城市学院毕业证(CCSF毕业证书)成绩单学校原版复制
怎样办理旧金山城市学院毕业证(CCSF毕业证书)成绩单学校原版复制
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