Fifth Elephant 2014 talk - Crafting Visual Stories with Data

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Amit Kapoor

Talk at Fifth Elephant 2014 on the principles behind crafting visual stories with data.

Data & Analytics

Fifth Elephant 2014 talk - Crafting Visual Stories with Data

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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.

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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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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.

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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.

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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.

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Deep Learning for NLP

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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.

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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

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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

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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.

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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.

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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

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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.

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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.

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Context 1. Make Insight-informed Decisions: Clinic collected data on heart disease diagnosis and other patient information, and wants to use the data to make insight-informed decisions. Objective 2. Predict Patient’s Well-being: To identify the rules that will predict whether a patient will have heart disease in the future, based on the data collected on him/her. Strategy 3. Deploy Decision Tree Model: Create a Decision Tree Model, with rules, to predict whether a patient will have a heart disease in the future based on collected data. 3.1 To train and evaluate the model 3.2 Boost the model’s performance 3.3 Conduct predictions Author: Anthony Mok Date: 18 Nov 2023 Email: xxiaohao@yahoo.com

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