What is Data Visualization ?
Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software.
Why Data Visualization ?
A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.
Data visualization is both an art and a science. It is viewed as a branch of descriptive statistics by some, but also as a grounded theory development tool by others. The rate at which data is generated has increased. Data created by internet activity and an expanding number of sensors in the environment, such as satellites, are referred to as “Big Data”. Processing, analyzing and communicating this data present a variety of ethical and analytical challenges for data visualization. The field of data science and practitioners called data scientists have emerged to help address this challenge.
Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner – and you can experiment with different scenarios by making slight adjustments.
Data visualization can also:
UNICOM is Organising one day workshop event on Data visualization on 12 December and one day conference on Data visualization on 13 December, Manchester.
This event will bring together industry professionals and thought leaders from the field of Data Visualization. It will help you in understanding and implementing data visualization in your business/for your client . It will also provide an excellent opportunity to interact and network with some of the top minds.
We are looking for speakers willing to share their experiences and stories about their work in the field of Data visualisation. If you wish to submit a proposal to present at this event please fill in the speaker’s response form.
There’s no “right” answer here, but some topics that we’re always looking for include:
David Moss, Freelance Data Guru
David introduces the art of data visualization across various modern software platforms including Tableau and Power BI to visualize Big Data, Real Time data, Business Intelligence and some intriguing visual data stories.
Dominic Jordan,Chief Data Scientist, INRIX
An exploration of how good upfront visualisation can save a Data Scientist many days of blind walking when building an ML model. With practical real-world examples.
Steve Bradbury, Head of Fraud and Data Division, GRSC Group
The GRSC Data Platform was developed to provide a single, comprehensive user-friendly platform to manage huge volumes of disparate data. The platform incorporates the latest security features available; caters for a wide variety of file formats; offersautomatically OCR and Profile ingested data; provides advanced Entity Extraction functionality; has powerful and unique visualisation of data capabilities.
Omar Costilla-Reyes, Researcher, The University of Manchester
This presentation demonstrates accurate spatio-temporal gait data classification from raw tomography sensor data without the need to reconstruct images. This is based on a simple yet efficient machine learning methodology based on a convolutional neural network architecture for learning spatio-temporal features, automatically end-to-end from raw sensor data. In a case study on a floor pressure tomography sensor, experimental results show an effective gait pattern classification F-score performance of 97.88 1.70%. It is shown that the automatic extraction of classification features from raw data leads to a substantially better performance, compared to features derived by shallow machine learning models that use the reconstructed images as input, implying that for the purpose of automatic decision-making it is possible to eliminate the image reconstruction step.
Flow Bohl, UX Architect, Bloomberg New Energy Finance
♦ What is the Bloomberg approach to data visualisation?
♦ How does animation matter in data viz?
♦ How can data viz be made feasible for large scale consumption on web and video?
Uros Strel Lencic, Lead UX/UI Designer,Koodee
Delivering habit-forming products is a must for every organisation to succeed. From race prediction experience and providing insights into the relative ease of living a healthy life to hiring the right candidates for restaurants, shops and events. Using and visualising data to enhance user experience can massively help. This talk will present how UX/UI design can communicate data effectively to create engaging data enhanced applications that are built on a solid foundation of data analysis and visualisation.
Emma Cosh, Freelance Visualisation Director
There’s an abundance of beautiful data visualisation in the public sphere, but inside companies and analytics teams, the standard is often much lower. This talk proposes that User Experience Design is the key to transforming internal data visualisations and provides a simple framework and design tips to begin the journey.
Pete Moore, Founder, Chief Strategist and GDPR expert, Look At Your Data Ltd.
Demystifying the GDPR with a graph database. The GDPR is only 99 articles long. These articles refer to themselves 447(!) times. Reading the GDPR is difficult and you can use Graphing to do it more efficiently. For example, article 83 refers to 22 different parts of the regulation: read it last! Which article should you read first? The graph visualisation we create will tell us that and more. I needed to do this because I am not a lawyer and I needed to read the GDPR in an order that made sense. Repeat: I AM NOT A LAWYER. The content is given from a data strategy point of view and whilst it is intended to inform and enlighten, it is not binding.
Kasper Van Lombeek, Data Scientist and Co-Founder, Rockestate
Since the 1 million euro contest to improve the performance of the Netflix recommendation engine, recommender algorithms are one of the hottest topics in data science today. That is why we, passionate data scientists, had to build our own recommendation engine. As young fathers with lots of friends having babies, we asked ourselves: can we build a service that finds out your taste and recommends a baby name for you? Today we have a solid website www.namesilike.com that does exactly that.
With today’s online tutorials, it is not very difficult to learn how to build a recommender system based on a dataset. Those tutorials only talk about mathematically optimizing the algorithm. But that is not what you have to optimize! You have to set up a whole process online, starting from finding out the user’s taste with the least possible effort, and ending with showing the recommendations. The difference between optimizing the accuracy versus the user experience is enormous. For example: some of the most interesting algorithms create beautiful visualizations of your products. Although you lose a bit in prediction power, the gain in user experience is enormous. In our talk we highlight some of these trade-offs. They are all real lessons learned on Names I Like, validated by thousands of user clicks.