Effective Data Visualization: The Right Chart For The Right Data

A comprehensive and coherent set of mathematics standards for each and every student from prekindergarten through grade 12, Principles and Standards is the first set. Dont simply show data, tell a story with it At storytelling with data, Cole Nussbaumer Knaflic shares practical tips and examples of effective data visualization. If you dont like the default colors in a bar chart or pie chart, check out a color palette that is designed for data visualization. Effective Data Visualization: The Right Chart For The Right Data' title='Effective Data Visualization: The Right Chart For The Right Data' />Data visualization techniques, tools at core of advanced analytics. Predictive modeling and other kinds of advanced analytics are done with powerful software built specifically for running complex algorithms on large data sets, such as programming languages like R and Python and analytics tools like SAS and IBM SPSS. But many data scientists and analytics managers will tell you that a lot of their work and ultimately their ability to provide useful information to business executives also relies heavily on more humble data visualization tools. Far from being a bit player in analytics applications, data visualization fills several crucial roles throughout the process. From initial data exploration to development of predictive models to reporting on the analytical findings the models produce, data visualization techniques and software are key components of the data scientists toolkit. Without them, analytics teams are engaging in a nearly impossible task thats tantamount to flying an airplane while blindfolded. Data visualization just makes our analyses so much more efficient, said Daqing Zhao, director of advanced analytics at Macys. The human brain can only comprehend so much. Effective Data Visualization: The Right Chart For The Right Data' title='Effective Data Visualization: The Right Chart For The Right Data' />Data visualization is critical for technical and operationalsavvy business analysts who juggle multiple projects at a time. Both analysts and project managers tend. The only way to see patterns is to use your eyes. The advanced analytics team at Macys. San Francisco based online arm of retailer Macys Inc. The data scientists managed by Zhao build recommendation engines, perform AB tests of new webpage layouts and help the marketing team plan and execute targeted email campaigns. They run a mix of machine learning and predictive modeling applications that require a variety of tools and approaches, and data visualization enters very early in the process. In fact, Zhao said his team starts every job by visualizing the data its working with. For example, the analysts might pull out some specific variables into a graph to see if theres any correlation between them. Post2_Stacked_Bar.png' alt='Effective Data Visualization: The Right Chart For The Right Data' title='Effective Data Visualization: The Right Chart For The Right Data' />Or theyll chart basic summary statistics things like mean and median averages, data spread and standard deviation metrics to get a sense of the scope of the data. Exploring the data visually gives them a better idea of where to focus their attention when building analytical models than they could get by looking at a giant spreadsheet, Zhao said. Software smorgasbord. Some of the analysts use Tableau software to explore and visualize data sets. Others use visualization components built into more sophisticated analytics software such as SAS, R and the H2. O open source machine learning platform. Some are even visualizing data directly in Excel spreadsheets. Were tool agnostic, Zhao said, adding that in an advanced analytics environment, its best to support whatever tools your data scientists feel most comfortable using. Boris Savkovic. At Building. IQ, an analytics services provider that helps building owners and facilities management companies predict and control their energy use, data visualization similarly helps narrow down data sets and provide guidance on developing predictive models and algorithms to the companys analysts. Building. IQ, which was founded in Australia and is now based in San Mateo, Calif., collects data from the heating, ventilation and air conditioning HVAC systems in buildings identifies power consumption trends and looks for areas in which the buildings could become more energy efficient. Boris Savkovic, the companys lead data scientist, described data visualization as a first pass in that process. Savkovic and his team create advanced machine learning algorithms using Mathworks MATLAB software. The algorithms take into consideration variables such as historical energy usage, future weather forecasts, power meter readings, information from HVAC pressure sensors and energy cost data. Its a lot to take in all at once, so the analysts start by employing some simple data visualization techniques. Generally, they put a couple of variables into a line plot to see if the metrics track together. If so, that might be grounds for investigating whether theres a true statistical correlation and building an analytical model around the data. Visualization is the bread and butter, Savkovic said. Cd Tim Maia O Melhor De Tim Maia 2009 here. It helps expose patterns over time as well as patterns between different variables. Plotting a number of variables helps paint a picture as to what issues might be present in a given building. Complementary technologies. How To Save A Gw Basic Program. Predictive analytics programs are becoming more common in organizations, fueled partly by the rise of big data architectures and the increasing commercialization of machine learning technologies. As a result, predictive modeling and data visualization tools appear to be developing an even greater affinity for one another. In an ongoing survey conducted by Tech. Target Inc., data visualization was the top business intelligence and analytics technology that respondents said their organizations had invested in during the past six months before they took the survey. As of late August, 4. Meanwhile, predictive analytics ranked fourth 2. However, the two technologies essentially tied for the top spot on planned investments over the next 1. Predictive analytics was narrowly ahead, selected by 3. Ties That Bind. Those results jibe with the findings of a separate BI and Big Data Analytics Market Landscape Study, also conducted by Tech. Target. Based on a survey of 6. Samurize Windows 7 64 Bit. IT, BI and analytics professionals in late 2. The ties between the two technologies dont just apply to the analytics planning stage. Data visualization techniques and tools can also help keep the development and training of predictive models on track. In this highly technical phase of the analytics process, the popular image of a data scientist hunched over a keyboard unspooling lines of code isnt far from the truth. But it can be easy to lose your way in a maze of parentheses, brackets and commands. At this point, a picture can be worth a thousand lines of code. Preface. You should look at your data. Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also. Brendan Herger. Brendan Herger, a data scientist at banking and credit card company Capital One, based in Mc. Lean, Va., said he uses data visualization software to monitor the data coming out of predictive models as he writes and tests them. That helps him see whether a model is working as expected and its output makes sense. Herger uses H2. O to build and run the models as part of machine learning applications, and he visualizes the data with H2. O Flow, a web based interactive user interface offered by vendor H2. O. ai. In addition to visualizing data for his own benefit, Flow lets Herger share the results of his work with other members of the data science team at Capital One so they can also take a look and confirm the effectiveness of the predictive models hes building. Its pretty cool to be able to spot check and make sure the data looks right, he said. All inclusive analytics loop. Reporting on the results generated by predictive models is where effective data visualization techniques can really pay off or, conversely, where advanced analytics initiatives can go awry. If data scientists arent able to show corporate executives and business managers that finished predictive models are delivering worthwhile information with the potential to improve internal decision making and operational processes, support may dry up, and analytics projects could be cut back or abandoned altogether. Visualization makes data accessible to a much wider audience, and that helps grow the analytics culture of the organization.