This week, we get a crash course on data visualization with a step-by-step guide. We learn how to plan the right sample size for data analysis and consider the importance of the human touch. We share the biggest myth about machine learning and we examine the new world of cultivated data: what it is and why it’s important.
Don’t Be Left Behind! A Step-by-Step Guide to Data Visualization
by Ann K. Emery, contributing writer for NonProfitPro.com
Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your organization’s numbers out of spreadsheets and into real-world conversations. A series of small, intentional edits can completely transform your visualizations. This article provides six practical steps of the data visualization design process so that you know how to present your data in the most visually effective way to transform your spreadsheets into compelling stories.
Planning the Right Sample Size for Data Analysis
by Pierre DuBois, contributing writer for InformationWeek.com
There are many steps to the data modeling workflow. One important step is deciding the right sample size of observations for a model. Sample size affects the statistical results for correlations, regressions, and other models. This article covers limitations you’ll face, steps you can take to get around those limitations, and how to compare data to ensure an apples-to-apples comparison.
Busting the Single Biggest Myth About Machine Learning
by Leslie Wood, contributing writer for MarTechAdvisor.com
Machine learning has the power to transform the advertising industry and live up to the massive hype surrounding the technology—but getting there requires a human touch. It will give you conclusions about your data, but it won’t tell you which of your models is the most accurate and valid. You need human expertise for that.
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Cultivated Data is the Next Gold Rush
by Bernard Moon, contributing writer for TechCrunch.com
First, it was big data, now cultivated data is the next big thing. Cultivated data is existing data that is analyzed and developed into a more usable form than it was before through the application of approaches and techniques to data sets that previously weren’t used. This article covers what’s happening initially in the cultivated data space. From well-coordinated government policies to market forces to increased startup activity around cultivated data, these trends and developments are an indicator that this space will be one of the major gold rushes for startups and venture capital over the coming years.
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