Rootstrap Blog

Category: Data Science

Total 16 Posts

How Women Need To Be Involved In Data Science To Prevent Bias In Algorithms

With the technology at our disposal today, we are trusting computers to do more and more everyday things for us. While some may be skeptical, there isn’t anything as such wrong with this as the more data we generate, the more precise the algorithms powering these computers will become. However,

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Data Cleaning

The data never comes perfect. There is always missing information, different formats, or it is full of useless information for your analysis. The process of data cleaning consists of the correction and transformation of the values, standardizing all the formats, fixing encoding, removing unnecessary information, splitting columns and extracting relevant

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What is Data Science

Why should we care about Data Science?  Nowadays more and more data is being generated by smartphones, social media, health, banks, stores, online services, governments, sensors, etc. Every piece of information is saved ‘just in case’. Thus, the available data cannot be processed by human’s brains, we need algorithms and

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Data Samples and error visualization techniques

Why we should choose representative samples with error in mind when we build data visualizations. A brief overview of uncertain bar charts and uncertain ranked lists.

The type of data samples that populate our visualizations can add uncertainty to our results. Some common data displays like bar and pie charts work better than others for making that uncertainty understandable. This article explores how to understand our data samples and create the most suitable graphs for visualizing what they represent.
In general, the goals of data science are to understand data and generate predictive models that help us make better decisions. For a more thorough overview of data visualization, see “Data visualization and The Truthful Art.”

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Data Revolution Inside Organizations

How to be prepared for the change that will transform the business landscape forever.

Worldwide access to vast amounts of data has changed the business landscape. Competitive marketing depends on knowing how to manage, process, and analyze that data. This article describes the path organizations need to take from collecting data to maximizing its use.
Today’s organizations are undergoing a challenging transformation process around their technical systems. The static software platforms that might have stored and processed a business’ data are no longer sustainable in the current web environment. Enterprises need cutting-edge technology to collect big data in real-time, analyze that data, and then get the information they need to stay competitive in today’s marketplace.

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