Data visualization is helping companies worldwide to identify patterns, predict outcomes, and improve business returns. Visualization is an important aspect of data analysis. Simply put, data visualization conveys outcomes of tabular or spatial data in a visual format. Images have the power to capture attention and convey ideas clearly. This aids decision making and drives action for improvements.
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Deep Learning algorithm is one of the most powerful learning algorithms of the digital era. It has found a unique place in various industrial applications such as fraud detection in credit approval, automated bank loan approval, stock price prediction etc
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Image recognition is one of the features that is deployed in most modern devices and programs. The feature has a wide range of applications, the most common one being in security systems. It enables devices to recognize the identity of a person or object just by its image. Face recognition is a product of image recognition.
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Kotlin comes from industry, not academia. It solves problems faced by working programmers today. As an example, the type system helps you avoid null pointer exceptions. Research languages tend to just not have null at all, but this is of no use to people working with large codebases and APIs which do.
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Almost a decade ago, Satoshi Nakamoto, the creator of Bitcoin, silently disrupted trust-providing organizations, like banks, with an eight-page long research paper describing how money can be decentralized (through Bitcoin). At the time, not many people understood the potential that those eight pages concealed within themselves.
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A Small Real-World Project for Learning Three Invaluable Data Science Skills. As with most interesting projects, this one started with a simple question asked half-seriously: how much tuition do I pay for five
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What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.
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Thousands of programming languages exist, but there are some trusty ones that developers turn to again and again. GitHub, the startup at the center of open-source software development, tracks these programming trends.
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I recently joined the Enterprise Insight Studio team at Accenture's global center for innovation in Dublin as an Artificial Intelligence (AI) Software Engineering.
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Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform a technical analysis of data. A basic visualization such as a bar chart might give you some high-level information, but with statistics, we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
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With the release of technologies such as TensorFlow and CloudVision, it's becoming easier to use machine learning (ML) in your mobile apps, but training machine learning models still require a significant amount of time and effort.
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Deirdre Dempsey has decades of experience dealing with data analytics, data science stemming from a strong foundation in mathematics.
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Computers are good with numbers, but not that much with textual data. One of the most widely used techniques to process textual data is TF-IDF in Python. In this article, we will learn how it works and what are its features.
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A team of researchers at The University of Texas at Austin has created CRN++, a new language for programming deterministic (mass-action) chemical kinetics in performing computations.
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This list contains free learning resources for data science, machine learning and big data related concepts, techniques, and applications. Inspired by Free Programming Books.
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Top 100 Data Science Resources & Tools For 2018 Whether you're just getting started in data science, or are gearing up to get a Masters in Data Science or Business Analytics, there's always more to learn. This guide gathers 2018's top data science resources on the web for learners of all stages.
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An illustrated guide to becoming a DevOps Engineer or Programming Language expert with links to relevant courses.
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Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary.
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