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Category: Data Science

  • A Guide to Selecting a Data Science Vendor data science vendor selection

    Making a choice of a data science vendor can be a make-or-break decision for your company so here are some points to consider when developing a solid strategy for vendor selection in the data science technology space.

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  • Building up data science capabilities: to hire or not to hire data scientists

    Today, every business wants to increase their data science capabilities, and it’s not surprising why. Data scientists can gather business insights, and develop products that can significantly contribute to the overall business value. Essentially, they make the data more understandable and useful to the rest of the company. Here is how you can build up your data science capabilities.

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  • Role of AI Technology in Popular Mobile Apps in 2017 AI technology

    It wasn’t long ago when half the world got glued to their screens watching the presentation of a new iPhone X, which claims to be “innovational”. All the fans who have recently been lining up for a couple of days in order to be among the first iPhone X owners also add hype to the…

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  • Belarus AI landscape. Sixty companies across ten industries. AI landscape

    Aleksey Melnichek, IT entrepreneur & investor, decided to illustrate that not only world-famous AIMatter and MSQRD are involved in the sphere of AI in Belarus and prepared Belarus AI landscape. AI technologies are developed by more than 60 companies at the moment and the borders of Belarusian AI map will expand. With the help of…

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  • 5 Artificial Intelligence trends shaping business in 2017 Artificial Intelligence trends

    Artificial Intelligence as an industry matures. Many of the problems that impeded adoption of this technology in the past are disappearing and AI reveals its potential to change how we live, move, and work. So let’s have a look at the key AI trends that will shape our future.

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  • What companies need to understand to benefit from AI technology AI technology

    Although businesses are actively looking for ways to put AI technology to work and improve business results, many of them are still hesitant when it comes to undertaking necessary measures associated with certain corporate changes. Here are the tips that might help overcome those doubts and evaluate your company’s readiness to adopt the AI technology….

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  • How to win Kaggle competition based on an NLP task not being an NLP expert Kaggle competition

    Apart from performing for our clients, InData Labs data science team is keen on taking part in top notch data science competitions, for example, Kaggle Competition. The team has recently shown one of the best results in Quora Question Pairs Challenge on Kaggle. The challenge is remarkable for a number of interesting findings and controversies…

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  • Keys to building robust data infrastructure for a data science project Keys to building robust data infrastructure for a data science project

    Ones you decide to leverage data science techniques in your company, it is time to make sure the data infrastructure is ready for it. Starting a data science project is a big investment, not just a financial one. It involves a lot of time, effort, and preparatory work. Data science is about leveraging a company’s data…

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  • Starting a data science project: Three things to remember about your data data science project

    Many companies are collecting and managing the data with little to no forethought. And just as planning is key to any strategic business project, forethought is utterly important when dealing with data. Here are the key points to remember when starting a data science project. Identify the right questions about your data In most cases…

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