The nine commandments of data project management

At its heart, data science is about making better use of data. As organisations increasingly recognise the value of data, they are turning to data science teams to help them make the most of it. However, managing a data science team presents a unique challenge. Data scientists are not like other workers — they are more independent and need more flexibility to do their best work. As a result, traditional project management methods are often ineffective with data science teams.

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3 Simple and Popular Hashtags to Make You a Data Science Rockstar

Are you looking to sharpen your data science skills? Whether a beginner or an experienced practitioner, using hashtags can help you expand your knowledge and strengthen your skills. In this post, we’ll introduce three popular hashtags that are relevant to data science. We’ll also provide tips on how to use these hashtags effectively. So, if you’re ready to become a data science rockstar, keep reading! What are Hashtags? A hashtag is a keyword or phrase prefixed with the symbol “#.” Hashtags are

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Modern Analytics — A Data Stack For Successful Reporting

What exactly is a data stack? A data stack is a process of collecting, storing, and analyzing data in order to make better judgments. Data stacks are made up of many components that work together to build an end-to-end solution for reporting on your company’s most critical data. These components include ETL (Extract, Transform, and Load), which extracts raw data from multiple sources such as social media or portals, transforms it into useful formats such as tables with columns, and

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Augmented analytics may be the game-changer for your company.

Our best shot at decentralizing business analytics At this point, every business recognizes the critical nature of data analysis for business growth and data-driven decision-making. By 2023, companies worldwide will have generated 163 zettabytes of data. So why, then, are so few businesses leveraging data to inform business decisions? Data analytics is not for the faint of heart. Data on its own means very little; it only becomes information when combined with context. And can we then glean insights from

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