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What’s new in Julia: Version 1.0 is here

New package manager, better optimization debut in the first production release

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Julia tutorial: Get started with the Julia language

Want the convenience of a dynamic language and the performance of a compiled statically typed language? Try Julia

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Data is inherently messy. Is that really such a bad thing?

In an imperfect world, consider shifting your data quality mindset from “how do I clean all this up?” to “how do I make the most of this state of affairs?”

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Practical use cases for real-time decisioning

Real-time decisioning has its place in almost every transaction and interaction you have with data and technology today

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What’s new in the Anaconda distribution for Python

Anaconda 5.2 adds job scheduling, support for GPUs, and integration with version control systems including Git and GitHub

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What’s new in Python 3.7

Python 3.7 adds new classes for data handling, optimizations for script compilation and garbage collection, and faster asynchronous I/O

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What is Julia? A fresh approach to numerical computing

A “no compromises” programming language for data scientists, Julia combines the ease of a dynamic language with the speed of a compiled language

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In an age of fake news, is there really such a thing as fake data?

The pitfalls and benefits of using synthetic data to train AI algorithms

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Why dynamically visualizing relationships in data matters

The ability to visualize data, their relationships to one another and connections to business objectives gives organizations the power to uncover insights that would otherwise elude them

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It’s time we tapped APIs for business analytics

With so much information flowing through APIs, the API management system offers a central hub for business insight

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How to test developer, data science, and devops job candidates

Even if they can show you code on GitHub, you want your finalists to do a trial project as a real, objective test. Here are the test projects you should use

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How will data intelligence transform the enterprise?

Traditional business methods aren’t enough to help businesses stay competitive in today’s data-driven world. Data science outperforms these outdated methods, especially in certain key areas

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How Arcadia Data brings self-service BI to the data lake

The data lake is a new approach to data management that requires a new approach to analytics

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Review: Amazon SageMaker scales deep learning

AWS machine learning service offers easy scalability for training and inference, includes a good set of algorithms, and supports any others you supply

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The new data roles, brought to you by AI

Artificial intelligence is providing market competitiveness through higher productivity and lower costs across many industries. AI is also creating new data roles companies are desperate to fill; read more about these new roles.

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Monetizing your models with machine learning solutions

Connecting the business user to the science is a necessary step for companies looking to extend their leadership position

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Big data analytics: The cloud-fueled shift now under way

Public clouds are the future of enterprise big data analytics, and their use is creating the unified platform needed to fully gain its value

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Why data science and machine learning are the fastest growing jobs in the US

The US could have as many as 250,000 open data science jobs by 2024, and the data science skills gap will find companies scrambling to train or hire talent in the coming years

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Key steps to model creation: data cleaning and data exploration

By following best practices and philosophies around these processes, an organization can enable successful collaboration and iteration between data science and IT teams

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Visual Studio Code joins the Anaconda Python data science toolkit

Microsoft’s Anaconda support is the next step in its open source analytics expansion

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