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The Future of Data Scientists and Algorithms

  05 October 2018       London, UK

If you have visited a social media site today or simply used the internet or your smartphone, you have generated data; 90% of data today has been generated just in the past 2 years. “A data scientist is a person employed to analyze and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its’ decision-making” - Google 2018.

There is no ‘typical’ day for a data scientist but their daily tasks revolve around gathering, looking at, and shaping big data in many different ways. Essentially data scientists try to find patterns in vast amounts of data and connect these patterns to real world / business decisions. Within this role incorporates a lot of decision-making and problem solving, largely through the use of developing algorithms.

The quantity of data has surged massively, but has this been a positive or negative impact for data scientists? What is the future for data scientists?

As we know, artificial intelligence and machine learning is on the rise and machines are slowly taking over human tasks. However, big data is as relevant as ever and businesses do not know how to cope with this data or how to read it; this is where data scientists step in. There is a shortage of data scientists due to a massive surge of data being gathered the past few years and businesses not realizing this would happen and thus not employing people data scientists. Theoretically, AI could erase the need of a data scientists using machine learning algorithms; analyzing data a lot faster than a human. However, there are a lot of complications with this including communication about the data and finding exactly what is needed. In the near future, the need for data scientists is on the rise with companies desperately trying to build their team to read more data. In the future, we are uncertain as to whether AI can be so advanced to take over their role.

What does the future of data scientists look like with AlgoLib?

With AlgoLib, data scientists’ job will be made easier and more efficient with using just one line of code. Many data scientists develop code and re-use code which can take up to weeks to develop; with AlgoLib, these developed algorithms can be found within minutes, saving them time and resources. With more efficient processes, data scientists can use their time to research the market trends and analyse more data, all leading to a more effective business.

It has been said that a data science team of 6 members or more can lead to reproducibility suffering. With AlgoLib’s shared workspace we can offer solutions to fix this problem with features designed to track changes. Sharing and building algorithms together could not be easier with optimized communication through just one platform.

Make your life easier as a data scientist. Click the link to join the UK’s first AAAS platform today!

Author: Beccy Ballantine.

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