Tips on Why Data Science Jobs Will be automated in Future

The march of computers towards to the line of automating all processes, seems relentless and almost inevitable. This is not any different in the case of data science- the future of data science and big data analysis is in automation and it is only wise for data scientists to brace themselves for the future.

Although data science is a relatively new field in terms of age, it has already made giant leaps in the world of automating its operations and sooner than later, the human aspect will completely be eliminated from its dispensation.

In this Guttulus blog post, we respond to a number of questions in our inbox on the possibility of automating data science processes in the future and how data scientists can brace themselves for inevitable change. Here are 10 tips on why data science jobs will be automated in the future;

Tips on Why Data Science Jobs Will be automated in Future

Repetitive Nature of the Job

One of the main reason why data science will easily be automated, is the fact that it is very repetitive in nature. Data science revolves around training systems to carry out a number of tasks by constantly feeding them with data. With this repetitiveness, training systems to automatically carry out the job, becomes very easy.

High processing abilities of computers

The modern day computers have very high processing capabilities and this is only bound to get better with time. This high processing abilities of computers makes it very easy to train systems to automate most of the tasks involved in data science.

Simulation has already taken effect

Some of the tasks which were initially delegated to junior engineers and interns in companies, are being simulated and rendered by these powerful modern day computers. With improving technology and computing capabilities, the simulation and rendering will only get complex and all-inclusive.

Computers have proved to be faster than humans

Trials to automate some of the basic elements of data science have proven that automated data science processes are way quicker than what humans are capable of doing with the same set of data. Who doesn’t want to save time while processing and analyzing a set of data? This is why automation will be received with open arms in most companies and institutions.

Automated data science is more accurate

Provided the training is done properly and using the right software on appropriate hardware, automated data science is way more accurate than what humans are capable of. A research by the MIT Big Data Analytics, showed that the accuracy level of a properly trained system is in the neighborhoods of 96% in terms of accuracy.

Automation improves prediction abilities of systems

In predictive analytics, the time and effort spent to predict or forecast trends using a given set of data is reduced by a very big margin. Although systems haven’t been automated to be used in prediction, tests point towards very successful and accurate automated predictor systems.

Tips on how to remain relevant in future as a data scientist

Following the inevitable automation of most data science processes, data scientists will need to be on their A-game to retain their positions in companies. Here are a few tips by Guttulus on how to remain relevant in the world of data science following the predicted automation;

Add new skills to your CV

If you want to remain relevant in the job market in future, you’ll need to learn new skills to align with the flow of technology. If the future leans massively towards automation, scamper today and learn about automation.

Learn about deep machine learning

To automate data science systems, you need knowledge in deep machine and artificial intelligence. If these are things you hadn’t considered when learning data science, then this is the best time to take up courses in the field and equip yourself with skills in these areas.

Learn about neural network simulations of the brain

The human brain is being ‘cloned’ in computers and being used to automate a number of processes. To understand how the human brain works and its relevance in the world of data science, you will need to take a course in neural network simulations of the brain. Understanding this will place you ahead of most of the data scientists in the eyes of employers.

Learn new and more sophisticated coding technology

Besides the already in use programming languages, there are new programming technologies and languages which you will need to learn to stay relevant and employable in data science. Research on the new trends in the world of data science and automation and try as much as possible to keep up with what the gurus in the field are learning.

Talk to Guttulus about the future of automation and what you should learn

Want more tips on how to get started in Data Science and the best courses to take? Want to know the best courses to learn for automation of data science systems?  Give Guttulus a call today or subscribe to the Guttulus blogs for constant tips and guidance on how to get started in data science.

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