Source: analyticsinsight.net
Data scientists are at present particularly popular. In any case, there is simply a question regarding whether they can automate themselves out of their positions. Can artificial intelligence replace data scientists? Assuming this is the case, how much can their tasks be automated? Gartner as of late stated that 40% of data science tasks will be automated by 2020. So what sort of aptitudes can be effectively dealt with via automation? This theory adds fuel to the progressing ‘Man versus Machine’ banter.
Data scientists are costly to recruit and there is a lack of this expertise in the business as it’s a generally new field. Numerous organizations try to search for alternative arrangements. A few AI algorithms have now been created, which can analyse data and give experiences like a data scientist. The algorithm needs to give the information yield and make exact forecasts, which should be possible by utilizing Natural Language Processing (NLP).
Numerous people who are all in with the possibility that data scientists will be automated and jobless soon underestimate the complexities of the data preparation process. To automate anything, you have to take care of smart information” to the machine. By smart, it implies that this data should be by one way or another structured and gathered with a plan in mind in the first place.
You have to execute a predictive solution for commercial loan evaluation. As a data scientist, you should investigate the intricate details of the business. Also, at exactly that point you will concoct a type of plan on the best way to gather and analyze data that will be utilized to implement the solution.
While you may contend that banks will give engineers all the information they need, this can’t be farther from reality. In reality, it is data scientists who are liable for scanning all the data for the model. They have to make sense of significant factors, find patterns, and analyze key indicators to decide a decent versus the awful business loan.
Indeed, even the most brilliant machine learning frameworks work with what you tell it to work with. In like manner, it will improve information utilizing a representative training data set that you have prepared.
Automation in data science will crush some physical work out of the work process rather than totally supplanting the data scientists. Low-level capacities can be productively dealt with by AI systems. There are numerous technologies to do this.
Automation has its own set of limitations, nonetheless. It can just go up until this point. Artificial intelligence can automate data engineering and machine learning processes yet AI can’t automate itself. Data wrangling comprises physically changing over raw data to an effectively consumable structure.
The cycle despite everything requires human judgment to transform raw data into insights that bode well for a company, and consider all of a company’s complexities. Indeed, even unsupervised learning isn’t totally automated. Data scientists despite everything prepare sets, clean them, indicate which algorithms to utilize, and decipher the insights. Data visualisation, more often than not, needs a human as the discoveries to be introduced to laymen must be exceptionally customised, contingent upon the technical knowledge of the audience. A machine can’t in any way, trained for that.
Low-level visualisations can be automated, yet human insight would be needed to decipher and clarify the data. It will likewise be expected to compose AI algorithms that can deal with mundane visualisation tasks. Besides, intangibles like human curiosity, intuition or the desire to create/validate experiments can’t be reproduced by AI. This part of data science presumably won’t be ever dealt with by AI soon as the technology hasn’t developed to that level.
There is an unmistakable point of reference in history to propose data science won’t be automated away. There is another field where exceptionally trained people are making code to cause computers to perform astonishing accomplishments. These people are paid a noteworthy premium over other people who are not trained in this field and there are education programs specializing in training this skill. The subsequent financial strain to automate this field is similarly, if not more, intense. Data science field is software engineering.
Moreover, as software engineering has gotten simpler, the demand for software engineers has only increased. This paradox, that automation increases efficiency, driving down costs and at last driving up demand isn’t new. We’ve seen it over and over in fields running from software engineering to financial analysis to accounting. Data science is no exemption and automation will probably drive up demand for this range of abilities, not down.
Automation will act as a supplementary tool that will boost data science tasks and make them more efficient. Bots can take care of lower-level tasks, whereas data scientists can take care of problem-solving tasks. This combination of human problem-solving and automation will, moreover empower data scientists, rather than threatening their jobs. There will be more technological advancements coming up in the future. However, it is important to understand that data scientists possess a very important skill – intuition, which is very difficult to be emulated by advanced artificial intelligence.