Will Automation Eliminate Data Science Positions
With the rise in popularity and concept of data science services, there has been speculation that data science can be automated.
While there are many operations that data scientists perform that can and will likely be automated, there are several essential phases that will virtually always require professional participation. Model comparison, visualization generation, and data cleansing are all things that can be automated in data science.
However, some of these processes are not where data management services are most effective to begin with. While data science generally focuses on coding and model creation, the immediate need for a person to oversee this procedure is because of how data science should be integrated into business and products.
Will Data Science Be Automated?
Here we will see some examples of when data science consulting services cannot be automated-
1- Understanding the Business Problem:
The most crucial question in data science isn't which machine learning algorithm to use or how to clean your data. It's the questions you should ask yourself before writing a single line of code: What data do you select to use, and what questions do you ask of it?
Target's data science team had registry data linked to previous purchases and understood how to connect it to customer spending. What criteria do we use to determine success? One of the most challenging data science tasks — and perhaps the hardest to master — is converting nontechnical objectives into technical questions that can be addressed with data. We wouldn't be able to begin without the help of experienced individuals to develop these queries.
2- Making Your Assumptions:
Data scientists must define their assumptions after generating a data science query. Data munging, data cleaning, and feature engineering are common examples of this. Real-world data is notoriously messy, and numerous assumptions must be made to fill the distance between the information we have, and the business or policy concerns we're trying to answer. These assumptions are also heavily reliant on real-world knowledge and the business environment.
While some aspects of fundamental machine learning can be automated, data munging, data cleaning, and feature engineering, which account for 90% of the real effort in data science, cannot be securely automatable.
3- Data Exploration:
Automated machine learning () does not know which data sources to seek, like how data science automation or automated machine learning () cannot initiate itself with the business question. can join, merge, and eventually union a final dataset, but it can't locate the original data before it's changed.
Data that a data scientist obtains is required for data exploration to take place. The data scientist will search several sites, sources, and platforms for data that may be used in a model. would find it challenging to contact firms and, in general, to know what data to seek – whether traffic data, customer data, or any data.
A data scientist will do this procedure, which includes determining what sort of data to seek, what industry to adhere to, the rules involved, and when to cease looking for data.
To Sum It Up...
You may have noticed a pattern in these examples: most of these scenarios happen at the start of the data science process, with one towards the finish. Because of this, the middle half of data science may be automated, and systems that do so are highly beneficial. It is, however, how you begin and end your data science analysis. A data scientist is needed at both the beginning and the completion of the process.
We have seen this contradiction before, in sectors ranging from software engineering to financial analysis to accounting, where automation enhances efficiency while driving down costs and eventually driving up demand. Data science consulting services are no exception, and the demand for this skill set will rise rather than diminish as technology advances.
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