Is Data Science a dying career? Will it become obsolete? AI impacts

Data helps organizations to get valuable insights and come up with strategic decisions. Many of a company's decisions are data-driven, making it an asset of a company. 70% of companies will shift to a data-driven decision model by 2025. So there will be an increasing demand for jobs roles like data scientist, analyst, etc. In the 21st century, data science has been the most in-demand, promising, high-paying career of all time.

Data Science in Demand:

• LinkedIn's Jobs on the Rise article 2021: 15 job opportunities that are in demand included data science specialists and artificial intelligence practitioners.
• Hiring for a data scientist role has increased by 46% and AI practitioners by 32% since 2019.
• The Bureau of Labor Statistics predicts that there will be a 36% growth in data science jobs.
• Also, data science comes 3rd in the top 50 best jobs in America (2022) on Glassdoor.

Now let's understand,

Why Data Science became of the most demanding and high-paid careers of all time?

The term "Data Science" started in the 1960s, describing a new profession that involved gaining insights from data. At that time there weren't concepts like data collection, analysis, or mining, and there weren't enormous amounts of data to gain insights.

With the rise in technology came platforms that brought in massive data that could be used to gain valuable insights.

Back in the day, there wasn't specifically a well-defined path for becoming a data scientist. People who did the work of analyzing data were exceptional at math and knew how to code. They held Ph.D. in experimental physics or were astronomers, psychologists, and meteorologists(source). It was not that they were a requirement for a Ph.D., it was just that these individuals could work with complex data sets and build complex algorithms.

• In the mid-2000s, companies started to realize the importance of data and wanted to make use of it to make better business decisions.
• AI started to become mainstream, and companies wanted to hire data scientists to develop AI models.

• Demand for data scientists has increased because of the increasing number of data sources or platforms that bring in different kinds of data. Proper analysis of this data will allow a business to take effective decisions, optimize its performance, reduce costs, and have more customers and better profits. Therefore the skills of a data scientist can provide valuable results to a company.
• Data scientists command high salaries because the demand is more and the supply is less.

Why is a Data scientist needed? What does a data scientist do?

The role of a data scientist is to mine data for information and analyze and interpret data to help make decisions for an organization. The role of a data scientist varies according to different domains. Different companies have their requirements and use the data accordingly.

A data science course includes:

• Programming languages like Python/R - Data analysis and visualization using Numpy, Pandas, Mathplotlib, Seaborn, etc.
• Statistics and Maths.
• Machine Learning and Algorithms.
• Tools like Tableau, Tensorflow, etc.
• Model training and deployment.
• SQL
• Capstone projects

What are some other job roles in the world of Data science?

1. Data analysts
2. Business Analyst
3. Data and analytics manager
4. Database administrator
5. Statistician
6. Machine Learning Engineer
7. Data engineers
8. Data Scientist

Is the data science job market oversaturated?

No, it is not,

• Challenging - Although a career in data science is one of the most rewarding ones but is also one of the challenging ones. It requires a lot of hard skills like mathematics, the ability to program, good knowledge of data structures and algorithms and ways to use them efficiently, ability to transform data into usable formats. A career in data science is more challenging than other tech fields. The learning curve is also steep.

• Skill Gap - Many individuals jump into data science only by looking at the salary aspect of it but do not possess the necessary skills. Many professionals do not have the skills to keep pace with automation and implement algorithms effectively. Therefore there is still a skill gap, which is not fulfilled.

• 99% of firms surveyed by NewVantage Partners want to leverage the power of data but cannot find the talent they need.

• Constant changes to the field - The field of data science is constantly evolving, and there are many upcoming changes in the future. The field still has room for development.

• Room for acceptance - Many businesses, as of now do not understand the technical aspects of data science and how to use it effectively. The field of data science has evolved quickly, and organizations have struggled to keep up. Many organizations will hire data scientists to gain a competitive edge. (source)

So why do some people think data science is dying? Is it dying?

1. Automation impacts: The main reason why people think it is dying is that data science involves a lot of statistical and quantitative analysis, which many believe can be automated. While others believe not everything in data science can be automated or replaced by AI, which is true.
2. FOMO of Companies: Companies are hiring data science professionals, creating data teams, and investing in AI without understanding the applications of machine learning or deep learning which later does not yield the desired results.
3. Lack of awareness and guidance: There is no well-defined path to becoming a data scientist, as of now. There's a lack of proper guidance and a lack of awareness about the job requirements among aspiring data scientists. Most end up having only a broad overview and end up being a jack of all trades and master of none.
4. Data science gives an overview: Many believe the study of data science gives you an overview of the world of data. Further, there are Machine learning engineers, Data engineers, and Artificial Intelligence engineers are the one who specializes in something very specific and can solve more complex problems.
5. Data science is a degree like an MBA or a computer science degree. There are no job titles like MBA or computer science. This is all a part of the technology hype cycle and is temporary. (source)
6. It's a start: Companies are just in the initial phase of adoption of ML, and AI, getting to know the technical aspect of data science. There are hiring data scientists now because they understand it can give them an edge over their competitors.

So is data science dead?

No data science is not dead. The job title of a data scientist may fade away, but the degree of a data scientist is here to stay. The role of a data scientist may change over time.

So a data science degree will stay as an important degree. It's just that there are going to be many changes in the field of data science.

What may happen?

It may happen that a data scientist degree will give an overview of everything and specialization in one, or a data scientist will bridge the gap between technical and business skills, and a data scientist will lead the data team.

Will AI replace data scientists? Will there be newer roles in the future?

McKinsey estimates that about 64–69% of the total time spent on data collection and processing can be automated.

What parts of data science can get automated?

• AI will be able to perform many tasks that data scientists, data analysts, and data engineers perform including,

1. Cleaning data.
2. Data Visualization
3. Identifying if the data is correct.
4. Model building or generating variations of models.
5. Automating deployment of models.
6. Generating variations of models.

So will AI replace data scientists?

No, AI will not replace data scientists completely. A data scientist will still be required to do the following.

• Ensuring a model works in the desired manner with accuracy or making sure that a model works the way it is supposed to, will be the responsibility of a data scientist.
• Some common problems faced by most organizations can be solved using a fixed set of algorithms or with the use of a tool, and the other issues that are specific to an organization or that require a combination of approaches to solve will require a data scientist.
• Finally, the best results will be delivered with the combination of a data scientist and AI tools.
• Many data scientists will remain valuable to a company because of their experience with different types of data.

• Apart from the data provided, a data scientist may have to also consider the external factors that may affect or optimize a solution.

Data scientists with problems solving skills, and who can use the upcoming AI tools effectively will always stay in demand.

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