Data Science In 10 Years: Will Data Scientists Become Obsolete?

Data science encompasses many disciplines including machine learning, artificial intelligence (AI), data analysis, and statistical analysis. If you want to get a job in data science but you’re concerned about the future of the industry, then you have landed at the right place. Will data science become obsolete? This article explores the answer.

What Is Data Science?

Data science is a subset of computer science that uses analytical tools and artificial intelligence (AI) to transform raw data into valuable datasets. Studying data science can open up job opportunities for you in a variety of industries, including data analytics, business intelligence, AI, machine learning, and statistics.

Data science skills are in high demand across several lucrative industries. Businesses use data science to improve marketing strategies, enhance productivity, and increase revenue. You’ll often find data scientists working in the information technology, education, biomedical technology, and manufacturing industries.

There are several data scientist skills that industry professionals need to master to truly harness the power of the discipline. An expert data scientist should be familiar with machine learning, AI, Python, SQL, data analysis tools, statistical analysis, data wrangling, and database management.

Will Data Science Be Replaced by Future Technology?

Data science will certainly evolve and change over the years, as it already has. It’s hard to predict if any one technology will replace data science as a whole. As it stands now, data scientists are some of the most in-demand professionals in business as the value of data becomes increasingly well known.

Read below to learn about some of the technologies that apply to data science. We’ll discuss their functions and debate whether they’re likely to replace data science entirely.

AI Could Replace Humans

One of the biggest threats to data scientists is the rapid growth of artificial intelligence (AI). AI technologies are used extensively in data science and allow for predictive modeling, data analytics, and statistical algorithm testing. These technologies help automate tasks, which can reduce the need for human data scientists in the future.

However, it’s not fair to say AI will replace data science. Data science and AI combined have revolutionized decision-making largely, through predictive analytics models. Data scientists aren’t likely to be replaced by AI anytime soon, as human intelligence is needed to modify algorithms and correct machine errors.

It is more suitable to consider AI technologies to be a complementary branch of data science. AI technologies like machine learning, neural networking, and deep learning will likely only assist data scientists in their duties for the foreseeable future.

Privacy Could Be an Issue 

The entirety of data science applications is reliant on acquiring reliable datasets. However, data privacy laws aren’t all-encompassing and much of the data collection performed by companies is unregulated. When data is collected by invading people’s privacy, this can start to erode the foundations of data science.

Synthetic datasets completely bypass the need for real datasets and can be used to train machine learning models or predict certain outcomes for businesses. This is one workaround to avoid violating privacy rights, but it can’t be used as a stand-in for some cases where real-word datasets are essential.

Data Science is Ingrained Across Industries 

One reason why data science will not become obsolete is because it’s essential to many business operations. Data science offers companies qualitative analysis, quantitative analysis, statistical predictive modeling, and data synthesis. These produce valuable insights provided by data scientists that can’t be gained elsewhere.

The finance, ecommerce, healthcare, education, and computer science industries are reliant on data science solutions. Data scientists ensure the algorithms used in the discipline are error-free and help in translating hard data into digestible visual data.

Technologies and Trends That Could Make Data Science Obsolete

Below we’ve listed some of the technologies and trends that will affect the trajectory of data science. While data science as a field isn’t not in danger of becoming obsolete, these technologies will shape how the field evolves in the future.

Internet of Things 

The growth of the Internet of Things (IoT) means data sources and datasets will increase over the next 10 years, creating the need for more data scientists. IoT devices have sensors that allow them to store data and communicate with other IoT devices. Everything from refrigerators, televisions, toasters, and automated cars utilize IoT and create scores of data.

Automated Machine Learning Technologies 

Another major technology emergence is machine learning automation technologies. The current data science industry is highly integrated with the machine learning field. Repetitive data tasks such as data cleansing and data sorting are now automated with machine learning modeling.

However, complete reliance on machines isn’t a viable solution for organizations as AI systems lack optimal human intelligence capabilities. The progression of machine learning technologies could transform the job duties of data scientists and require them to focus more on optimizing the automation of tasks rather than the tasks themselves.

Synthetic Datasets

AI technologies can create synthetic datasets to further train machines without the need for real data. The further development of synthetic datasets will allow data scientists to provide extensive machine learning training without infringing on the privacy rights of people. This technology also paves the way for an expansive and bright future for accurate dataset prediction.

When Will Data Science Become Obsolete?

Data science as a discipline will not become obsolete. However, it will grow and evolve significantly. The importance of big data and data science applications is established across the information technology, video game, ecommerce, marketing, biomedical technology, education, and sports sectors.

Without data analytics and data mining customer service, operation productivity scales, disease tracking, and result prediction will be highly inaccurate. So, a more fitting response is that the field of data science will evolve to match AI and automated technologies in around the next ten years.

What This Means for Businesses

Businesses are highly dependent on the applications of data science and will be highly impacted by the evolution of the field. If you want to become a business intelligence analyst, a market research analyst, or you’re a business owner, then you should keep up with the ongoing changes across the data science field.

Below are the various ways businesses will be impacted due to the progression of the data science sector. Regardless of the industry, almost all businesses can make use of the services offered by data scientists.

Increased Data Collection Resources

Large and accurate datasets are vital to a business’s decision-making process and revenue growth. With the increase in IoT devices and the interconnectivity of a user’s digital footprint, businesses will have even more access to their user data. This surplus in big data will allow businesses to develop more informed target marketing campaigns and strategic sales plans.

Enhanced Customer Relationship Management Services 

Large customer databases need strong customer relationship management services. The further combination of artificial intelligence and data science will enhance customer services for businesses regardless of their sizes. Businesses can use virtual AI assistants to improve customer service metrics.

Productive Project Management with Data Science 

The positive impact of the evolution of data science won’t just be limited to marketing and customer service departments. It will also expand to internal project management and operational departments.

The progress across data science automation technologies allows businesses to track productivity metrics, employee work satisfaction rates, and overall workplace efficiency. This will help increase workplace productivity and help managers launch efficient project management strategies.

Data Science Job Outlook

The job outlook for data science occupations is positive, with the US Bureau of Labor Statistics reporting a 22 percent growth in employment from 2020 to 2030. This positive outlook is also reflected across other data-related occupations. According to the BLS, the projected job outlook rate for market research analysts is also 22 percent between 2020 to 2030.

It’s safe to say if you pursue a data science education, you will be on your way to a fruitful and future-proof career. The skills you can learn in data science will translate to many different industries, making you an attractive candidate for companies.

How Much Money Will Data Scientists Earn in the Future?

Determining how much money data scientists will earn in the future relies on your educational qualifications, specific job role, company, and the state you live in. According to the BLS, the average salary for data scientists was $98,230 in May 2020.

This average salary has increased since the previous financial year’s wage estimates. According to the BLS  May 2019 report, the average salary for data scientists was $94,280. This figure puts data science ahead of most occupations in terms of salary.

What Are the Best States for Data Science Jobs?

According to ZipRecruiter, the best states for data scientist jobs are New York, New Hampshire, California, Vermont, Idaho, Massachusetts, and Wyoming. ZipRecruiter reports that data scientists earn an average salary of $132,826 in New York, the highest-paying state. In the lowest-paying state of North Carolina, the average salary was $87,662.

How to Avoid Becoming Obsolete: The Key to Future-Proofing Your Career as a Data Scientist

Find tips below to future-proof your career as a data scientist and avoid becoming obsolete in this highly competitive field. You can customize these tips to match your educational and professional qualifications.

Stay Informed About Latest Data Science News and Technologies 

The first tip to building a long-term data science career is staying up to date with the latest data science news and technologies. Join online data science communities and read about emerging AI and machine learning technologies.

Take Newer Data Science Courses 

The next tip to increase your candidacy and future-proof your data scientist career is to learn new and in-demand data science skills. Depending on your preferred educational route, you can enroll in online data science courses or attend the best data science bootcamps.

Make a Strong Data Science Portfolio

A data science portfolio will greatly increase your chances of being hired. You can add freelance projects sourced from Kaggle datasets, online courses, and school projects. Be sure to customize your projects to meet the industry skills specific to your data science job.

Does Data Science Have a Future?

Yes, data science does have a lucrative future with a high job outlook rate and ample career growth opportunities. The integration of automated AI technologies within the data science sector indicates that the future is bright for data scientists. Pursuing an education in data science can lead to a wide range of career opportunities across several industries.

You might work in market research, data analysis, FinTech analysis, or database management as a data scientist. The variety of industries you can work in include education, healthcare, and computer science.

Will Data Science Become Obsolete FAQ?