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What Is The Future Of Data Science? Will It Remain In Demand?

What Is The Future Of Data Science? Will It Remain In Demand?
Data Analytics / Data Science / Data Security / General

What Is The Future Of Data Science? Will It Remain In Demand?

What is the future of data science? Are there opportunities to explore in the coming years to better not only business but even our lives?

Data science refers to collecting, storing, sorting, and analyzing of data to obtain valuable insights that an organization can execute in decision-making. Harvard Business Review called data science ‘the sexiest job of the 21st century.’

Most of the data science industry applicants are from the STEM field. This means the space will have solid experts who understand how to interpret data for better decision-making.

Here is what the Chief Scientific Officer of TetraScience; Michael Tarselli, had to say about getting into data science;

“Ten years ago, you would have had to go down a very specific track and make a structured career decision to end up in data science,”

He added, “Nowadays, scientists are coming out of school and saying, ‘You know what? I can do an end-around on this. I can do a one-month data science boot camp and school myself up quickly on Python, recursive logic, or neural networks, and then boom, they are a leading candidate for us.”

What is the future of Data Science? Data Science’s Contribution to the Future

Data science is engrained across industries. The argument that it might become obsolete is null and void. However, some technologies will enhance their contributions to the future. They include;

Internet of Things( IoT)

The growth of the internet of things means an increase in data and datasets, which will require more data scientists to make sense of the data.

For instance, in manufacturing, companies can use the data generated by the IoT sensor devices in their machines to improve their operational systems.

“With the introduction of comprehensive, real-time data collection and analysis, production systems can become dramatically more responsive,”  says consultants at Mckinsey.

Machine Learning ( ML)

Automated machine learning ( AutoML) is possible using a given data set. Data scientists are tasked with completing the life cycle of data that builds an AI model.

AutoML is not meant to replace data scientists buy to increase their demand. The early stages of AutoML data cleansing and visualization require experts in data science to prepare the data.

Synthetic Datasets

Synthetic data is created digitally rather than collected from or measured in the real world. It’s annotated information that computer simulations or algorithms generate as an alternative to real-world data.

In June 2021 report, Gartner predicted that by 2030, most of the data used in AI will be artificially generated by rules, statistical models, and simulations. Now this is a classical prediction if you are still wondering on what is the future of data science.

Future scope of data science

Data science is applicable in many areas and even more fields are emerging or improving the use of data science.

Virtual reality will be friendlier.

VR is often likened to gamers. However, you can do more with your data using virtual reality. An example company is which allows you to ‘step in your data.’

The visualization of data helps users better understand it and recognize patterns. AR and VR help users interact with the data to make it easier to glean insights. It isn’t often possible to see crucial information such as data clusters at the intersection of several dimensions in traditional 2D data visualizations.

Since most data sets are multivariate, virtual reality enables better data visualization beyond charts, graphs, and reports. It allows data scientists to see everything at once for better decision-making.

Blockchain updating with data science

The demand for data scientists to make sense of data is constantly growing. Some data challenges that they face without blockchain technology include;

  1. Data privacy – This is the biggest hurdle for data scientists. The need to distribute data for analysis in a secure way is still a challenge. And with more and more countries adopting data privacy legislation such as General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), getting access to data is becoming difficult for data scientists.
  2. Real-time data analysis is not accessible using traditional data management systems. This limits data scientists from analyzing data in real-time.

Since the emergence of the secured electronic cash system of digital currency using cryptography protocols, blockchain technology demand has been increasing. The technology solves most of the challenges data scientists face;

  • As a distributed system, blockchain maintains multiple data instances rather than a single copy. This enables blockchain to prevent data tampering and revision since data authenticity can easily be verified. The blockchain retains a unique “fingerprint” for each of its blocks.
  • Blockchain protects data privacy with its particular protocols while still allowing data scientists to utilize the data. There are various ways blockchain can help data scientists access privacy-protected data for their endeavors.

Data Science Careers

Data science careers go beyond data analysis, programming, or mining. The data science field is interdisciplinary across different healthcare, retail, and agriculture industries.

The path to data science is pretty straightforward if you have a STEM bachelor’s. However, you can switch from any field to data science as there is more that goes into becoming a data scientist lie creative and critical thinking.

Some of the top data science careers include;

Data Scientist With an average salary of $117 212, a data scientist must be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions.

Data Architect – With an average salary of $118,868, they should ensure data solutions are built for performance and design analytics applications for multiple platforms.

In addition to creating new database systems, data architects often find ways to improve the performance and functionality of existing systems and work to provide access to database administrators and analysts. Existing systems and working to provide access to database administrators and analysts.

Business Intelligence (BI) Developer – With an average salary of $92 013, the BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions.

Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end user’s understanding of their systems.

The Future of Data Science

The demand for experts in data science, from data analysts, data architects, and data engineers, is continuously growing. Big data calls for these experts to make sense of the numbers for better decision-making.

For instance, in the Salesforce ecosystem, the experts who understand data scientists are at an advantage in helping clients get insights from the customer datasets that grow their businesses.

We have a comprehensive data science course that will equip you with all the necessary skills to master data science. Contact us or check our courses section to get started.