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Data Science Latest Technology Advancements,Growth and Future Scope

Data Science is a buzzword in the technology world right now and for good reason, it represents a major step

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Data Science is a buzzword in the technology world right now and for good reason, it represents a major step forward in how computers can learn. The need for Data Scientists are high in demand and this surge is due to evolving technology and generation of huge amounts of data aka Big Data. So, Let’s discuss the Future Scope of Data Science in the following order:

What is Data Science?
  • Data Science’s Contribution to the Future
  • Future Scope of Data Science
  • Data Science Careers

What is Data Science? 

Data science is referred to the process of collecting, storing, segregating and analyzing data which serves as a valuable resource for organizations to carry out data-driven decision making. It is often used by highly skilled computing professionals.

future-of-data-science-edurekaData Science exists everywhere, to be honest, every exchange and interaction on any technological domain includes a certain set of data, be it Amazon purchases, Facebook/Instagram feed, Netflix suggestions or even finger and facial recognition facility provided by phones.


Amazon is a key example of how data influences all our lives and shoppers particularly. Its data sets store every buyer’s data; what you have bought, the amount paid and your search history is all remembered in Amazon’s system by virtue of data collection. This greatly enables Amazon to personalize and customize its homepage according to your preferences and shopping history.

Data Science’s Contribution to the Future

Data Science encompasses many breakthrough tech concepts like Artificial Intelligence, Internet of Things, Deep Learning to name a few. With its progress and technological developments, data science’s impact has increased drastically.

The importance of gathering and collecting data is crucial as it enables retailers to determine and thus influence our purchasing habits. Hence, it exercises major control through its purchasing power.

Future Scope of Data Science

Let’s have a look at a few factors that point out to data science’s future, demonstrating compelling reasons why it is crucial to today’s business needs.

  • Companies’ Inability to handle data

Data is being regularly collected by businesses and companies for transactions and through website interactions. Many companies face a common challenge – to analyze and categorize the data that is collected and stored. A data scientist becomes the savior in a situation of mayhem like this. Companies can progress a lot with proper and efficient handling of data, which results in productivity.

  • Revised Data Privacy Regulations

Countries of the European Union witnessed the passing of the General Data Protection Regulation (GDPR) in May 2018. A similar regulation for data protection will be passed by California in 2020. This will create co-dependency between companies and data scientists for the need of storing data adequately and responsibly. In today’s times, people are generally more cautious and alert about sharing data to businesses and giving up a certain amount of control to them, as there is rising awareness about data breaches and their malefic consequences. Companies can no longer afford to be careless and irresponsible about their data. The GDPR will ensure some amount of data privacy in the coming future. 

  • Data Science is constantly evolving

Career areas that do not carry any growth potential in them run the risk of stagnating. This indicates that the respective fields need to constantly evolve and undergo a change for opportunities to arise and flourish in the industry. Data science is a broad career path that is undergoing developments and thus promises abundant opportunities in the future. Data science job roles are likely to get more specific, which in turn will lead to specializations in the field. People inclined towards this stream can exploit their opportunities and pursue what suits them best through these specifications and specializations.

  • An astonishing incline in data growth  

Data is generated by everyone on a daily basis with and without our notice. The interaction we have with data daily will only keep increasing as time passes. In addition, the amount of data existing in the world will increase at lightning speed. As data production will be on the rise, the demand for data scientists will be crucial to help enterprises use and manage it well.

  • Virtual Reality will be friendlier 

In today’s world, we can witness and are in fact witnessing how Artificial Intelligence is spreading across the globe and companies’ reliance on it. Big data prospects with its current innovations will flourish more with advanced concepts like Deep Learning and neural networking. Currently, machine learning is being introduced and implemented in almost every application. Virtual Reality (VR) and Augmented Reality (AR) are undergoing monumental modifications too. In addition, human and machine interaction, as well as dependency, is likely to improve and increase drastically.

  • Blockchain updating with Data science

The main popular technology dealing with cryptocurrencies like Bitcoin is referred to as Blockchain. Data security will live true to its function in this aspect as the detailed transactions will be secured and made note of. If big data flourishes, then Iot will witness growth too and gain popularity. Edge computing will be responsible for dealing with data issues and addressing them.

Future Scope of Machine Learning.

The essence of intelligence is learning. Machine learning (ML) is a subset of AI that focuses on computer programs capable

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The essence of intelligence is learning. Machine learning (ML) is a subset of AI that focuses on computer programs capable of parsing data using specific algorithms. Such programs modify themselves without human intervention, generating the desired output based on the analyzed data. In essence, using ML techniques, the machine is trained to analyze large amounts of data and then learn to perform certain tasks.

Machine learning, an important subset of artificial intelligence, allows computers to learn from data analysis. While ML technology is expected to play a significant role in the future of many business types, it is poised to have a more significant impact on some industries than others.

Below are the IT sector industries that are most affected by machine learning and look forward to further integration of these industries and AI and ML in the coming years.

1. Advertising

Almost all machine learning algorithms depend on huge amounts of data. The one industry that collects data from almost all of our interactions with the internet is advertising. Additionally, steep competition in the area creates demand for better and better algorithms. Some companies are already able to predict what we’d like to buy before we know it ourselves.-Robert Krajewski,Ideamative

2. Housing Construction

The housing construction industry is prime for reaping the benefits of mainstream AI/ML. Consider the housing construction process—the selection process of interiors, the renovation process, choices of selections and smart technologies. There is a wealth of data that are generated through these that can be harnessed to make smarter decisions and benefit the industry as we apply advanced analytics using AI. – Sudip Shekhawat, Interior Logic Group.

3. Credit Card Security

The discovery of fraudulent patterns in credit card transactions is one area where ML can have a huge impact as the main problem is going through so much data. Areas like medicine are not like that. The disadvantage of a faulty diagnosis in any direction is serious. It is important to associate ML with problems where it is appropriate. – Mike Lloyd, Redseal

4. Manufacturing

Manufacturing will be most affected by machine learning, especially as it relates to strengthening the global supply chain. Mass shortages of PPE equipment, disinfectant products and toilet paper showed the need for accelerated digital transformation, with the widespread use of ML helping data-driven businesses improve manufacturing operations from the first concept to final delivery. – Ram Chakravarti, BMC Software 

5. Banking And Finance

Global cloud migration of apps and content repositories continues and will change how data and content are indexed by enterprise search engines. This will enable big data and AI, thanks to on-demand processing and optimized storage costs. AI’s appetite for content and user behavior data will grow and drive a virtuous circle of user experience improvements, particularly in banking and finance. – Alexandre Bilger, Sinequa

6. Healthcare

As patient electronic medical records become universal, ML predictive analytics can be used to detect and prevent potential health issues before those issues actually present themselves. The better IT hardware products on the market already do that to eliminate downtime, and we should be doing it more broadly to help humans avoid health problems. – Ken Steinhardt, Infinidat

7. Consumer Goods

Consumer goods continue to be subject to real-time demand, so any investments in ML-assisted improvements in quality or distribution will be quickly recouped. Flawed products will be recognized during manufacturing, and predictive distribution will result in shorter delivery wait times and better-stocked retail, leading to higher customer satisfaction. – Luke Wallace, Bottle Rocket

9. Humanitarian Aid

Humanitarian nonprofit organizations stand to gain incredible insights into their missions through machine learning. Whether through the identification of at-risk refugees or solutions for time-sensitive problems, machine learning is poised to revolutionize the way nonprofit organizations operate. – Tal Frankfurt, Cloud for Good

10. Insurance

Insurance companies will take full advantage of machine learning to fine-tune their risk calculations so they always come out ahead—they’re already doing this. I can’t say whether this will work out in favor of the consumer until every insurance company adopts machine learning and they must compete again on price. – Vaclav Vincalek, Future Infinitive

11. E-Commerce

E-commerce has tremendous potential for growth with the help of machine learning technology. When e-commerce meets the street, plans go awry. Machine learning applications can prevent delays and ensure packages ordered online reach their destinations on time by sifting through big data and combinations of zip codes, package sizes, times of day, congestion and even the weather. – Adi Ekshtain, Amaryllis Payment Solutions.

12. Contact Centers

Contact centers are currently leading the charge when it comes to implementing ML. Adapting to Covid-19 has been particularly important for customer-facing organizations that need to find automated ways of interacting with customers. AI can help human agents deal with increased traffic while providing data insights to enable continually increasing personalization of the customer experience. – Martin Taylor, Content Guru

13. Copywriting

Sectors that require extensive copywriting—such as financial services, pharmaceuticals and e-commerce—will be most affected by ML. With all the advancements in natural language understanding, natural language generation and natural language processing, it’s logical to go for the next big thing, in which augmentation of human creativity by software is a must. – Robert Weissgraeber, AX Semantics.

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