future of data science

The early part of the 21st century that we are in right now is dominated by big data. Thanks to the digital platforms, smartphones and IOT we now generate more data than we possibly could have imagined a decade ago. In fact, cloud vendor Domo estimated that the average person in 2020 generated about 1.7MB of data every second! Therefore there will always be more data than we know what to do within every industry and organizations are catching up to utilize it to the full extent. For example, the recommendation engines used by eCommerce is one of the places where data is used to analyze customer behaviour and build recommendation charts to increase sales and give the buyers what they want. Data gathered for different sources will be useful in industries such as medicine, finance, government, marketing, business management, manufacturing and energy to name a few. Therefore the future of data science has a broad scope for all industries and those who want to build a career in it. Skilled talent is the need of the hour and the need of the future for companies building technology and hiring those who can work with advanced systems built out of artificial intelligence. 

Trends in Data Science 

Although the core concepts of data science have been around for a while, recent technological advancements have made it possible to harness data and its usefulness.

Here are the trends in data science: 

Business Insights from Big Data

The data generated and stored for years and the data that is constantly being captured offer incredible business insights that help organizations improve their reach, optimize their processes and increase their returns on investment. Marketing experts can use data obtained through analytics, researching trends and reports from social media searches and engagements. Data scientists break down the volumes of data that arrive into observable metrics and figure out things such as where the conversions are happening the most, the kind of content customers regularly interact with, the most effective method needed to reach a demographic and eliminate efforts that have a low return on investment. 

Data Science in Manufacturing

The second industry that is reaping huge benefits from data science is manufacturing. Analysis of gathered data has revolutionized manufacturing operations, cut down redundancy, optimized production rates, improved yields in manufactured goods, reduced errors in supply chain forecasting and many other aspects related to the industry. Companies employing automation, data mining and machine learning have boosted their efficiency including their competitive advantage and reduced supply chain risk. Data analysis is also highly useful in predictive maintenance and reduction of losses in terms of unforeseen downtimes. It has also brought about improvements in post-sale services and customization of products. 

Real-Time Data Analysis 

Several industries make good use of data analysis in real-time such as medical diagnostics and the logistics industry. As more data has been historically collected and analyzed, it has helped data scientists come up with accurate predictive models that can be applied in real-time applications. In hospital environments, real-time data analysis can mean the difference between life and death in special circumstances or reduce the individual workloads of staff and nurses. In the logistics industry, real-time data improves predictive times for shipments, avoids delays and downtime on critical assets and contributes to improving the performance of vehicles by insights into operation methods. While the data gathered is different in a different industry, it helps greatly with automation and performance improvements when combined with insights from data scientists in terms of safety, pricing, profit margins and other variables.  

What the future is expected to bring in Data Science?

Now that we know the potential of data science beyond what is already being implemented, these are some of the things that can be expected from the future: 

AI and Machine learning will gain dominance  

Probably the most powerful technology data scientists will have to work with is Artificial Intelligence (AI). It could also be said that the future of data science will eventually align itself in the pursuit of making it better. Artificial Intelligence is no longer a matter of science fiction and is already assisting in making business decisions and running operations. At the practical level, Artificial Intelligence will apply automated solutions to run through tremendous amounts of data sets to gain insights to make better business decisions or to help the business leaders of tomorrow in decision making. It is already expected to become a key factor according to one 2018 survey by Deloitte. The survey showed that over 65% of businesses believe that AI will give them an advantage over the competition while 9% of the business believes it will give them a major leap ahead of their competitors. 

AI applications such as machine learning and deep learning have already had their shining moment in outperforming the best of the human decisions makers in some areas. These systems have developed capabilities and improve their performance over time by themselves without needing human intervention or programmed instructions. They are leaps and bounds ahead of the automation that we have today in terms of giving deeper business insight. One example of it is the AlphaZero, the product of Google’s DeepMind unit that is a self-learning chess machine. AlphaZero learns from its actions to come up with different strategies to reach its goal. Instead of the “brute force” mode of operating in machines that preceded it, AlphaZero was observed declining a chance to consider opponent pieces so it can set up a better positioning. 

Increased adoption of AI in business 

In the last decade, data mining and preparation techniques took up much of the spotlight in the discipline insights gleaned by those significantly improved business decisions. However, they are nowhere close to the disruption that AI methods are about to bring in the upcoming decade. AI can dramatically improve the efficiency of businesses and their process and also offer major advantages in managing customers and client data. One region it will disrupt will be customer service where AI and its greater access to data on the customers will replace human operators on the front. While it could prove challenging for smaller companies with their limited budgets and finances, organizations capable of deploying it will see major pay-offs for their investment. 

Automated machine learning models is the second component of AI that can learn by itself and transform business functions through better data management and analytics. This will also free up data scientists to work on bigger technology such as deep learning. 

The tremendous growth in Data Science jobs 

While IT-focused jobs have been all the rage over the last two decades the rate of growth in the sector has been projected to be about 13% by the Bureau of Labor Statistics. It is still higher than the average rate of growth for all other sectors. However, data science has seen an explosive growth of over 650% since 2012 based on an analysis done on LinkedIn. The role of a Data Scientist has catapulted forward to one of the most in-demand jobs and ranks second to machine learning engineer- which is a job that is adjacent to a data scientist. 

The high demand for data scientists comes from the need of big companies to mine their data for insights and process optimization at every level. The C-level executives who are in charge of the IT decision making place data analytics and business intelligence as one of the most critical technologies needed by companies. Consequently, data skills have been ranked as one of the most sought-after in all industries.  

More responsible AI that is faster and smarter 

According to Gartner, AI will make a significant leap from piloting to operationalizing by 2024 with about 75% of the enterprises having a program. This would mean a five-fold increase in the analytics infrastructure and data streaming. The technology has already shown tremendous capability in modelling the spread of the coronavirus and the effect of the countermeasures using techniques such as machine learning and natural language processing. A number of other AI techniques such as distributed learning and reinforcement learning are able to build systems that are highly flexible and adaptable in making business decisions. 

AI systems that are more responsible enable transparency which can protect against poor decisions. Advancements in hardware such as neuromorphic architectures can take away workloads from centralized systems which consume high bandwidth. They also allow for scalability in AI solutions that can make it available to a wider range of businesses and have a greater impact on the world. Responsible AI models also ensure better collaboration between humans and machines that will ensure better adaptations in organizations. 

The future for Data Scientists

The future of data science for professionals in the field is promising as they learn to work with advanced AI methods and technology. This is because although the technology is developing at a rapid pace with high rates of integration with businesses, a major obstacle organizations face is in the form of the talent that drives the AI initiatives. This study by EY and MIT Technology Review points out that the biggest barrier to the adoption of AI by companies was slowed hiring because of the lack of skills in those who have the qualifications. In fact, about 80 percent of respondents said that they did not have the skills to manage AI programs. 

The path to becoming a data scientist

There is a broad scope for data science in India with the newest generation heavily inclining towards it. Although there is no specific degree, the path to it for many starts out in software engineering. Coding proficiency that is gained through bachelor’s or even master’s level of study can significantly improve the chances of graduates to build a promising career. Taking up a data science course along with it can significantly boost job prospects and help professionals transition into subjects experts who are ready for the roles with an industry-relevant certification. Since it is a field where skills matter more than the degree there are a few requirements that need to be checked off before considering becoming a data scientist: 

  • Having a degree in computer science or a parallel or related stream. 
  • Must have some proficiency in coding and working with software such as Python, Hadoop, Pig, SQL and others.
  • Should have great leadership and business skills. 
  • Have a deep understanding of mathematics and algorithms. 
  • A drive to solve problems and understand broader contexts in data. 

Essential skills for a data scientist

  • Math: Linear algebra, calculus and statistics. Data science models are all based on numbers and large volumes of it. Therefore understanding statistics is key to learning how to crunch the numbers. Calculus is also a requirement as the numbers are always varying and many of the results involve finding the differential. Other essentials also involve but are not limited to cost functions, scalars and vectors, matrix and tensor functions, gradients and derivatives. 
  • Handling data: All data that comes to organizations is in the raw form which needs to be converted to numbers for further examination. Then there is the step of mapping the data and cleaning out the noise which doesn’t contribute. Data handling is in itself a skill for which data scientists need to know the tools and techniques. 
  • Cloud computing: Cloud computing is fast replacing centralized systems in terms of both data storage and computing power. Cloud computing and data science are almost inseparable now as the cloud platforms such as AWS, Google Cloud and Azure are used extensively in the industry. Data scientists need to be familiar with using the products offered by cloud services for their daily tasks with data such as examination of data, visualization and other tasks. 
  • Communication Skills: Having strong communication skills is an absolutely essential non-technical skill that data scientists need to have. As they grow in the role they will need to interact with internal teams and stakeholders to communicate effectively and lead projects.  


The future of data science is promising for those with the right skill set pursuing it as a career. It is set to revolutionize many sectors such as health care, transport, business, finance and manufacturing industries through Artificial Intelligence and automation. 

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