Interesting predictions on data science trends for 2024

Data science utilizes mathematics, statistics, and machine learning to analyze and extract knowledge from data, enabling industries to anticipate customer needs, make business decisions, and automate operations. However, the field of data science has evolved tremendously over the last decade. As the world grows more data-driven, businesses and organizations must adapt to stay current on data science trends and innovations.

The recent trends in data science have enhanced the working of every industry and have opened numerous job opportunities for skilled data scientists all over the globe. Let’s explore forecasted trends in data science for 2024 to understand what it has for the future.

Recap of 2023 Trends

• Artificial Intelligence

The AI market is anticipated to reach USD 407 billion by 2027. It will allow more accurate forecasts, lower the amount of time we spend on boring and repetitive labor like data collection and purification, and empower workforces to act on data-driven insights.

• Real-Time Data and Analytics

The capacity to analyze real-time data has become a critical growth enabler for multinational firms. The worldwide big data analytics industry is anticipated to exceed USD 655 billion by 2029. Social media platforms such as Facebook track real-time data and analyze data for advertising hundreds and tackling the spread of fake news.

• Data Governance and Regulation

Data governance is the process of controlling data availability, accessibility, integrity, and security in corporate systems, using internal standards and regulations to manage data consumption. According to records, just 3% of data in an average company fulfills quality criteria. Thus, data governance is becoming increasingly important as firms meet tighter data privacy requirements and rely more on data analytics to enhance operations and drive business decisions.

Prediction 1: Enhanced Integration of AI and Data Science

As data becomes more and more accessible and diversified, data scientists will need to rely on advanced analytics techniques and AI algorithms to acquire useful insights. The worldwide artificial intelligence industry is expected to reach USD 1,811.8 billion by 2030.

Healthcare is one of the most promising fields where future AI and data science will leave an everlasting impact. The healthcare RPA (robotic process automation) market is projected to reach USD 6.2 billion by 2030, with a CAGR of 26.01%, compared to USD 2.9 billion in 2022.

AI algorithms and data-driven insights are also useful in assessing individual student progress, identifying learning gaps, and recommending individualized learning courses.

Prediction 2: Increased Emphasis on Ethical AI

Over the next decade, the emergence of AI will need a greater emphasis on responsible practices, the creation of ethical frameworks, and the incorporation of ethical issues into AI development processes. As per reports, in an average firm, 21% of all files are accessible to anybody. Data scientists who work with large and frequently sensitive information must follow particular ethical guidelines to guarantee that their work respects human rights, societal standards, and overall justice.

By 2024, it is expected that 75% of the worldwide population will have their data secured by privacy legislation. Industrial manufacturers are the fastest adopters of AI ethics policies, with 89% of respondents stating they already have such policies, followed by retail and tech industries.

Prediction 3: Rise of Explainable AI

AI systems are rapidly being utilized to make judgments that directly influence people’s lives. As AI systems become more complex and influential, the demand for explainable AI increases. According to the NMSC, the worldwide explainable artificial intelligence (XAI) market was worth USD 51 billion in 2022 and is expected to grow to more than USD 24 billion by 2030.

Recent trends in data science and explainable AI (XAI) seek to develop models and algorithms that provide obvious reasoning for their findings. It will allow us to correct problems, enhance system performance, and avoid repeating blunders. AI transparency can assist in overcoming bias and fairness concerns.

Prediction 4: Quantum Computing’s Impact on Data Science

The quantum computing market is estimated to be the greatest contributor to quantum technology, with a projected revenue of USD 93 billion by 2040. When quantum algorithms are used for data analysis tasks, they provide an intriguing connection between quantum computing and data science. When dealing with huge, dispersed data sets, quantum computing provides rapid detection, analysis, integration, and diagnosis. Quantum computing’s ability to handle complicated calculations is revolutionizing data science.

According to a Capgemini poll, more than 40% of firms in the telecom and governmental sectors are working on or want to work on quantum technology. Also, the bulk of the players working and developing in quantum computing are startups (40%) and institutions (33%).

Prediction 5: Democratization of Data Science

Traditionally, data access was restricted to a small set of people, such as data analysts or IT specialists. The lack of inclusion resulted in a power imbalance. Data democratization challenges this power imbalance, making data more understandable and allowing everybody to access it. This movement is all about fostering a culture in which everyone utilizes data to make decisions, resulting in more ideas and cooperation throughout the organization.

Over the following 5 years, up to 2025, worldwide data generation is expected to exceed 180 zettabytes. Making all this data accessible is, thus, important. With the rising data, even non-technical business users or individuals, such as marketing analysts, may seek access to the required data.

Prediction 6: Continued Growth of Edge Computing in Data Science

The edge computing market, valued at USD 1.734 billion in 2017, is expected to reach USD 16.557 billion by 2025, with a 32.8% CAGR from 2018 to 2025. The rise of edge computing is changing the way data is handled and evaluated.

Edge computing involves processing data closer to its source, lowering latency, and improving real-time analytics. Its potential in data science will be fully realized during the next decade. Edge computing can enhance retail stores’ customer experience by enabling faster checkouts and processing facial recognition payment data. It can also aid wildlife protection in remote areas by processing real-time poaching alerts.

How to become future-ready with MAHE

Thus, let’s delve into the future of Data Science with Manipal Academy of Higher Education MAHE. The M.Sc in Data Science by MAHE caters to industry demands and is designed specially to instill analytical and technological skills into learners.

• MAHE’s curriculum is devised to reflect the latest advances, allowing students to keep up with new technology and trends.
• MAHE’s MSc program caters to students of all backgrounds, introducing technical aspects from scratch to help them grasp fundamental concepts before studying advanced topics.
• MAHE trainers are highly informed and committed to making a positive difference in students’ lives by supporting them to succeed in their chosen professions.

Conclusion

Data science is a multidisciplinary field that leverages digital data to revolutionize enterprises, enabling informed decision-making through technology and techniques for extracting information from massive data volumes. Recent trends for data science suggest that India’s data science employment market is quickly expanding, providing fascinating prospects for individuals in the sector. Ready to study up and face these trends head-on? Enroll in MAHE and watch your career rise.