Top 9 Data Science Trends in 2024-2025

Though it might sound like a cliche, in 2024, data is just everywhere, and it's only expanding. To manage it, data science is a cutting-edge field that combines statistics, computer science, and domain expertise to extract meaningful insights. This is useful for companies across industries to predict current data science trends and make smarter business decisions.

In this article, Binariks will look into the recent growth of data science technology and top data science trends in 2024, both across industries and for specific fields like healthcare, insurance, and banking.

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Data science technology growth

The data science market, which includes platforms that help companies analyze massive volumes of data, is experiencing rapid growth. In fact, the market is projected to reach USD 322.9 billion by 2026, with a Compound Annual Growth Rate (CAGR) of 27.7% (Source ).

The increasing demand largely fuels this growth for data to drive decision-making across industries, along with other latest trends in data science.

• By 2025, there will be 181 zettabytes of data, which is way above what an average consumer can imagine (Source ). To put this into perspective, in 2013, the volume of data created was just nine zettabytes. This explains data science's crucial role in managing the expansive data that will grow larger.
• The adoption of big data analytics is widespread across various sectors. For instance, 56% of healthcare centers have adopted predictive analysis , with higher rates in some countries like Singapore (92%) (Source ).
• Many challenges come with handling vast amounts of data via data science. For reference, 43% of IT managers believe that the current IT infrastructure might not be sufficient to handle future data demands. This points to a growing need for advanced data science tools and technologies to process and analyze the burgeoning data volumes efficiently (Source ).
• About 87 percent of companies have increased their investment in data (Source ). This demonstrates that data science is a cross-industry phenomenon that is impossible to ignore.

9 emerging trends in data science for 2024-2025

Now, let's move to top data science trends defining 2024-2025 and the years to come. The nine recent trends in data science that Binariks team has carefully selected to be represented in this article are based on the current state of the market, the landscape of evolving technologies , and the demands of consumers.

1. TinyML

TinyML refers to implementing machine learning models on tiny, low-power devices like sensors and IoT (Internet of Things ) devices. This trend is significant for edge computing, where data processing occurs close to where it's generated. TinyML is a user-friendly way to process data quickly and competently.

2. Predictive analytics

Want to use data-driven insights for your best benefit? Predictive analytics is your best bet for an impeccable marketing strategy. Predictive analytics highlights the increasing use of machine learning and statistical models to predict future outcomes based on historical data.

For those wanting to anticipate market trends and potential consumer behavior, this is a data science trend to adopt in 2024. Risk assessment also benefited tremendously from predictive analytics .

Predictive analytics relies heavily on the availability of big data . Today, we have more efficient data processing tools capable of handling large volumes of data at incredible speeds, data visualization tools, and cloud computing, which are constantly developing.

3. AutoML

Automated machine learning is one of the new trends in data science. AutoML streamlines and automates the process of applying machine learning models. In this way, it becomes more available to non-experts and more efficient, leading to the democratization of data science.

Essentially, AutoML is ML plus automation and application to real-life problems. With this data science trend, professionals whose primary expertise is not ML have access to ML. The development of ML-based apps heavily relies on automated machine learning.

4. Cloud migration

In 2025, no tool for data storage is more scalable, flexible, and cost-effective than a cloud . Surprisingly, data migration is also quite budget-friendly, as there is no need to invest in additional physical infrastructure.

Therefore, approximately 44% of traditional small businesses utilize cloud infrastructure or hosting services. In contrast, this adoption is higher among small tech companies, with 66% leveraging these services. Enterprises show the highest adoption rate at 74%, and the numbers are only expected to grow (Source ).

Right now, the cloud migration market is one of the data science trends that is impossible not to notice. It is currently worth USD 232.51 billion and is projected to grow at a (CAGR) of 28.24% and reach 806.41 billion by 2029 (Source ).

5. Cloud-native

Cloud-native solutions are designed for cloud-computing environments. They are used to develop services packaged in containers. Unlike cloud migration, which refers to migrating data to the cloud, cloud-native technologies are designed for cloud environments.

Examples of these include microservices, containerization, and dynamic orchestration. Cloud-native technologies are one of the data science trends that participate in scalability and lead to faster development and deployment cycles. They are managed by DevOps technologies. Cloud-native technologies are one of the most popular trends in data science because they are cheaper than building on-premise infrastructure.

6. Augmented consumer interface

This data science trend refers to advanced, data-driven interfaces that enhance user experience through personalization and interactivity. AI and machine learning are both significant tools in creating augmented consumer interfaces.

Moreover, they are prone to using IoT , VR , and AR. These interfaces are expected to transform how we shop and interact, with potential applications in virtual reality shopping experiences and communication interfaces like Brain-Computer Interfaces (BCI)​.

An example of an augmented consumer interface is a virtual fitting room in an online retail store where customers can create an avatar based on their body measurements and overall looks.

7. Data regulation

In 2024, there is just so much data online that protecting data privacy is the top priority for every business, whatever it might be. This is especially true for data-sensitive domains like healthcare and insurance.

There are several new data regulation acts for new companies to watch for in 2024, including:

• State privacy laws in the USA in states including Montana Consumer Data Privacy Act, Florida Digital Bill of Rights, Texas Data Privacy and Security Act, Oregon Consumer Privacy Act, and Delaware Personal Data Privacy Act.
• In 2024, Canada will introduce the Consumer Privacy Protection Act (CPPA), the Personal Information and Data Protection Tribunal Act, and the Artificial Intelligence and Data Act (AIDA). You can expect enhanced individual control over personal data and more substantial penalties for non-compliance from these acts.
• In the EU, an ePrivacy Regulation (ePR) finalized in 2024 will establish regulations on cookie usage and apps like WhatsApp and Facebook Messenger.
• 2024 will see the long-awaited enactment of the one-of-a-kind AI Act, which is expected to be a general EU legislation that brings a category-based approach to different types of artificial intelligence.
• Digital Services Act (DSA) is an upcoming EU regulation that defines legal and harmful content that can be removed from digital platforms.

Naturally, new legislative acts will persuade businesses to audit their current processes in alignment with the new legislation.

8. AI as a Service (AIaaS)

AI as a service is one of the data science industry trends that allows your company to implement newly emerging AI technologies like OpenAI GPT4 and Google Bard without significant investments. Many of these open-language models make their APIs available to the general public. Businesses can create learning frameworks and chatbots based on the existing language models to cater to their needs.

9. Python's increasing role

Python is the primary programming language for data analytics. If you pursue an engineering job in data science in 2025, this is the language to learn now. Python's role in data science continues to grow due to its versatility and the extensive range of libraries available for data science and machine learning.

Popular examples include Pandas and Scikit-learn. Python is attractive because it is also increasingly used in diverse fields beyond its traditional applications, such as 3D game development and bioinformatics.

What data science trends will be widespread across industries?

Aside from the data science future trends that will undeniably rule most industries, some trends are more industry-specific than those due to their specific benefits. Let's focus on the benefits for the domains in which Binariks has immaculate experience.

Medtech (medical technology)

In medicine, the most critical aspect is to make professionals benefit from the technology and make it a tool that assists them in decision-making and makes everything more accurate and fast. However, this is a hanging balance for stakeholders to maintain, as doctors and caretakers should not over-rely on technology.

• Data democratization

Data democratization is one of the emerging trends in data science that caters explicitly to medical technology simply because medical establishments have medical and non-medical staff who must be educated about technological advancements for everything to work. Knowledgeable doctors and nurses enhance patient care through informed decision-making.

Example: Large frontrunner companies like Philips and Siemens Healthineers use data science to improve diagnostic tools and patient care. Third-party companies like Tata Consultancy Services (TCS) assist medical companies in making healthcare data accessible.

• Explainable artificial intelligence

XAI is a type of AI in which humans get to keep intellectual oversight over their output. Unlike a traditional AI, XAI helps pinpoint where and how a model might go wrong or where biases exist. In MedTech, these types of AI can and will assist in treatment and decision-planning. More effective time spent on diagnosing means more time for actual treatment and room for patient satisfaction and better outcomes.

Example: IBM Watson Health uses XAI in the decision-making process.

Insurance

Insurance as a sector moves towards faster detection of issues and automatizing some basic human interactions so that professionals can focus on more comprehensive tasks.

• Data unification

Consolidating data from various sources helps insurance companies assess risk and process claims better. It is also a step towards reconciliation.

Example: Companies like Progressive and Allstate use data unification for personalized insurance premiums and fraud detection.

• Graph analytics

Graph analytics are used to detect fraud patterns and understand customer networks to tailor insurance products​.

Example: Large financial institutions used graph analytics for fraud detection and risk assessment.

• Large language models

LLMs transform customer service and claims processing by automating interactions and analyzing customer feedback more effectively.​ They can also help with fraud detection and risk assessment.

Example: Most large banks now use large language models, including JPMorgan Chase and Bank of America.

Financial services

The latest trends in data science mainly focus on processing large amounts of data.

• Data-driven consumer experience

Banks increasingly use AI to personalize banking experiences . For instance, they recommend financial products or advise on investments ​​.

Example: Banks like Wells Fargo and Bank of America use data-driven consumer experience in their expertise.

• Adversarial machine learning

Adversarial Machine Learning (AML) is a relatively new field in AI that focuses on the security aspects of machine learning systems. This is especially useful in areas like fraud detection and algorithmic trading​​.

Example: JPMorgan Chase employs adversarial machine learning to safeguard its AI systems.

• Data fabric

A data fabric is one of the data analytics trends that is an architecture and set of services that provide consistent data management across various environments. Managing and analyzing large, complex datasets is vital for banks to gain real-time insights for better decision-making and risk management​​.

Example: Large banks like Citibank or HSBC use data from different sources and integrate it into a cohesive platform. Such data include transaction records, customer interactions, and market analytics.

Binariks offers data science services based on the latest data analytics trends, including steps like:

• Business analysis
• AI solution project planning
• Data preparation
• ML modeling and output integration
• QA , user training, and support

Conclusion

As more and more data is being created, trends in data science will evolve to focus on capacity and innovation. However, helping people handle information will remain at the core of data science future trends. Throughout the 2020s, we will look into improved data processing technologies and enhanced analytics tools. Data science also needs talent to develop innovative solutions and data analytics trends.

For now, the data science technology trends covered in this article clearly show what to adopt if your business is only hoping on data science trends or perfecting your strategies. The stats demonstrate that no company can ignore data, no matter the industry or how large or small that company is. A capable tech partner like Binariks can help you integrate the right data science trends for your company.