Top Upcoming Data Science Trends for 2023

It’s that time of the year that excites all tech enthusiasts around the world. As data scientists, we read articles about the industry, consume videos and podcasts on the topic and immerse ourselves in this domain all through the year. And as experts, we also take pride in ‘visualizing’ specific trends for an upcoming year based on the events and occurrences of the current one.  

This topic has always fascinated me, and I can’t wait to share the trends that we as data scientists foresee for the year 2023.  

Let’s start.  

More Sophisticated Data Visualization Techniques 

Scenario development and storytelling are going to be used prominently in 2023. According to Fortune Business Insights report, the market worth is expected to be $19.20 billion at a compound annual growth rate of 10.2% 

Usage of smartphones, smart televisions, dynamically increasing use of the internet, the steady and significant rise of Artificial Intelligence usage, the Internet of Things, Cloud Computing are all leading to rise in Data Visualization. 

Besides, the tools we predict to trend in 2023 include: 

  • Power BI 
  • Tableau 
  • Zoho Reports  
  • And Dundas BI 

People are generally more inclined towards information that is presented visually and no doubt, it is easy to learn and implement. 

Hence, we can say that we need to prepare more sophisticated data visualization techniques in 2023, as data scientists strive to communicate their findings to a wider audience.  

Data-as-a-Service (DaaS) 

The DaaS market is projected to reach 12 billion by the end of 2023, at a CAGR of 39%. One of the main users of DaaS is Microsoft. DaaS makes data sharing an effortless task. The financial demand for DaaS is increasing day-by-day thanks to the availability of the right resources and the affordability in data storage. 

Data Science in the Blockchain 

Blockchains are used as decentralized, distributed, write-only databases that run on peer-to-peer computer networks. Traditionally, blockchains have been used for keeping records of cryptocurrency transactions for example, for example, Bitcoin. However, nowadays, this technology is gaining many other use cases. By 2024, corporations are estimated to spend $20 billion per year on blockchain technical services.  


AutoML can be used by Non-Machine ML experts. AutoML makes the whole process of Machine Learning easy, reducing the iterative tasks. It makes the task less laborious. Tools like Azure ML and Data Robot help clients analyze seamlessly with less coding work. AutoML is more efficient and scalable as well. AutoML is definitely in demand in 2023.   

Machine Learning-as-a-Service 

Machine Learning-as-a-Service (MLaaS) is a cloud-based model where companies outsource machine learning work to an external party. Using MLaaS, companies can implement Machine Learning without a significant upfront investment of budget and resources. Cost of entry is less and that is the reason there are fewer barriers in MLaaS. In the year 2016, the global market share was around $571 million, and is projected to reach at $5,537 million by 2023. This is growth at a CAGR of 39.0% from 2017 to 2023. 

Final Thoughts 

The coming weeks and months sure do look exciting for not just data scientists but for the entire tech spectrum out there. As days pass, newer experiments are being made, leading to the onset of super-niche processes, implementations and workflows. Do you see a trend for the upcoming year? We would love to know.  

Share them in the comments.

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