data science trends - top data science trends

Top data science trends to keep you informed.

Data science is constantly evolving and is expected to continue growing rapidly over the next several years. It has even led to the creation of fields within artificial intelligence like computer vision, deep learning, and natural language processing. We’ve raked through the most popular sources for technology and created a short list of the most recurring trends in data science this year.

Small data.

The format and volume of small data is what makes it so accessible for people, not just machines. Allen Bonde defines small data as what “connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged- often visually- to be accessible, understandable, and actionable for everyday tasks.” This trend will probably predict how we personalize technology and make data work for people, instead of making people work around data.


TinyML is machine learning that makes deep learning networks small enough for small hardware. It should be no surprise that TinyML has been trending because of the need for everyone to have a smartphone and maybe even a tablet. Keeping up with the different platforms and applications will tell us how machine learning will adapt to keep a whole world of information at the touch of our fingertips.

Synthetic data.

This fake “data” that is created by a computer program is used for training machine learning models. Using synthetic data can save you the time and money it takes to collect the large amounts of real data needed in order to train a model quickly.


According to Exploding Topics, the search for “deepfake” has increased by 18% alone in the past year alone, with a volume of 33,100 Google searches per month. With the ever-growing trend of young people loving SnapChat and TikTok filters, getting in on the ground floor of deepfakes now will allow you the ability to make the potential power of deepfakes work for you instead of against you.

Generative AI.

This type of AI can be unsupervised or semi-supervised in machine learning. The algorithm lets the computer use code, content, and data to make brand-new versions. This type of machine learning allows computers to have an understanding of how the real world works and predict outcomes. 


Automated machine learning is exactly what it sounds like. The increasing demand in AutoML allows people who don’t have expert knowledge in machine learning to use premade programs with ease.

Augmented analytics.

This technology will help with preparing data, providing insights, and explaining how the data can be analyzed and explored. The automation will help to speed up and streamline the process of analyzing data to assist experts and help clarify to the casual user.

Explore how you can make your data work harder.

Machine learning and artificial intelligence are developing faster and in more ways than we could imagine. We’re seeing lots of trends in automation and machine learning technology that can help itself even more than humans can. Keeping up with these data and analytics trends will help you prepare for what is soon to come in 2023. 

You can also gain real-world data science experience and keep up with the latest from the world of data science with the Data Scientist Nanodegree program.


Jennifer Shalamanov
Jennifer Shalamanov
Jennifer is a content writer at Udacity with over 10 years of content creation and marketing communications experience in the tech, e-commerce and online learning spaces. When she’s not working to inform, engage and inspire readers, she’s probably drinking too many lattes and scouring fashion blogs.