Industry Insights

Top 8 Data Science Trends for 2023

Written By Ryan Loftus | September 29, 2022

Called the sexiest job of the twenty-first century, data science is one of today’s most promising technical disciplines. So promising, in fact, that Google CEO Sundar Pichai compared data science’s ongoing development of artificial intelligence (AI) to the discovery of fire and electricity.

In the coming decade, data science will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works. Here are the eight data science trends that will drive that transformation in 2023.

What is Data Science?

Companies of every size and industry need data to make informed business decisions. Doing so requires technical professionals who use statistics and data modeling to unlock the value in unprecedented amounts of raw data. Data scientists use statistical analysis, data analysis, and computer science to transform this unprocessed data into actionable insights.

On a more technical level, the core job responsibilities of data scientists include:

  • Writing code to obtain, manipulate, and analyze data
  • Building natural language processing applications
  • Creating machine learning algorithms across traditional and deep learning
  • Analyzing historical data to identify trends and support decision-making

2023 Data Science Trends

Automated Data Cleansing

Before business can make data-driven decisions, data scientists have to detect, correct, and remove flawed data. This process is both time and labor intensive, which drives up costs and delays decision making. Automated data cleansing is emerging as an efficient and scalable way for data scientists to outsource labor-intensive work to AI-based platforms. This will give data scientists more time and resources to focus on higher-impact actions, like interpreting data and building machine learning (ML) models.

AutoML

Automated machine learning (AutoML) is the process of “automating the time-consuming, iterative tasks of machine learning.” With AutoML, data scientists are able to build machine learning models in a development process that’s less labor- and resource-intensive. Efficient, sustainable, and scalable, AutoML also has the potential to increase the production of data science teams and make machine learning more cost-effective for businesses. Tools like Azure Machine Learning and DataRobot are even making it possible for users with limited coding experience to work with ML.

Customer Personalization

Have you ever received an ad for a product right after you thought about it? It wasn’t a coincidence. And brands aren’t able to read a consumer’s mind (yet). It turns out that data science is to blame. 

Data scientists are using artificial intelligence and machine learning to make recommendation systems so effective that they can accurately predict consumer behaviors. And it turns out that consumers are surprisingly excited about this new approach. 52% of consumers expect offers from brands to always be personalized. And 76% get frustrated when it doesn’t happen. To deliver on these expectations, companies need to collect, store, secure, and interpret huge quantities of product and consumer data. With the skills to analyze customer behavior, data scientists will be at the forefront of this effort.

Data Science in the Blockchain

By 2024, corporations are projected to spend $20 billion per year on blockchain technical services. So, it shouldn’t come as a surprise that data science is poised to help companies make sense of the blockchain. Data scientists will soon be able to generate insights from the massive quantities of data on the blockchain.

Machine Learning as a Service

Machine Learning as a Service (MLaaS) is a cloud-based model where technical teams outsource machine learning work to an external service provider. Using MLaaS, companies are able to implement ML without a significant upfront investment of budget and labor. With such a low cost of entry, machine learning will spread to industries and companies that would otherwise not be able to implement it. 

Use cases for MLaaS include: 

  • Analyzing product reviews
  • Powering self-driving cars
  • Designing chatbots or virtual assistants
  • Performing predictive analytics
  • Improving manufacturing quality
  • Automating natural language processing
  • Building recommendation engines

Leading MLaaS providers include AWS Machine Learning, Google Cloud Machine Learning, Microsoft Azure Machine Learning, and IBM Watson Machine Learning Studio.

Natural Language Processing

Natural language processing (NLP) is the branch of AI focused on training computers to understand language the way human beings do. Because NLP requires massive quantities of data, data scientists play a significant role in this advancing field. 

There are a variety of use cases for natural language processing, including: 

  • Credit score analysis
  • Customer service chatbots
  • Cyberbullying prevention
  • Fake news detection
  • Language translation
  • Speech and voice recognition
  • Stock market prediction

With so many potential applications, NLP is among the most promising trends in data science.

TinyML

TinyML is the implementation of machine learning on small, low-powered devices.  Instead of running on consumer CPUs or GPUs, TinyML devices can run microcontrollers which consume 1,000x less power. With such a high cost-efficiency, TinyML provides the benefits of machine learning while avoiding.

Synthetic Data

In 2021, GPU Manufacturer Nvidia predicted that data would be the “oil” that drives the age of artificial intelligence. And with 912.5 quintillion bytes of data generated each year, it might seem like the supply to drive this revolution is endless. But what if you could make your own oil? 

Much like natural resources, access to data isn’t distributed evenly. Many companies don’t have access to the huge quantities of data they need to drive AI, machine learning, and deep learning initiatives. 

That’s where synthetic data can help. Data scientists use algorithms to generate synthetic data that mirrors the statistical properties of the dataset it’s based on. 

Unsurprisingly, the potential use cases for synthetic data are as limitless as the data it creates: 

But there’s one effect of synthetic data that’s a guarantee: more data. With synthetic data, the world’s data generation will accelerate to a rate the human mind can’t begin to fathom.

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