As businesses wrestle with ever-greater volumes of data, both generated within their organizations and collected from external sources, finding efficient ways to analyze and “operationalize” all that data for competitive advantage is increasingly challenging.
That’s driving demand for new tools and technologies in the realms of data science and machine learning. The global machine learning market alone reached $ 15.44 billion in 2021, is expected to reach $ 21.17 billion this year and grow to $ 209.91 billion by 2029 for a CAGR of 38.8 percent, according to a Fortune Business Insights report.
The global market for data science platforms, meanwhile, was valued at $ 4.7 billion in 2020 and is projected to reach $ 79.7 billion by 2030, a CAGR of 33.6 percent, according to an Allied Market Research report.
“Data science” and “machine learning” are sometimes confused and even used interchangeably. They are two different things, but they are related in that data science practices are key to machine learning projects.
Data science is a field of study that uses a scientific approach to extract meaning and insights from data, according to the Master’s in Data Science website. It includes developing data analysis strategies, preparing data for analysis, developing data visualizations and building data models.
Machine learning, a subsegment of the broader AI universe, uses data analytics to teach computers how to learn – imitating the way that people learn – using models based on algorithms and data, according to the Fortune Business Insights report.
The demand for data science and machine learning tools has spawned a wave of startup companies developing leading-edge technology in the data science / machine learning arena. Here’s a look at 10 of them:
* Black Crow AI
* Snorkel AI