The increasing demand for artificial Intelligence (AI) and data science experts, driven in part by the COVID-19 economic crisis, is showing no sign of abating. Many employers are failing to identify viable job candidates, much less interviewing or hiring them. What’s the biggest obstacle holding them back? In our experience, it is often a poor job posting.
Picture this: You’ve been working hard on a project at work. You’ve run several algorithms, tuned the necessary hyperparameters, performed cross validation and exhausted the checks required to ensure you’re not overfitting.
It’s 2020 and the world has changed remarkably, including in how companies screen data science candidates. While many things have changed, there is one change that stands out above the rest. At The Data Incubator, we run a data science fellowship and are responsible for hundreds of data science hires each year.
The concept of “edge computing” has been around since the late 90s, and typically refers to systems that process data where it is collected instead of having to both store and push it to a centralized location for off-line processing.
The medical field has been one of the fastest adopters of new data science technology. Finding new ways to treat and manage patient health has become a growing industry for data science.
Historical data can be an authoritative source of intel for a company looking to make smarter and faster decisions. When efficiencies are possible, it’s best to use the tools available to achieve them.
GPUs built on CUDA have been used for deep-learning and other applications for a long time. But, when you look at data scientists and they work they’re doing, CUDA doesn’t really fit well into their workflow.
Diana Sujanto, after completing Data Science Foundations, went on to a graduate degree in data science program.
Managers understand that having employees who understand the latest tools and technologies is vital to success .
© Copyright 2021, The Data Incubator
