Call it data analytics, data science, machine learning, or AI—no matter your preference, the process of analyzing data, training predictive models to outperform humans, and revealing hidden connections in vast unstructured data can bring significant business value to your existing processes and new products.
A skilled data scientist sits at the intersection of tech and business. On the one hand, they’re familiar with the technology landscape and know how to acquire the right data—performing preprocessing and cleaning, training machine learning models, managing experiments, writing clean code, and deploying analytics into production systems onsite or in the cloud.
On the other hand, data scientists can seamlessly translate from business to tech and back again—relating every line of code to the problem at hand, presenting findings in a way that resonates with non-technical stakeholders, and keeping key business KPIs in mind throughout.
The truth is that the technology landscape is constantly evolving. This is especially evident in data analytics: from programming languages to machine learning techniques, from experiment management systems to AI platforms, and from pretrained models to cloud technologies.