While analysis tools and algorithms have evolved at a rapid pace, the overall business process for analytics has remained remarkably stable. One seminal work on the analytic process is IBM’s Cross-Industry Standard Process for Data Mining (CRISP-DM). At over 20 years old, it remains a relevant and useful tool for describing the overall data science workflow.
A successful data scientist needs to draw on skills from many disciplines, and one of the core skill sets is knowledge of relational databases and querying using structured query language (SQL). Relational databases are the most common way to store structured data, so a firm understanding of databases is key to obtaining performing simple analysis and reporting quickly.
Hiring is one of the most challenging tasks in a manager’s portfolio. Selecting individuals for a long-term commitment based on a few comparatively short interviews and conversations is not easy. Luckily, there are a number of personality traits to look for which can help you select promising individuals for your data science or analytics team.