Tableau Automation Toolbox: Overview The role of a data scientist is wide-ranging; typically a data scientist at a startup will have to understand the entirety of an ETL pipeline (from web application to user reports) and be in communication with every team at the company. Reporting isn’t always the most technologically exciting part of a data scientist’s job but it’s vital to understanding data and empowering all employees to promote data-driven decisions.

Supervised Learning Techniques: Background Supervised learning is a machine learning technique where a model learns from labeled data. Since every data input is paired with a corresponding output label, the model can iteratively test various weights and optimize those weights using the known ‘correct’ prediction. This allows the model to pull out complicated patterns from historical data with the goal of predicting the output for new, unseen data. Contrast this with unsupervised learning where the model uses unlabeled data to find patterns without explicit guidance.

Supervised Learning Techniques: Background Supervised learning is a machine learning technique where a model learns from labeled data. Since every data input is paired with a corresponding output label, the model can iteratively test various weights and optimize those weights using the known ‘correct’ prediction. This allows the model to pull out complicated patterns from historical data with the goal of predicting the output for new, unseen data. Contrast this with unsupervised learning where the model uses unlabeled data to find patterns without explicit guidance.