What does a Full Stack Data Scientist do

A full stack data scientist can cover the whole chain of steps that are required to create value out of data plus build predictive models for the future. This includes data engineering, data analysis and data science.

Data engineers are people who build systems for data collection, processing, storage and analysis. They prepare the data so that analysts can use it to derive insights.

Data analysts are people who dig into data, clean it up, and use math to find patterns. Using these patterns, they can answer a specific question or solve a related problem. They produce charts and reports based on their analysis that help others understand the data.

Data scientists are people who can do all that a data analyst does. Additionally, they bring expertise to the table on advanced techniques like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to analyse data, predict trends & patterns and provide actionable insights. The basic responsibilities of a data scientist are:

• Data Engineering: Design and implement data warehouse architectures, optimize databases, and develop data pipelines/workflows with real-time data transformations to enable timely insights and decision-making.

• Handle large and complex datasets: Handle huge datasets, often from multiple sources, using Python or other tools to manage and process them efficiently for later use in analysis.

• Build predictive models: Use machine learning and advanced statistical techniques to build predictive models.

• Communicate insights: Provide actionable insights that can contribute to strategic decisions, often using visualisation and storytelling techniques to make complex analyses accessible to non-technical stakeholders.

Being a data scientist also requires you to possess a certain set of skills. Let us look at the technical and non-technical skills required for a data scientist next.