Resources

To deliver results, data practitioners must ensure a project is set up for success before committing the time, labor and resources required for analysis. Learn the importance of refining your data project early and having difficult conversations with project stakeholders based on the data available.

On average, as a data practitioner, you spend 80% of the time preparing and cleaning the data for analysis. However, without data preparation, any subsequent data analysis will be flawed. Preparing and identifying the opportunities...

While data science is a competitive advantage, data isn’t magic. Working closely with your data partners to develop questions is where the magic happens. In this ebook, learn how to connect the question you’re asking to the analysis required to ensure the findings will have real business impact.

What’s the difference between a successful data analysis project and one that falls flat? Before you begin working with the data, you need to understand what you’re solving for. Gathering context and aligning around goals...

What is the difference between a data engineer and a data architect? What are the responsibilities of a data analyst vs. data scientist? What languages does a database administrator need to know?  And how does...

What is a data custodian? How about a data engineer? From HiPPO to Python, data wrangling to deep learning, our Data Science Glossary defines the industry terms you should know, no matter your role in...