Resources

Data analytics can be very powerful when used properly. However, to deliver results for stakeholders and organizations, data practitioners must ensure a project is set up for success before committing the time, labor and resources

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