To get certified as a Data Scientist Associate, this candidate had to demonstrate that they have the knowledge, skills, and abilities to succeed at the entry level in this role. The competency domains assessed included, but were not limited to:
Using standard manipulation and visualization techniques to learn more about the data available and insights that may be gained. Preparing data for further analysis and modeling including the creation of new features.
Developing predictive models using appropriate machine learning techniques for the data and task at hand. Performing all elements of the model development workflow from initial fit to model validation and parameter tuning.
Being able to write reusable code to solve data problems. Identifying when problems have occurred and resolved them effectively, ultimately resulting in a process suitable for production environments for solving data challenges.
Presenting data in reports or dashboards to make available to stakeholders and clearly presenting actionable analytic results to business problems.
Typically these skills can take candidates 100+ hours to acquire over time
We tested the candidate's skills rigorously through:
Through a series of questions on a range of topics, we are able to establish that this individual has the knowledge required for a data analyst role. We make use of adaptive testing approaches to understand to a high degree of confidence the skill level of individuals who take the assessments.
Practical exam submission
This stage of the certification is graded manually and stringently by our data experts. The candidate must complete a written report that addresses a business problem. In this report, they’ve selected appropriate visualizations, fitted and evaluated a model, and effectively defended their decisions.
To learn more about how we built the certification and what our candidates have to achieve to pass, Download our whitepaper.