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Lucas Fernandez's
Data Scientist Professional certificate

Obtained: 6 November 2022.
Expires: 5 November 2024.
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Lucas Fernandez's Data Scientist Professional certificate
Explore Lucas' work
To get certified this candidate had to display that they have the knowledge, skills, and abilities to perform a Data Scientist role. These included but were not limited to:
  • Data Management

    Data Management

    Cross
    Getting data from a range of appropriate sources and resolving data issues, such as formats, handling missing data, reshaping and joining, and validating data.
  • Exploratory Analysis

    Exploratory Analysis

    Cross
    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.
  • Statistical Experimentation

    Statistical Experimentation

    Cross
    Using data and relevant statistical approaches to perform experiments.
  • Model Development

    Model Development

    Cross
    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.
  • Coding for Production Environments

    Coding for Production Environments

    Cross
    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.
  • Communication and Reporting

    Communication and Reporting

    Cross
    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:

Timed assessments

Through a series of questions on a range of topics, we are able to establish that this individual has the basic knowledge required for a data scientist 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

The final stage of the certification required the individual to complete a practical exam. This stage of the certification is graded manually and stringently by our data scientist experts.

The practical exam is split into two parts:

1. Technical report:

In the case of the technical report, the audience is a data science manager. It can be considered that the work is being presented to show how the task has been approached, why certain actions were taken, and how the work helps to solve the problem defined. There is no one right answer.

2. Non-technical presentation

The final stage was to adapt the information towards a non-technical audience. It is a common requirement for data scientists to have to present their work to others who have no background in data science. These audiences are interested in why the work was done and what the outcome was, typically not how it was done.

To learn more about how we built the certification and what our candidates have to achieve to pass, Download our whitepaper.