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David Hannon's
Professional Data Analyst certificate

Obtained: 15 March 2022.
Expires: 15 March 2024.
Get Certified
To get certified this candidate had to display that they have the knowledge, skills, and abilities to perform a Data Analyst role. These included but were not limited to:
  • Analytic Fundamentals

    Analytic Fundamentals


    Whilst more in depth analytics may be passed to data science teams, data analysts need to have a good grasp of the fundamentals of core statistical techniques. Performing A/B tests is a common requirement of a data analyst and they should have a solid understanding of how to implement and analyze this data.

    At the analyst level, a core understanding of the approaches and interpretation of analytics is essential. As such, assessment of this skill was limited to knowledge and understanding rather than application. The skill was tested through a theory assessment.

  • Exploratory Analysis

    Exploratory Analysis


    From specific business questions to more general exploration, being able to approach a problem, find the right data and get it into the right format is the core work of a data analyst. This candidate was comfortable in everything from creating summaries of data to transforming data into relevant formats for generating reports and dashboards.

    This skill was primarily tested through a hands-on SQL coding challenge but assessment was also supplemented through a test in either R or Python.

  • Data Management

    Data Management


    At the level of data analyst, data management tasks relate mostly to data cleaning and processing. This includes identifying data quality issues, performing transformations and being able to work with data from multiple sources, typically multiple database tables. For the large part, these tasks are performed in SQL.

    This skill was tested through a hands-on SQL coding challenge. The individual was required to code specific cleaning and transformation tasks that can be applied to a given data source.

  • Visualization and Reporting

    Visualization and Reporting


    As a direct line between business stakeholders and the data, it is essential to be able to effectively communicate insights. Creating dashboards, reports and presentations are all day to day aspects of the data analysts role. While the tools to do this may vary widely, the ability to create strong representations of the data through tables and visualizations and being able to effectively talk through findings, are essential at all levels.

    This skill set was tested through a practical case study. The case study required the candidate to perform data analysis tasks related to a provided business problem typical of the analyst role. They were then required to present their findings. The case study was manually graded against a predefined rubric.

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 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.

Coding challenge

Our coding challenges are free form, where candidates are presented with certain data but it is up to them to come up with an appropriate solution. The goal of this task is to demonstrate that the individual has the ability to perform the tasks required of them as a data analyst without being guided towards the appropriate solution.

Case study submission

The final stage of the certification required the individual to complete a case study. This stage of the certification is graded manually and stringently by our data scientist experts. The case study 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 analysts to have to present their work to others who have no background in data analytics. These audiences are interested in why the work was done and what the outcome was, typically not how it was done.