IoA Membership Grades

Technical Requirements of our Affiliate members on entry:

Typically, in the early stages, Affiliate members will be working towards or be able to perform the following data tasks:

• Uses a preferred analytics tool efficiently (spreadsheet, coding environment, software etc.)
• Carries out exploratory analysis, producing and analysing the 5 number summary.
• Makes use of mean, mode, and standard deviation to understand data trends.
• Produces and makes use of boxplots, histograms and scatter plots, as well as other charts.
• Can explain normally distributed data and skewness.
• Can explain outliers and a strategy to identify outliers.
• Understands the terminology in data privacy rules.
• Describes data using common official terminology (numerical, ordinal, categorical etc.)
• Cleans data set manually, removing duplicates.
• Carries out simple linear regression.
• Documents work to prioritise readability.
• Creates some simple visualisations making choices beyond the default in software or libraries, such as financial and sales reporting.
• Can structure a report on own work to facilitate contestability.
• Plans and executes simple projects.
• Makes connections between own work and the business operations.
• Can explain some risks of own work.

Please see below for a breakdown of competencies at Affiliate (late stage) level.

Technical requirements of our Affiliate (Late stage) members on leaving the level:

Typically, in the later stages, Affiliate members will be able to perform the following tasks and be working towards the Associate criteria to move up a membership grade:

• Produces visualisations with some flexibility in terms of labelling, colour and chart choice, and drawing attention to core features
• Intentionally joins data sets on key variables
• Creates new features of data (e.g. one hot encoding or dummy variables, mathematical summations and data filtering)
• Produces interactive visualisations and work with map data
• Works on both downloaded data tables and cloud data sets
• Manages data version control
• Works independently within a data privacy regulations framework
• Can produce a schema to promote data reuse and with respect to copyright laws
• Carries out simple hypothesis testing
• Separates data into a train and test split intentionally
• Creates surveys for data collection

Please see below for a breakdown of competencies at Associate level.

Technical requirements of our Associate members:

Some Associates may be working towards the technical requirements for full Members if they wish to pursue a data career.

For those in non-data professions, Associate Member grade evidences advanced data literacy for non-technical specialists.

Typically, all our Associates will be able to perform the following tasks.

• Plans and executes a manual and automated data cleaning strategy
• Explains the benefits and disadvantages of methods for dealing with missing data (e.g. listwise deletion)
• Explains risks of data collection and data bias
• Embeds visualisations in data stories, making intentional choices and avoiding deception
• Uses a mix of automation and manual work to create new features of data
• Gets a data set ready for the machine learning pipeline
• Reports confidence intervals and risk factors for all results
• Plans, implements and reports on a strategy to normalise the data for machine learning
• Works with the output of common machine learning models (regression, clustering, categorisation, anomaly detection, tree based models and association mining)
• Explains analysis of unstructured data even if they don’t carry out the analysis
• Identifies black boxed techniques and name some unintended consequences
• Can explain key AI architecture (e.g. transformers) and name potential risks
• Writes functions
• Breaks down a complex problem using computational thinking
• Protects data sets and the data within them
• Links data work to business strategy and outcomes

Please see below for a breakdown of competencies at Member level.

Technical requirements of our Members:

Our full Members will begin to specialise, and may have advanced knowledge of specific aspects of data analysis. The following skills, however, will be common to all Members:

• Chooses imputation methods intentionally
• Makes connections across data sets efficiently and effectively
• Carries out machine learning processes in a coded environment (R or Python)
• Works with feature engineering to improve outputs
• Demonstrates thorough understanding of deep learning frameworks and architectures either through practice or technical knowledge
• Demonstrates advanced understanding of mathematics, statistics, probability, pattern detection and algorithm construction relevant to the analytical process
• Uses SQL flexibly
• Works within emerging and sometimes complex data and AI regulatory frameworks

Our Senior Members may be highly specialised and may be working in more leadership roles, possibly with less time carrying out hands-on analytics.
The following competencies will be common to all Senior Members:

Technical requirements of our Senior Members:

There are no additional technical requirements for Senior Members. We would expect our Senior Members to have considerable influence within their organisation, and typically, they will hold a Chief Data Officer or research role.

Our senior members will consolidate the technical skills already mastered, demonstrating increasing understanding of the nuance of the tools they are using. They will also demonstrate their leadership skills through increased responsibility and influence within their organisation.

Our Fellow Members will be able to demonstrate an outstanding contribution to the data professions.

Technical requirements of our Fellow Members:

There are no additional technical requirements for Fellow Members and application to Fellow status is either by invitation or individual application and panel review. Please see the Membership Page for more information on how to nominate yourself or others for fellow status.