You have knowledge of explicative statistical skills.

# NCF Category: Data

## Operational Research (Level One)

You have a good understanding of the strengths and limitations of OR techniques.

## Economics (Level One)

You can utilise quantitative research skills including knowledge of statistical techniques used in applied economics.

## Social Research (Level One)

You are able to think logically and have an interest in the issues relating to society, groups and individuals.

## Behavioural Science (Level One)

You can identify problems effectively and are able to gather research from multiple areas and contexts.

## Hypothesis Testing (Level One)

You can identify a business question and convert it into an appropriate hypothesis. You understand the difference between analytical hypotheses and null hypotheses. You understand one-sided and two-sided hypotheses.

## Machine Learning (Level One)

You understand the principles behind machine learning and their link to predictive analysis. You can identify the data needed for successful machine learning to take place and understand the iterative nature of machine learning.

## Data Automation (Level One)

You understand timeliness of data and the need for data to flow with minimal interaction. You can identify where automation would improve processes.

## Technological Specialisms (R, Python, SQL, Tableau etc.) (Level One)

You can use a chosen technology/tool to create or manipulate data sets and create basic visualisations.

## Information Governance (Data) (Level One)

You know the key data protection principles. You understand when data can be accessed and shared and know who in the organisation to approach for advice/approval.

## Data Modelling (Level One)

You understand what a data model is and how data items are stored. You are able to explain the potential relational nature of data and can express conceptual models. You understand how conceptual, logical and physical data models relate to each other.

## Geographical Data Mapping (Level One)

You understand how geographical data can be displayed to show geographical features such as simple chloropleth mapping using appropriate tools.

## Context (DAC1.3) (Level Five)

You can apply innovative approaches to resolve business and team issues.

## Data interpretation (DAC1.2) (Level Five)

You guide colleagues to create and interpret strategic insights.

## Data transformation (Data Analysis) (DAC1.1) (Level Five)

You can apply innovative approaches to resolve business and team issues.

## Advanced Statistics (Level One)

You understand when advanced inferential statistical techniques are needed and the different methods available. You are able to build basic regression models and analyse results from three or more groups.

## Statistical Process Control (Level One)

You understand the principles of normal and special cause variation and how data can be presented in SPC charts. You are able to produce basic XmR charts.

## Behavioural Science (Level Five)

## Social Research (Level Five)

## Economics (Level Five)

## Operational Research (Level Five)

## Machine Learning (Level Five)

## Technological Specialisms (R, Python, SQL, Tableau etc.) (Level Five)

## Data Automation (Level Five)

## Business Analysis (Level Five)

## Skills (Level Five)

5

## Hypothesis Testing (Level Five)

## Data Visualisation (Level Five)

## Geographical Data Mapping (Level Five)

## Statistical Process Control (Level Five)

## Descriptive and Explicative Analytics (Level Five)

## Predictive and Prescriptive Analytics (Level Five)

## Evaluative Analytics (Level Five)

## Advanced Statistics (Level Five)

## Population Segmentation and Stratification (Level Five)

## Data Modelling (Level Five)

Drive the strategy for the data modelling principles within data engineering.

## Information Governance (Level Five)

## Longitudinal Analysis (Level Five)

## Behavioural Science (Level Four)

## Social Research (Level Four)

## Economics (Level Four)

## Operational Research (Level Four)

## Data Visualisation (Level Four)

## Geographical Data Mapping (Level Four)

## Statistical Process Control (Level Four)

## Descriptive and Explicative Analytics (Level Four)

## Predictive and Prescriptive Analytics (Level Four)

## Evaluative Analytics (Level Four)

## Advanced Statistics (Level Four)

## Population Segmentation and Stratification (Level Four)

## Data Modelling (Level Four)

## Information Governance (Level Four)

## Longitudinal Analysis (Level Four)

## Machine Learning (Level Four)

## Technological Specialisms (R, Python, SQL, Tableau etc.) (Level Four)

## Data Automation (Level Four)

## Business Analysis (Level Four)

## Skills (Level Four)

4

## Hypothesis Testing (Level Four)

## Behavioural Science (Level Three)

You have a core knowledge in data science, economics, psychology and policy and are able to transform theory into practice and better adapt interventions to a specific context.

## Social Research (Level Three)

You are able to review existing research evidence and work with other analysts to provide timely, relevant and robust policy responses and debate.

## Economics (Level Three)

You can create econometric models for example CGE (Computable General Equilibrium) or partial equilibrium for scenario analysis in software packages.

## Operational Research (Level Three)

You have a breadth of knowledge across a range of hard (e.g. linear programming, integer programming) and soft analytical techniques (e.g. strategic options development and analysis (SODA), soft systems methodology (SSM))

## Evaluative Analytics (Level Three)

You understand the role of proxy measures for less straightforward outcomes. You can assess these measures and quantify their uncertainty. You can evaluate previous research to determine appropriate measures.

## Advanced Statistics (Level Three)

You know all standard advanced statistical techniques and keep up to date with new developments e.g. time series modelling using ETS, ARMA, ARIMA, BATS, TBATS etc. You understand the context for these developments and their limitations.

## Population Segmentation and Stratification (Level Three)

You can use a variety of methods within advanced broad technique categories eg k?mean, k-modes, CHAID etc. You understand Random Forests and how they build on standard decision trees. You know other dynamic segmentation techniques.

## Data Modelling (Level Three)

You can build appropriate data models from physical data models and pick the most appropriate infrastructure. You understand data entities, attributes and specific modelling environments, e.g. Oracle, SQL Server, Hadoop etc.

## Information Governance (Data)(Level Three)

You know when data can be accessed and shared and know who to approach outside the organisation for advice. You understand how data linkage and different types of analysis can re-identify or help anonymise data. You have knowledge of GDPR.

## Longitudinal Analysis (Level Three)

You understand pragmatic differentiation between independent measures and repeated measures design. You understand how changing populations can affect analysis and choice of techniques used. You understand data attrition.

## Machine Learning (Level Three)

You have detailed understanding of supervised, unsupervised, semi-supervised and re-inforcement models and where these methods are most effective. You have skills in programming languages such as Python to enable more detailed Machine Learning models.

## Technological Specialisms (R, Python, SQL, Tableau etc.) (Level Three)

You can produce complex data models and visualisations whilst ensuring accurate linkage and data quality. You use appropriately advanced coding and debugging skills to utilise and contribute to the open source community.

## Data Automation (Level Three)

You are able to link directly to source data using appropriate tools. You understand the inbuilt functionality of Microsoft and other products to directly link to SQL servers, Azure etc. You have knowledge of APIs and how they may benefit automation.

## Business Analysis (Level Three)

You can draw up detailed technical roadmaps to achieve the “to be” state and communicate them with non-technical stakeholders.

## Skills (Level Three)

3

## Hypothesis Testing (Level Three)

You understand Type I and Type II errors and how these relate to statistical power. You understand a-priori and post-hoc hyptheses, the difference between them and the strengths and weaknesses in relation to exploratory analysis.

## Data Visualisation (Level Three)

You can use underlying coding such as mCODE, DAX etc to create the most efficient datasets to visualise. You can tell a story using data.

## Geographical Data Mapping (Level Three)

You understand when geographical mapping is appropriate and can combine it with other visualisation methods to create greater impact. You can produce dynamic maps based on changing data.

## Statistical Process Control (Level Three)

You understand the different types of SPC charts and when each should be used. You have knowledge of process redesign and its dependence on removing special cause variation.

## Descriptive and Explicative Analytics (Level Three)

You produce indicators and metrics that clearly measure what is required. You understand and apply reliability and validity assessments. You select the most appropriate methods of visualisation.

## Predictive and Prescriptive Analytics (Level Three)

You understand the difference between predictive and prescriptive analysis, and have knowledge of tools and techniques for prescriptive analysis including business modelling and algorithms. You are aware of the link to machine learning.

## Behavioural Science (Level Two)

You are able to abstract concepts allowing you to prototype a solution and a methodology and can design, deploy and manage experimental designs.

## Social Research (Level Two)

You are able to use tools including surveys, interviews, focus group discussions and observations to collect information that you are able to analyse and from which you can draw conclusions.

## Economics (Level Two)

You can apply the appropriate micro or macro-economic principles to lead the production of forecasts and associated analysis, related to the performance of the aggregated economy. You can design, create, test and refine econometric or statistical models to support decision making.

## Operational Research (Level Two)

You have the ability to analyse issues of concern incisively and to develop effective and systematic methods to resolve them persuasively.

## Longitudinal Analysis (Level Two)

You can identify when longitudinal analysis is suitable and know the key techniques for analysis. You understand repeated measures designs and the limitations of standard statistical techniques.

## Machine Learning (Level Two)

You can create algorithms/queries etc. that use analytical methods such as regression analysis and iteratively enhance the supervised learning models. You understand the differences between the various learning models.

## Technological Specialisms (R, Python, SQL, Tableau etc.) (Level Two)

You utilise associated modules and add-ins to perform complex manipulation and visualisation, data linkage and data quality. You can code to a standard to conduct work independently.

## Data Automation (Level Two)

You can distinguish between data analytics automation and data flow automation. You can link to and produce data flow maps that show where automation would be beneficial. You can use tools e.g. Power Query to automate data processing tasks.

## Business Analysis (Level Two)

You process map current practices thoroughly. You can create and evaluate detailed gap analyses.

## Skills (Level Two)

2

## Hypothesis Testing (Level Two)

You can determine the correct statistical tests for hypotheses, along with whether testing should be one tailed or two tailed. You understand alpha and beta and what p-values and confidence limits mean and you know how to calculate them.

## Data Visualisation (Level Two)

You can import data into underlying data models. You understand the relational structure of the data and use the most appropriate visualisation method.

## Geographical Data Mapping (Level Two)

You understand different geographies and how they can be displayed using point mapping, density mapping, chloropleth, isoline maps etc. You understand how geographical boundaries relate to eachother.

## Statistical Process Control (Level Two)

You understand the statistics and assumptions behind XmR charts, including when they are not the best method to use. You know the other types of SPC charts for different data types.

## Descriptive and Explicative Analytics (Level Two)

You understand variablilty and how it affects the data being analysed. You understand how descriptive statistics such as prevalence and incidence are interdependent. You are able to describe data in an unambiguous fashion.

## Predictive and Prescriptive Analytics (Level Two)

You are able to use key techniques in predictive analytics including regression methods and use them with different data types. You can select the appropriate technique for the subject.

## Evaluative Analytics (Level Two)

You understand the iterative nature of evaluative techniques. You can identify when there is a cause and effect relationship and take into account the related time lag within any evaluative analysis.

## Advanced Statistics (Level Two)

You understand key regression models, cluster analysis, factor analysis, principal component analysis etc. You apply the currect techniques to the business questions. You understand the limits and assumptions behind each technique.

## Population Segmentation and Stratification (Level Two)

You are able to use decision trees and cluster analysis in addition to simpler techniques. You communicate about methods and related limitations to stakeholders.

## Data Modelling (Data) (Level Two)

You can express logical and physical data models to define how a model will be built. You understand the different data model infrastructures and the limitations of each.