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QUALIFI Level 7 Diploma in Data Science
Unit code: H/618/4975 RQF
Data reduction is a key process in business analytics projects. In this unit, learners will learn data reduction methods such as PCA, factor analysis and MDS. They will also learn to form segments using cluster analysis methods. Forming segments and then analysing is a key technique for large groups of data and their intrinsic information comes out in detail once segmented thoughtfully.
Learning Outcomes and Assessment Criteria
Learning Outcomes. When awarded credit for this unit, a learner will be able to:
Assessment Criteria. Assessment of this learning outcome will require a learner to demonstrate that they can:
1. Define Principal Component Analysis (PCA) and its derivations and assess their application.
1.1 Evaluate the need for data reduction.
1.2 Perform principal component analysis and develop scoring models using R and python to minimise data loss and improve interpretability of data.
1.3 Resolve multi-collinearity using Principal Component Regression.
2. Understand hierarchical and non-hierarchical cluster analysis and assess their outputs.
2.1 Perform data reduction and derive interpretable factors and use factor scores to interpret the data set.
2.2 Obtain a brand perception map using multi-dimensional scaling.
3. Evaluate the concept of panel data regression and implement panel data methods.
3.1 Evaluate the need for cluster analysis.
3.2 Obtain clusters using suitable methods.
3.3 Interpret cluster solutions and analyse the use of clusters for business strategies.
To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:
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