Statistics for Data Analysis (Level 8)

Duration i year

Stage Award

NFQ Level Level 8

ECTS Credit 10

This program aims to take learners beyond a basic grasp of statistical analysis to acquire the critical and analytic skills needed to successfully apply statistical tools and techniques in the domain of data analytics. Students will first go over descriptive statistics, inferential statistics, and regression analysis. Learners will next graduate to a level 9 critical awareness of the methodologies and tools available for data analysis for both data and large data analysis.

The objectives of this module are to enable the learner to:

To give the student the chance to gain the statistical abilities needed to choose, investigate, summarize, analyse, and visualize data.
To enable the student to improve their critical thinking skills in terms of selecting the appropriate statistical tools and methodologies to tackle data analysis challenges.
To enable the student to provide a sound and resilient analysis based on sound concepts.

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Indicative Syllabus

Segment 1

Introduction to Linear Algebra, Linear Algebra and Machine Learning and Examples of Linear Algebra in Machine Learning, NumPy, Introduction to NumPy Arrays, Vectors and Vector Arithmetic, Vector Norms, Matrices and Matrix Arithmetic, Types of Matrices, Matrix Operations, Sparse Matrices and Tensors and Tensor Arithmetic Matrix Decompositions, Eigen decomposition and Singular Value Decomposition.

Segment 2

Introduction to Multivariate Statistics, Principal Component Analysis, Statistics vs Machine Learning, Examples of Statistics in Machine Learning, Gaussian and Summary Stats, Simple Data Visualization, Random Numbers, Law of Large Numbers and Central Limit Theorem.

Segment 3

Statistical Hypothesis Testing, Statistical Distributions, Critical Values, Covariance and Correlation, Significance Tests, Effect Size, Statistical Power, Resampling Methods, Introduction to Resampling, Estimation with Bootstrap, Estimation with Cross-Validation.

Segment 4

Introduction to Estimation Statistics, Tolerance Intervals, Confidence Intervals, Prediction Intervals, Nonparametric Methods, Rank Data, Normality Tests, Make Data Normal, 5-Number Summary, Rank Correlation, Rank Significance Tests and Independence Test.

Module Details

Minimum Intended Learning Outcomes (MIMLOS)

Evaluate tools and processes for data selection, exploration, summarization, interpretation, and visualization.
Provide a critical examination of a problem domain and choose and recommend relevant statistical tools and approaches for analysing and gaining insight from a given dataset.
Select a problem domain and create and implement a solution linked to the program area.
Reflect on a statistical study of a problem domain, as well as a critical review of the tools and methodologies used, as well as the usefulness of the insights generated from discovered measurements.

Assessments / Exams

This module is assessed via 50% continuous assessment and 50% Final Exam. The continuous assessment involves three separate assessments.

Method of Assessment	Percentage Weighting	Learning outcome
Formative Assessment	50%	1,2,3,4,
Reflective Activities Class activities including quiz and discussion	25%	1,4
Presentation Data Mining Journal and its reflections	25%	2,3
Summative Assessment	50%	1,2,3,4
Final Exam	50%	1,2,3,4

Reassessment Opportunity

Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board Meeting and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.

Statistics for Data Analysis (Level 8)

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If you have any questions specific to this course, you can speak directly with one of our course lecturers. Please get in touch, we’re happy to help!