Module Descriptors 1. Statistics for Data Analytics Dublin Business School Module Descriptor Stage Stage 1 Semester Module Title Statistics for Data Analytics Module Status Mandatory Module Code Pre-Requisite Module Co-Requisite Module Code(s) Level Code(s) 8 Date Approved Contact Hours Lecture
Tutorial
Date for Review
Seminar
12 12 24 Allocation of Marks Within the Module Continuous Assessment Project 40%
Assignment
Placement
36 Practical
Credits 5 ECTS Capstone No Total Effort
Non-contact Hours Practical
1
Independent Work 41
Final Examination 60%
125 Total 100%
School of Business Author: Kevin Nolan Description: This module aims to provide learners with a solid understanding of the fundamentals of statistical analysis. Learners will study 3 topics: descriptive statistics, inferential statistics, and regression analysis. The module will also introduce learners to statistical software, e.g. R/Python. The material will be taught assuming no prior knowledge. Successful completion will enable learners to progress to Tools for Data Analytics. Aims: 1. To provide learners with numerical and graphical tools to summarise & describe data. 2. To introduce learners to inferential statistics and its applications. 3. To introduce learners to regression analysis and modelling. 4. To develop basics skills in the use of statistical software. Learning Outcomes: On successful completion of this module, learners will be able to: 1. Appraise and evaluate a dataset using descriptive statistics and charts. 2. Formulate and evaluate a hypothesis using statistical methods and communicate the results effectively. 3. Apply regression analysis and identify the limitations of regression models. 4. Appraise and evaluate appropriate software tools (e.g. R) to perform data analysis. Assessment Strategy: Participant learning will be assessed by the following: Assessment is comprised of two components: an assignment and a final examination. The assignment (worth 40%) will be a piece of work to be completed individually using suitable statistical software. The end-of-term examination (worth 60%) will be a closed-book written exam covering all topics on the course. Method of Assessment Assignment
Percentage Weightings 40%
8
Learning outcomes assessed 3,4