The aim of the module is to: Equip the student with the skills and know-how to carry out common database administration tasks and to introduce the students to data analysis techniques.
Data Preparation and Analysis
Working raw data( importing data from different sources but only one source at a time), cleaning the data (using mean, median, mode, Standard Deviation, number imputation and graphing for outliers), analysis of the data (Descriptive,Exploratory, Inferential) using pythons built in librarys e.g. MatPlotlib, numpy, pandas.
Visualisation of Data
Overview of google analytics, visualization of data sets using Python tools such as Pandas and MatPlotlib
Machine Learning
Analysing datasets by training machine learning models, scoring the models, using the models to predict on seen and unseen data. Models to include Linear Regression, Gaussian NB, Logistic Regression, KNN, Decision Tree & KMeans.
Governance of Data
Examining the theory of governance of data e.g. privacy by design or default, Personal and sensitive data, subject access rights. GDPT and the Digital Services Act.
Lecture: Subject experts deliver foundational knowledge and theoretical insights into foundational and advanced topics.
Lab: Interactive sessions where students collaborate to apply theoretical knowledge to real-world scenarios.
Self-Directed: Guided by reading lists, online resources, and problem sets, students enhance their understanding through self-directed learning.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |