Course Content
The lecturing team consists of a team of staff that combine both academic qualifications with substantial practical experience, and this practical perspective is reflected in the delivery of the module. Some of the key subject areas are the following:
Importance of Data: Types of data such as record data, ordered data, time-series data, etc., Quality data. Errors in data. Different types of attributes of data such as nominal, ordinal, interval and ratio. The data analytics life cycle. The types of data analytics.
Data sources: Flat files, excel files, databases, such as relational databases etc.
Data Pre-processing: Techniques such as aggregation, sampling, dimensionality reduction, Feature subset creation etc.
Statistical Techniques: Measures of central tendency (mean, median, mode). Scatter chart, Correlation, Regression Analysis, Logistic regression, etc.
Supervised and Unsupervised Machine Learning: Techniques such as decision trees, support vector machines., k-nearest neighbours, k-means clustering, neural networks, etc. Developing predictive models, testing techniques , validating models. Interpreting and Evaluating Results.
Data Visualization: Connecting to different data sources, developing charts and graphs such as bar, line, pie charts, heat maps, bubble charts, motion charts, etc. Applying filters, creating groups, hierarchies and sets, etc. Creating stories and developing dashboards