Data Mining

Learning Outcomes:

• Applying data mining techniques to extract meaningful patterns and knowledge from large datasets
• Implementing core algorithms for classification, clustering, and association rule mining
• Evaluating the performance and suitability of different data mining models
• Preprocessing and transforming data to improve mining results
• Interpreting and communicating insights gained from data mining analyses
• Comparing supervised and unsupervised learning approaches
• Handling challenges like high dimensionality, missing data, and noise
• Selecting appropriate features and reducing dimensionality of datasets
• Analyzing time series data and performing forecasting
• Understanding probabilistic and statistical foundations of data mining methods
• Applying text mining and information retrieval techniques
• Assessing ethical implications and limitations of data mining

Skills for module:

Python

R

Machine Learning

Artificial Intelligence

Data Science

Data Visualisation

Statistics

Probability

Linear Algebra

Algorithms

Data Structures

Databases

Relational Databases (SQL)

Non Relational Databases (NoSQL)

Indexing

Scikit Learn

Pandas

NumPy

Matplotlib

Neural Networks

Deep Learning

Hyperparameters

Testing

Problem Solving

Critical Thinking

Time Management

Continuous Integration

Boosting

Mathematics