Data Analytics Using R

• Credit value: 15 credits at Level 7
• Convenor: Dr Cen Wan
• Assessment: problem-solving worksheets (20%) and a two-hour examination (80%)

In this module we cover the principle concepts and techniques of data analytics and how to apply them to large-scale data sets. You will develop the core skills and expertise needed by data scientists, including the use of techniques such as linear regression, classification and clustering.

We will show you how to use the popular and powerful data analysis language and environment R to solve practical problems based on use cases extracted from real domains.

Indicative syllabus

• Introduction to big data analytics: big data overview, data pre-processing, concepts of supervised and unsupervised learning
• Basic statistics: mean, median, standard deviation, variance, correlation, covariance
• Linear regression: simple linear regression, introduction to multiple linear regression
• Classification: logistic regression, decision trees, SVM
• Ensemble methods: bagging, random forests, boosting
• Clustering: K-means, K-medoids, Hierarchical clustering, X-means
• Evaluation and validation: cross-validation, assessing the statistical significance of data mining results
• Selection of advanced topics such as: scalable machine learning, big data related techniques, mining stream data, social networks
• Tools: R

By the end of this module, you will be able to:

• demonstrate knowledge of advanced aspects of big data analytics
• apply appropriate machine learning techniques to analyse big data sets
• assess the statistical significance of data mining results
• use the open-source tool R to perform basic data mining tasks on big data.