Understand and appreciate the role of bioinformatics in solving biological problems.
Use established bioinformatics databases and web servers
Demonstrate working proficiency with sequence search and alignment (local, global, pairwise multiple sequence alignment algorithms.) algorithms.
Acquire an elementary understanding of Hidden Markov Models and their applications to problems which involve sequence learning.
Gain a solid perspective of sequence, structure and function relationships in proteins.
Acquire a necessary foundation in machine learning methods for classification and their use to address biological questions.
Gain hands-on experience in the implementation of major clustering algorithms (k-means, hierarchical clustering) and their use in the analysis of biological datasets (e.g., gene expression) and be able to perform clustering analysis.

Have a grasp of gene expression analysis and perform basic expression analysis on gene expression data
Obtain a conceptual knowledge of gene set enrichment analysis and be able to analyze and interpret the results coming from omics data.
Recognize the increasing role of biological networks in analyzing biological systems