International StandardCurriculum

A comprehensive 42-credit curriculum designed by industry experts, updated annually to reflect the latest advances in bioinformatics and computational biology.

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42
Total Credits
comprehensive curriculum
14
Core Courses
hands-on learning
4
Quarters
intensive training
100%
Industry Projects
real-world experience

Program Structure

Four intensive quarters covering everything from molecular biology fundamentals to cutting-edge AI applications in biotechnology.

Quarter 1: Foundations

Build your foundational knowledge in molecular biology and computational methods

11
Credits
12 weeks
Duration

Learning Outcomes

Master fundamental concepts in molecular biology and genetics
Develop proficiency in Python and R programming languages
Learn to navigate and utilize major biological databases
Establish version control and collaborative development skills
BCB101
12 weeks
3 Credits
Intermediate
Prerequisites

Bachelor's degree in life sciences or equivalent

Introduction to Bioinformatics and Computational Biology

Advanced concepts in genetics, gene expression, and epigenetics. In-depth study of genomics, transcriptomics, proteomics, and single-cell sequencing. Practical use of NCBI, EBI, UniProt databases.

Learning Objectives:

Develop a strong understanding of molecular biology and 'omics technologies, and learn to navigate key biological databases.

Assessment Methods
Midterm Exam (25%)
Final Project (35%)
Lab Reports (25%)
Participation (15%)
Software & Tools
NCBI ToolsUniProtEnsemblUCSC Genome Browser
Required Textbooks
Molecular Biology of the Cell (Alberts et al.)
Bioinformatics and Functional Genomics (Pevsner)
BCB102
12 weeks
3 Credits
Intermediate
Prerequisites

Basic computer literacy

Programming and Statistical Computing for Life Sciences

Advanced Python scripting for data manipulation (Pandas, NumPy, BioPython). Statistical computing with R. Mandatory use of Git and GitHub for version control.

Learning Objectives:

Master essential programming skills in Python and R for data analysis, and establish a foundation in collaborative software development.

Assessment Methods
Coding Projects (40%)
Statistical Analysis Report (30%)
Git Portfolio (20%)
Peer Code Review (10%)
Software & Tools
PythonR/RStudioGit/GitHubJupyter Notebooks
Required Textbooks
Python for Data Analysis (McKinney)
R for Data Science (Wickham & Grolemund)
BCB103
12 weeks
3 Credits
Beginner
Prerequisites

BCB101 (can be taken concurrently)

Mathematics for Bioinformatics and Computational Biology

In-depth exploration of key databases and programmatic access via APIs. Hands-on work with standard data formats like FASTA, FASTQ, and VCF.

Learning Objectives:

Gain proficiency in accessing, parsing, and managing diverse biological data formats, and learn to automate data retrieval.

Assessment Methods
Database Project (40%)
API Integration Assignment (30%)
Data Format Analysis (20%)
Documentation (10%)
Software & Tools
REST APIsBioPythonEntrez Programming UtilitiesGalaxy
Required Textbooks
Bioinformatics Data Skills (Buffalo)
Biological Sequence Analysis (Durbin et al.)
BCB104
12 weeks
2 Credits
Intermediate
Prerequisites

BCB101, BCB102, BCB103 (concurrent)

Semester Project I

First semester capstone project applying foundational concepts learned in BCB101-103. Students work individually or in small groups on a bioinformatics problem.

Learning Objectives:

Apply foundational knowledge to solve a real-world bioinformatics problem and develop project management skills.

Assessment Methods
Project Proposal (20%)
Progress Reports (30%)
Final Presentation (25%)
Code/Analysis Quality (25%)
Software & Tools
Git/GitHubPythonRProject-specific tools
Required Textbooks
Project-specific literature
The Craft of Research (Booth et al.)

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