Advanced Bioinformatics Certificate

Our Advanced Bioinformatics Certificate program offers a comprehensive learning experience designed to equip participants with the skills and knowledge needed to excel in the intersection of biology and data science. By joining this program, candidates can expect to: Harness Data Insights through Statistics: Develop the ability to analyze biological data using statistical methods.

Gain skills in computational thinking and programming, enabling you to solve intricate bioinformatics problems. Learn to interpret RNA-seq, microarray, methylation, and proteomic data, unraveling insights that contribute to advancements in biological research. Master Programming for Bioinformatics: Acquire proficiency in programming techniques essential for bioinformatics work. Gain a solid foundation in Python and learn to efficiently handle and summarize extensive datasets using the R programming language.

These skills empower you to manipulate, process, and derive meaningful conclusions from biological data. Unveil Genomic Insights through Computation: Explore the pivotal role of computer science in genomics. Understand the process of DNA sequencing, genome assembly, and the application of computational tools to decipher genetic information. From sequence alignment algorithms to gene regulation analysis, equip yourself to unveil intricate genomic details that drive advancements in biological understanding. Leverage Bioinformatics Databases for Innovation: Dive into the world of bioinformatics databases and tools. Learn to implement pre-existing solutions for data acquisition, analysis, and visualization. Develop proficiency in handling biological databases, interpreting RNA-Seq and ChIP-Seq data, and utilizing SQL as a research tool.These skills empower you to extract meaningful insights and contribute effectively to bioinformatics research.By enrolling in the Advanced Bioinformatics Certificate program, candidates will emerge with a strong foundation in data analysis, programming, and genomics. This preparation will enable them to navigate complex biological challenges, drive innovative research, and contribute significantly to advancements in the field of bioinformatics.

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MODULE 1: STATISTICS IN BIOINFORMATICS

This module will introduce students to statistical methods commonly used in bioinformatics. The focus will lie on teaching students how to think computationally and practice writing programs to tackle problems in bioinformatics. Topics will include statistical techniques like RNA-seq, microRNA, microarray, methylation, and proteomic data, and the analysis of them.

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MODULE 2: PROGRAMMING IN BIOINFORMATICS

This module will introduce students to programming methods commonly used in bioinformatics. Topics will include a general overview of the Python programming and accessing and summarizing big dataset using the R program.

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MODULE 3: COMPUTATIONAL GENOMIC SEQUENCING

This module examines how computer science plays a central role in genomics: from sequencing and assembling DNA sequences to analyzing genomes in order to locate genes, repeat families, similarities between sequences of different organisms, and several other applications. The students will learn how to convert raw genomic information(i.e. sequence reads) into knowledge through the use of computational genomics tools and applications. Some of the most basic and useful algorithms for sequence analysis, together with the biological background necessary for students to appreciate the application to current genomics research, will be presented. Topics will include sequence alignments, hidden Markov models, multiple alignment algorithms and heuristics such as Gibbs sampling, and the probabilistic interpretation of alignments will be covered. Applications of these tools to sequence analysis will be covered: comparing genomes of different species, gene finding, gene regulation, whole genome sequencing and assembly.

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MODULE 4: APPLICATIONS AND IMPLEMENTATION OF DATABASES OF BIOINFORMATICS

This module will allow students to explore the application of existing bioinformatic tools, including implementation of pre-coded solutions to data acquisition, wrangling, analysis, visualization, and structural modeling problems. Through this module, students should also be able to understand how hierarchical and relational models work and give examples that are widely used for biological databases, to understand the basis of bioinformatics applications, and to develop the necessary skills that will enable them to use these tools accurately and creatively in their research. Topics will include Biological databases, Sequence and bio-information representation, RNA-Seq, ChIP-Seq data analysis, and sequence comparison and similarity search. As well as the capabilities of a standard, open-sourceRDBMS, and how to use SQL as a research tool.