Advance Diploma In Bioinformatics With Internship Project

Complete Bioinformatics, with internship project, Tools and Techniques, Computational Biology, Data Analysis and ML

What you'll learn

You will learn about introduction to Bioinformatics, fundamentals of Molecular Biology, key areas of Bioinformatics, core concepts in Bioinformatics

Learn scopes of Bioinformatics, applications of Bioinformatics, challenges in Bioinformatics, future of Bioinformatics.

You will be able to know about introduction to Tools and Techniques in Bioinformatics, Bioinformatics tools and Applications of Bioinformatics tools.

You will also understand about Sequence alignment tools, Genomic Data Analysis tools, Protein Analysis tools

Learn Phylogenetic Analysis tools, Pathway and Network Analysis tools, Structural Bioinformatics Tools and Bioinformatics Programming Frameworks

Learn Introduction to Proteomics, Structural and Functional Proteomics. Gain knowledge on Transcriptomics and RNA-Seq Data Analysis

Learn about Introduction to Computational Biology, Key Areas in Computational Biology, Tools and Techniques in Computational Biology

Know about applications of Computational Biology, challenges in Computational Biology, future directions of computational biology

Learn about Introduction to Data Analysis and Machine Learning in Bioinformatics, basis of Machine Learning in Bioinformatics

Learn Tools and Platforms for Bioinformatics Data Analysis. You will also learn about Machine Learning Techniques in Bioinformatics

Know about Drug Discovery and Development. Learn about CRISPR and Gene Editing Tools. You will also have the knowledge on Personalized Medicine and Precision

Develop an understanding about Literature Review in Bioinformatics. Gain knowledge on Research Proposal Writing

Requirements

A foundational understanding of Biology, particularly in Genetics and Molecular Biology.

A basic knowledge of DNA Sequencing Methods and data interpretation.

Analytical skills and a curiosity for computational approaches to solving biological problems.

Description

BioinformaticsDescriptionTake the next step in your scientific journey! Whether you're an aspiring researcher, a budding bioinformatician, a healthcare professional, or simply passionate about exploring the intersection of biology and data science, this course is your gateway to mastering the principles of bioinformatics. Dive into the world of genomic data analysis, computational biology, and algorithm-driven research. Strengthen your knowledge of DNA sequencing, gene expression analysis, and genome annotation. Enhance your analytical skills with molecular data interpretation, programming techniques, and bioinformatics tools. Build a solid foundation for advancements in personalized medicine, biotechnology, and genetic research. This is your opportunity to elevate your expertise, drive scientific innovation, and make a meaningful impact in the ever-evolving fields of bioinformatics and computational genomics!With this course as your guide, you'll learn how to:Understand the fundamental concepts and principles of bioinformatics and computational biology.Gain insights into key bioinformatics techniques such as sequence alignment, next-generation sequencing (NGS) data analysis, and genome annotation.Learn about the applications of bioinformatics in fields like CRISPR and Gene Editing, personalized medicine, drug discovery, evolutionary biology, and systems biology.Invest in your knowledge today and build a strong foundation for advanced studies and innovative research in bioinformatics, genomics, and computational biology.The Frameworks of the CourseEngaging video lectures, case studies, assessments, downloadable resources, and interactive exercises form the foundation of this course. This course is designed to provide an in-depth understanding of bioinformatics, its principles, tools, and real-world applications through comprehensive chapters and units.This course introduces crucial bioinformatics tools and programming techniques, including Python, R, BLAST, and data visualization libraries, equipping you with practical skills for genomic data interpretation and biological research.You will explore key concepts on genomics, proteomics and transcriptomics along with fundamental concept on RNA-Seq Data Analysis, Omics Data analysis. The course will cover topics including Computational Biology, Data Analysis and Machine Learning. This course will also introduce an understanding about Literature Review in Bioinformatics, Research Proposal Writing, Data Visualization, Report Preparation and Publishing Research Papers.This course also helps you to strengthen your knowledge and application of advanced research, data-driven discovery, and innovation in the fields of genomics, computational biology, and personalized medicine.In the first part of the course, you’ll learn about introduction, scopes and applications of Bioinformatics. You will learn about Tools and Techniques in Bioinformatics. You will learn the details about genomics, proteomics and transcriptomics. You will also understand about Computational Biology. You will also know about Phylogenetics-Tree Construction and Visualization.In the middle part of the course, you’ll be able to learn about Data Analysis and Machine Learning in Bioinformatics, basis of Machine Learning in Bioinformatics, Tools and Platforms for Bioinformatics Data Analysis. You will also learn about Machine Learning Techniques in Bioinformatics. Gain knowledge about Omics Data Analysis. You will understand about applications of Artificial intelligence (AI), data Analysis and Machine Learning in Bioinformatics. You will also know about Drug Discovery and Development. Learn about CRISPR and Gene Editing Tools. You will also have the knowledge on Personalized Medicine and Precision Healthcare.In the final part of the course, you’ll know about Research Methodology and Scientific Writing,Literature Review in Bioinformatics. Gain knowledge on Research Proposal Writing, Data Visualization and Report Preparation and Publishing Research Papers.Course Content:Part 1Introduction and Study PlanØ Module 1: Fundamentals of BioinformaticsØ Module 2: Tools and Techniques in Bioinformatics.Ø Module 3: Genomics, Proteomics and TranscriptomicsØ Module 4: Computational BiologyØ Module 5: Data Analysis and Machine Learning in Bioinformatics.Ø Module 6: Practical ApplicationsØ Module 7: Research Methodology and Scientific WritingPart 2Projects· Predict the Function of Non-Annotated Genes Using Supervised Learning Techniques.· Analyze Genomic Mutations Associated with Specific Cancers Using Bioinformatics.Internship in BioinformaticsThis course is designed to provide students with a foundation in bioinformatics, integrating biology, computer science, and data analysis. The course will cover essential concepts, tools, and techniques used in bioinformatics to analyze and interpret biological data. By the end of the course, students will have hands-on experience with bioinformatics tools and databases and will be able to apply computational approaches to solve biological problems.Assignment Title: Bioinformatics Analysis of Gene Sequences and Protein StructuresProject Title: Comprehensive Bioinformatics Analysis of a Biological Dataset

Overview

Section 1: Module 1: Fundamentals of Bioinformatics

Lecture 1 Introduction

Lecture 2 1.1. Introduction to Bioinformatics.

Lecture 3 1.2. Fundamentals of Molecular Biology.

Lecture 4 1.3. Key Areas of Bioinformatics

Lecture 5 1.3. Key Areas of Bioinformatics 2

Lecture 6 1.4. Core Concepts in Bioinformatics.

Lecture 7 1.4. Core Concepts in Bioinformatics 2

Lecture 8 1.5. Scopes of Bioinformatics.

Lecture 9 1.6. Applications of Bioinformatics

Lecture 10 1.7. Challenges in Bioinformatics.

Lecture 11 1.8. Future of Bioinformatics.

Lecture 12 1.9. Conclusion.

Section 2: Module 2: Tools and Techniques in Bioinformatics.

Lecture 13 2.1 Introduction to Tools and Techniques in Bioinformatics.

Lecture 14 2.2 Bioinformatics tools and Applications of Bioinformatics tools.

Lecture 15 2.3 Bioinformatics tools -Sequence alignment tools 1

Lecture 16 2.3 Bioinformatics tools -Sequence alignment tools 2

Lecture 17 2.4 Bioinformatics tools -Genomic Data Analysis tools.

Lecture 18 2.5 Bioinformatics tools -Protein Analysis tools 1

Lecture 19 2.5 Bioinformatics tools -Protein Analysis tools 2

Lecture 20 2.6 Bioinformatics tools -Phylogenetic Analysis tools.

Lecture 21 2.7 Bioinformatics tools -Pathway and Network Analysis tools.

Lecture 22 2.8 Bioinformatics tools - - Structural Bioinformatics Tools and Program

Lecture 23 2.9 Techniques in Bioinformatics.

Lecture 24 2.10 Integration of Bioinformatics tools and techniques.

Lecture 25 2.11 Challenges and Future Directions.

Lecture 26 2.12 Conclusion.

Section 3: Module 3: Genomics, Proteomics and Transcriptomics

Lecture 27 3.1 Introduction on Genomics.

Lecture 28 3.2 Functional Genomics.

Lecture 29 3.3 Genome Sequencing and Assembly.

Lecture 30 3.4 Introduction to Proteomics.

Lecture 31 3.5 Structural and Functional Proteomics.

Lecture 32 3.6 Transcriptomics: RNA-Seq Data Analysis.

Lecture 33 3.7 Conclusion.

Section 4: Module 4: Computational Biology

Lecture 34 4.1 Introduction to Computational Biology.

Lecture 35 4.2 Key Areas in Computational Biology 1

Lecture 36 4.2 Key Areas in Computational Biology 2

Lecture 37 4.3 Tools in Computational Biology

Lecture 38 4.4 Techniques in Computational Biology 1

Lecture 39 4.4 Techniques in Computational Biology 2

Lecture 40 4.5 Phylogenetics-Tree Construction and Visualization.

Lecture 41 4.6 Applications of Computational Biology.

Lecture 42 4.7 Challenges in Computational Biology.

Lecture 43 4.8 Future Directions.

Lecture 44 4.9 Conclusion.

Section 5: Module 5: Data Analysis and Machine Learning in Bioinformatics.

Lecture 45 5.1 Introduction to Data Analysis and Machine Learning in Bioinformatics

Lecture 46 5.2 Basis of Machine Learning in Bioinformatics.

Lecture 47 5.3 Tools and Platforms for Bioinformatics Data Analysis.

Lecture 48 5.4 Machine Learning Techniques in Bioinformatics.

Lecture 49 5.5 Omics Data Analysis.

Lecture 50 5.6 Applications of Artificial intelligence (AI) in Bioinformatics.

Lecture 51 5.7 Applications of Data Analysis and Machine Learning in Bioinformatics.

Lecture 52 5.8 Challenges in Applying Machine Learning to Bioinformatics.

Lecture 53 5.9 Future Directions.

Lecture 54 5.10 Conclusion.

Section 6: Module 6: Practical Applications

Lecture 55 6.1 Drug Discovery and Development 1

Lecture 56 6.1 Drug Discovery and Development 2

Lecture 57 6.1 Drug Discovery and Development 3

Lecture 58 6.1 Drug Discovery and Development 4

Lecture 59 6.1 Drug Discovery and Development 5

Lecture 60 6.2 CRISPR and Gene Editing Tools 1

Lecture 61 6.2 CRISPR and Gene Editing Tools 2

Lecture 62 6.2 CRISPR and Gene Editing Tools 3

Lecture 63 6.2 CRISPR and Gene Editing Tools 4

Lecture 64 6.3 Personalized Medicine and Precision Healthcare.

Lecture 65 6.4 Ethical and Legal Aspects of Bioinformatics.

Section 7: Module 7: Research Methodology and Scientific Writing

Lecture 66 7.1 Literature Review in Bioinformatics 1

Lecture 67 7.1 Literature Review in Bioinformatics 2

Lecture 68 7.1 Literature Review in Bioinformatics 3

Lecture 69 7.1 Literature Review in Bioinformatics 4

Lecture 70 7.1 Literature Review in Bioinformatics 5

Lecture 71 7.2 Research Proposal Writing 1

Lecture 72 7.2 Research Proposal Writing 2

Lecture 73 7.2 Research Proposal Writing 3

Lecture 74 7.3 Data Visualization and Report Preparation.

Lecture 75 7.4 Publishing Research Papers.

Section 8: Projects

Lecture 76 Predict the Function of Non-Annotated Genes Using Supervised Learning Technique

Lecture 77 Analyze Genomic Mutations Associated with Specific Cancers Using Bioinformatic

Section 9: Internship in Bioinformatics

Lecture 78 Intro to Internship in Bioinformatics

Lecture 79 Assignment: Bioinformatics Analysis of Gene Sequences and Protein Structure

Lecture 80 Project: Comprehensive Bioinformatics Analysis of a Biological Dataset

This course is designed for students and researchers in life sciences, computer science, or related fields who want to integrate computational methods into their biological research.,Computer Scientists and Data Analysts interested in applying their programming and data analysis skills to solve biological problems.,Healthcare Professionals and Biotech Enthusiasts looking to understand how bioinformatics drives innovations in drug discovery, personalized medicine, and genomics.