Data Science For Biologists.

Data Science For Biologists., available at Free, has an average rating of 3.25, with 5 lectures, based on 2 reviews, and has 243 subscribers.

You will learn about understand the importance of data abnalysis how big data needs a computational approach diffrent mathematical theoris for analysis of genomic data applications of mathematics in biological data analysis This course is ideal for individuals who are To learn different approaches of dta concepts in genomics It is particularly useful for To learn different approaches of dta concepts in genomics.

Enroll now: Data Science For Biologists.

Summary

Title: Data Science For Biologists.

Price: Free

Average Rating: 3.25

Number of Lectures: 5

Number of Published Lectures: 5

Number of Curriculum Items: 5

Number of Published Curriculum Objects: 5

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

understand the importance of data abnalysis
how big data needs a computational approach
diffrent mathematical theoris for analysis of genomic data
applications of mathematics in biological data analysis

Who Should Attend

To learn different approaches of dta concepts in genomics

Target Audiences

To learn different approaches of dta concepts in genomics

Course Title: Data Science for Biology

Course Description: Data Science for Biology introduces students to the principles and techniques of data science, focusing on their application to biological data. This course covers essential topics such as genomic data analysis, bioinformatics, and systems biology. Students will learn how to process and analyze large-scale biological datasets using computational tools and statistical methods. Key concepts include DNA sequencing, RNA-seq data analysis, genome-wide association studies (GWAS), and protein structure prediction.

The course provides hands-on experience with popular bioinformatics software and programming languages such as R and Python. Students will engage in projects that involve real-world biological data, developing skills in data visualization, machine learning, and network analysis. By the end of the course, students will be equipped to handle complex biological datasets, perform integrative analyses, and derive meaningful insights that can drive scientific discovery and innovation.

This course is suitable for biology students seeking to enhance their computational skills, as well as data science students interested in applying their expertise to biological problems. Prerequisites include basic knowledge of biology and programming. Through lectures, practical sessions, and project work, students will gain a comprehensive understanding of how data science tools can transform biological research. LETS START

Course Curriculum

Chapter 1: Introduction

Lecture 1: ANALYSIS OF GENOMIC DATA

Lecture 2: DATA THEORIES FOR NGS

Lecture 3: GENOMIC DATA TYPES

Lecture 4: QUESTIONS

Lecture 5: QUIZ

Instructors

Rajasekar Gutha
Rajasekar Gutha

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