Python for Bioinformatics-Self Learning Course

Python for Bioinformatics

What you’ll learn

  • algorithms for solving various biological problems
  • Python Fundamentals
  • Text Manipulation & Printing
  • Read & Write Files
  • List & Loops
  • Conditional Test & Regular Expression

Requirements

  • A PC and stable internet connection
  • If you haven’t programmed in Python before, not to worry, We are here to support you at every step.

Description

This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python.

It offers a gently-paced introduction to our Bioinformatics Specialization preparing learners to take the first course in the Specialization.

If you haven’t programmed in Python before, not to worry, We are here to support you at every step.

What will be taught in this course:

  • Module 1 Introduction to Python for Bioinformatics
  • Module 2 Python Fundamentals
  • Module 3 Text Manipulation & Printing
  • Module 4 Read & Write Files
  • Module 5 List & Loops
  • Module 6 Conditional Test & Regular Expression

After successful purchase, this item would be added to your courses.

You can access your courses in the following ways :

  • From Computer, you can access your courses after successful login
  • For other devices, you can access your library using this web app through the browser of your device.

Learntoupgrade is an online education platform providing rigorous industry-relevant programs designed and delivered in collaboration with world-class faculty and industry. Merging the latest technology, pedagogy, and services, Learntoupgrade is creating an immersive learning experience and getting you ready for the industry by empowering you with the latest skills and technology. We aim to bridge the gap between students and industry by helping students with the virtual training classes.

Who this course is for:

  • This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python.

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