Python Programming for Biological Problems

Python Programming for Biological Problems, available at $54.99, has an average rating of 4.1, with 89 lectures, based on 52 reviews, and has 336 subscribers.

You will learn about Learn the basic syntax of Python language quickly and easily Implement the main Python language operators: mathematical, logical, relational and conditional Create loop structures using for and while commands Implement functions for modularization of programs Implement the main Python language collections: tuples, lists, dictionaries, sets and arrays Manipulate text files Perform error and exception handling Learn the basic intuition and practice about regular expressions Learn the basic intuition and practice of Object Orientation Estimate the rate of recombination between genes Analyze genetic sequences Model bacterial growth Write a code that simulates a biology test, where at the end the grade is calculated Analyze gene sequence files directly from databases in .fasta format Analyze protein structure files in .pdb format Build a basic identification key for plant species This course is ideal for individuals who are Biology students or similar areas, such as biomedicine, pharmacy, forestry engineering, etc. or Biology or related professionals who wish to learn a programming language or Developers or IT professionals who are interested in applying programming knowledge in the field of biology or Undergraduate students taking programming courses or People interested in programming languages It is particularly useful for Biology students or similar areas, such as biomedicine, pharmacy, forestry engineering, etc. or Biology or related professionals who wish to learn a programming language or Developers or IT professionals who are interested in applying programming knowledge in the field of biology or Undergraduate students taking programming courses or People interested in programming languages.

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Summary

Title: Python Programming for Biological Problems

Price: $54.99

Average Rating: 4.1

Number of Lectures: 89

Number of Published Lectures: 89

Number of Curriculum Items: 89

Number of Published Curriculum Objects: 89

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the basic syntax of Python language quickly and easily

Implement the main Python language operators: mathematical, logical, relational and conditional

Create loop structures using for and while commands

Implement functions for modularization of programs

Implement the main Python language collections: tuples, lists, dictionaries, sets and arrays

Manipulate text files

Perform error and exception handling

Learn the basic intuition and practice about regular expressions

Learn the basic intuition and practice of Object Orientation

Estimate the rate of recombination between genes

Analyze genetic sequences

Model bacterial growth

Write a code that simulates a biology test, where at the end the grade is calculated

Analyze gene sequence files directly from databases in .fasta format

Analyze protein structure files in .pdb format

Build a basic identification key for plant species

Who Should Attend

Biology students or similar areas, such as biomedicine, pharmacy, forestry engineering, etc.

Biology or related professionals who wish to learn a programming language

Developers or IT professionals who are interested in applying programming knowledge in the field of biology

Undergraduate students taking programming courses

People interested in programming languages

Target Audiences

Biology students or similar areas, such as biomedicine, pharmacy, forestry engineering, etc.

Biology or related professionals who wish to learn a programming language

Developers or IT professionals who are interested in applying programming knowledge in the field of biology

Undergraduate students taking programming courses

People interested in programming languages

Biologists, biology students, and professionals in related fields generally have little or no contact with computer programming. However, the growing of data in genomic, protein and organism databases can be used to model the solution for some problems, such as the discovery of medicines and insecticides. It leads biologists to benefit from computer programming knowledge, so that they can develop useful applications in molecular biology, ecology, research on diseases, among others.

This course was developed with the purpose of introducing biologists, students of biology, biomedicine, ecology, pharmacy and professionals in related areas to programming using Python, which is nowadays one of the most used programming languages. It has a clear syntax and is easy to learn especially if you are a professional who are not familiar with technology. Many tools used in the field of biology were written in Python, which makes it a great option for establishing your first contact with computer programming. You will learn the following topics:

1. Python installation and main tools (IDEs)

2. Variables, constants and strings

3. Math operations

4. Logical, relational and conditional operators

5. Loops (for and while)

6. Functions

7. Lists, dictionaries, tuples, sets and arrays

8. Manipulation of text files

9. Error and exception handling

10. Regular expressions

11. Object oriented

After learning the basic concepts of Python, you will be able to apply the concepts in exercises, challenges and practical projects related to ??Biology. Below are some of the case studies that we will implement step by step:

1. Prediction of the mass of a peptide sequence according to its amino acid composition

2. Schedule a biology test that calculates the grade and whether the user got each question right or wrong

3. Creating classes related to objects in the biological world

4. .fasta gene sequence analysis

5. Analysis of gene frequencies according to the Hardy-Weinberg Theorem

6. Creating functions for population ecology calculations

7. Discover patterns in RNA sequences

8. Estimation of gene distances

9. Basic species identification

10. Troubleshooting gene frequencies

11. Creating scripts for parsing .pdb-type protein sequence files

12. Transcription of DNA sequences into RNA

There are more than 80 classes, concepts, code demonstration, and exercises with solutions! More than 30 proposed challenges and 4 small projects applying the concepts learned in each section in a biological context, with step-by-step resolution.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content – Jones

Lecture 2: Course content – Guilherme

Lecture 3: Introduction to Python

Lecture 4: Installation

Lecture 5: Python IDEs

Lecture 6: COLAB file

Chapter 2: Variables, data types, and user inputs

Lecture 1: Variables and constants

Lecture 2: Mathematical operations

Lecture 3: Exercises

Lecture 4: Exercise 1: Ki calculation

Lecture 5: Exercise 2: Recombination of genotypes

Chapter 3: Logical and relational operators

Lecture 1: Logical and relational operators

Lecture 2: Exercise

Lecture 3: Exercise 1: Comparing weights of proteins

Chapter 4: Strings

Lecture 1: Strings

Lecture 2: Exercises

Lecture 3: Sequences to be used in exercise 2

Lecture 4: Solution: abbreviating scientific names

Lecture 5: Solution: Extracting an exon from a gene

Chapter 5: Conditional operators

Lecture 1: Conditional operators

Lecture 2: Exercises

Lecture 3: Solution: Taxonomic families

Lecture 4: Solution: Codons in a RNA sequence

Chapter 6: Loops

Lecture 1: For loop

Lecture 2: While loop

Lecture 3: Exercises

Lecture 4: Solution: transcription of DNA

Lecture 5: Solution: Bacterial growth

Chapter 7: Collections

Lecture 1: Tuples and lists

Lecture 2: Dictionaries and sets

Lecture 3: Matrices

Lecture 4: Exercises

Lecture 5: Dictionary with the symbols of their aas and the mass

Lecture 6: Solution: mass of aminoacid sequeces

Lecture 7: Solution: transcription of DNA

Lecture 8: Project 1: Simulating a biology test

Lecture 9: Questions for the test

Lecture 10: Solution: part 1

Lecture 11: Solution: part 2

Lecture 12: Solution: part 3

Chapter 8: Functions

Lecture 1: Functions

Lecture 2: Exercises

Lecture 3: Solution: function of recombination

Lecture 4: Solution: Ki calculation

Lecture 5: Solution: transcription function

Lecture 6: Project 2: calculating gene frequencies

Lecture 7: Chi-square distribution table

Lecture 8: Project 2: part 1

Lecture 9: Project 2: part 2

Chapter 9: Modules

Lecture 1: Math and datetime

Lecture 2: Random and time

Lecture 3: Exercises

Lecture 4: Solution: generation of random DNA sequence

Lecture 5: Solution: function of population growth

Chapter 10: Custom modules

Lecture 1: Creating packages

Lecture 2: Exercises

Lecture 3: Solution: DNA module

Lecture 4: Solution: module for ecology functions

Chapter 11: Errors and exceptions

Lecture 1: Errors and exceptions

Lecture 2: Exercise

Lecture 3: Solution: validation of numerical inputs

Lecture 4: Project 3 – Identification key

Lecture 5: Image of the key

Lecture 6: Solution: part 1

Lecture 7: Solution: part 2

Chapter 12: Text files

Lecture 1: Reading and writing text files

Lecture 2: Genetic databases

Lecture 3: Protein database PDB

Lecture 4: Exercises

Lecture 5: Solution: reading a DNA sequence

Lecture 6: Solution: reading a .pdb sequence

Lecture 7: Project 4 – Reading and processing gene sequences

Lecture 8: Solution: part 1

Lecture 9: Solution: part 2

Lecture 10: Solution: part 3

Chapter 13: Regular expressions

Lecture 1: Introduction

Lecture 2: Search, match and find all

Lecture 3: Regular expressions – main metacharacters and quantifiers

Lecture 4: Exercise

Lecture 5: Solution: Identifying species names in a text

Lecture 6: Sample sequence for exercise 2

Lecture 7: Solution: Analyzing a genetic sequence

Chapter 14: Object oriented

Lecture 1: Introduction

Lecture 2: Practical

Lecture 3: Exercise

Lecture 4: Solution: Class Plant

Instructors

Jones Granatyr
Professor

Guilherme Matos Passarini, phD
Professor

AI Expert Academy
Instructor

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 14 votes

4 stars: 10 votes

5 stars: 25 votes

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