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Course description

This course is designed for beginners in programming with a biological background who want to deal with biotechnology data such as

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Course description

This course is designed for beginners in programming with a biological background who want to deal with biotechnology data such as

This course requires you to install Python (this was explained in detail in the first section), and we will also need to install the Integrated Development Environment in order to write codes in it (this was also explained in the first section).

All the Python files in which the codes were written, which you will see in the videos from the beginning to the end of the course, we have provided in a folder that you will download from the first section of the course.

I want you to read this well because it will explain many things to you that will help you to continue learning the Python language by clarifying the fruit of each section of the course.

In the first section, you will install Python, then you will know the modes through which you can write the codes, then you will learn the basics of the Python language, such as variables and what is related to them, comments, printing data to the user, and receiving data from the user.From this part, you will be able to learn how to receive data from the user, how to store the data inside the code, and how to output data to the user.

In the second section, you will learn about data types in Python. The meaning of data types in Python is the form in which Python understands user data. And you will learn about converting from one data type to another.

One of the fruits of the first section is storing data in variables. Here you will learn more about the format in which the data will be stored in these variables, and this varies according to the nature of the data that the user will enter. It may be a number, DNA sequence, table, file, and so on.

In the third section, you will learn how to access just one element of data.

After storing the data in a type of Python data, you will be able to delve into the data through Python, so you can access a very small part of the data and thus be able to deal with it however you want.

In the fourth section, you will work with me on simple code. We will rely mostly on the information that we learned in the first three sections, where we will store data in Python, then receive data from the user, then search for it in the stored data, and output information to the user related to the information entered.

In the fifth section, which is extremely important, we will learn operations that can be performed on the entered data and how the data can be updated. We will also put questions or conditions about the data that will give us one of two answers, either true or false.

This section will give you the power to make decisions about the data.

In the sixth section, you will learn how to write your decisions in code and help write procedures based on the decisions made.

This part is very related to the fifth part, so they must be linked together.

In the seventh section, you will learn how to use loops.

These are precisely the ones that a person is unable to perform because they are characterized by repeating a set of commands at the speed of a computer, and therefore they can do complex things that a person cannot do in a short time, and therefore you must understand them well.

In the eighth, ninth, tenth, and eleventh sections, we will learn many functions in Python that manipulate data.

Knowing many functions enables the programmer to manipulate data, which is reflected in the effectiveness of the extracted information.

In the twelfth section, one of the most important sections in the language in general and in the field of bioinformatics in particular, in which you will learn a new method of input and output, where input and output were functions, but now they are files, which are the biological data storage unit.

You will also handle operating system paths within Python.

In the thirteenth section, you will learn how to deal with time and date, and this section may be useful in calculating the time and printing it in the output.

You will find, for example, in some programs that contain more than one process, that the time and date are printed for each process.

In the fourteenth section, you will learn how to separate code and put it into structures that only work by calling, which is building your own functions.

This is the first way to build the code for the user in a usable form.

In the fifteenth section, you will learn to build scripts and modules that are considered the official output from the codes. That is, once you reach this stage, you can create your codes in modules that can be imported into Python easily.

In the sixteenth section, you will learn to build the package, which is a collection of modules.

There will be a project consisting of three modules that will be implemented step by step in front of you, and there will be improvements and error corrections, and therefore you will get good information and experience from this section.

In the end, you will find that the course will teach you information and experiences about how to receive data from the user, how to deal with it, and finally how to output the information that the user wants.

This is what you will need to build your code, whether it will be complete applications or some code that deals with data slightly, or contribute to the use of existing Python packages.

Enroll now

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for learners who want to engage with biotechnology data
Suits beginners who have a biological background
Provides a solid foundation for understanding Python
Covers a range of functions that are useful for manipulating data
Teaches learners how to build their own functions and modules

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python for Biologists with these activities:
Review Python Basics
Refreshes foundational concepts in Python, ensuring a strong starting point.
Browse courses on Python
Show steps
  • Review online tutorials or documentation on Python syntax, data types, and basic operations.
  • Complete practice exercises to reinforce your understanding.
  • Set up your Python development environment and familiarize yourself with the tools.
Compile a Bioinformatics Python Resource List
Gathers and organizes resources to support continued learning in Python.
Show steps
  • Create a document or spreadsheet to list helpful websites, tutorials, books, and tools for Python and bioinformatics.
  • Organize the resources by topic or category for easy reference.
  • Share the resource list with fellow students or the online community.
Explore Bioinformatics Python Tutorials
Provides guided instruction on using Python for bioinformatics tasks.
Browse courses on Bioinformatics
Show steps
  • Follow tutorials on platforms like Coursera or edX to learn specific bioinformatics tools.
  • Explore documentation and tutorials provided by bioinformatics software packages like Biopython.
  • Attend online workshops or webinars on bioinformatics with Python.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Python Coding Exercises
Reinforces programming concepts and develops proficiency in Python coding.
Browse courses on Python
Show steps
  • Solve coding challenges on platforms like HackerRank or LeetCode.
  • Build small Python projects to apply your skills in a practical setting.
  • Participate in online coding competitions to test your abilities.
Join a Python Study Group
Fosters collaboration, knowledge sharing, and problem-solving among peers.
Show steps
  • Find a study group focused on Python or bioinformatics.
  • Participate in regular meetings to discuss course topics, share resources, and work through challenges.
  • Contribute to group projects or assignments to apply your knowledge.
Read and Understand 'Bioinformatics Algorithms: Techniques and Applications'
Provides an in-depth and hands-on overview of data analysis methods commonly used in bioinformatics.
Show steps
  • Read each chapter thoroughly, taking notes on key concepts and algorithms.
  • Complete the end-of-chapter exercises to test your understanding.
  • Apply the algorithms to real-world biological datasets to gain practical experience.
Develop a Python Script for a Bioinformatics Task
Applies Python skills to solve a specific bioinformatics problem.
Show steps
  • Identify a bioinformatics task that can be automated with Python.
  • Design and develop a Python script to perform the task.
  • Test and refine the script to ensure its accuracy and efficiency.
Participate in a Bioinformatics Hackathon
Provides an immersive experience in applying Python skills to solve real-world bioinformatics challenges.
Show steps
  • Find a hackathon focused on bioinformatics or data science.
  • Form a team or work individually to address the hackathon challenge.
  • Develop a solution using Python and other relevant tools.

Career center

Learners who complete Python for Biologists will develop knowledge and skills that may be useful to these careers:
Bioinformatician
A Bioinformatician must master Python, as it has become the predominant programming language in the field. This course was designed specifically for those who have a biological background and wish to transition into a bioinformatics career. By covering a broad range of topics, including data input and output, coding, loops, functions, and error correction, this course will provide you with a solid foundation in Python programming and equip you for a successful career as a Bioinformatician.
Biomedical Engineer
As a Biomedical Engineer, you will be required to use Python for analyzing data and implementing complex algorithms. This course is an excellent introduction to Python for those with a biological background, covering all the essentials from data types to loops and functions. With a strong foundation in Python, you will be well-prepared to handle the programming challenges in the field of Biomedical Engineering.
Data Scientist
Python is a preferred language for Data Scientists due to its versatility and extensive data analysis libraries. This course, tailored for individuals with a biological background, provides a solid foundation in Python programming. It covers data types, loops, functions, and error correction, equipping you with the skills necessary to succeed as a Data Scientist.
Clinical Data Manager
Python is used extensively in managing and analyzing clinical data. This course, designed for those with a biological background, provides a comprehensive introduction to Python programming. By covering essential topics such as data types, loops, and error correction, this course can help you develop the programming skills required for a successful career in Clinical Data Management.
Data Analyst
Python is widely used in data analysis due to its simplicity and extensive libraries. This course, specifically tailored for individuals with a biological background, provides a solid foundation in Python programming. It covers essential concepts like data types, loops, and error correction, giving you the skills to succeed as a Data Analyst.
Research Scientist
Python has become an essential tool for Research Scientists due to its capabilities in data analysis and modeling. This course, designed for those with a biological background, offers a comprehensive introduction to Python programming. By covering fundamental topics such as data types, functions, and error correction, this course can enhance your programming skills and support your success as a Research Scientist.
Computational Biologist
Python is a widely used programming language in Computational Biology due to its versatility and extensive libraries for bioinformatics. This course, specifically designed for those with a biological background, provides a comprehensive introduction to Python programming. It covers essential concepts like data types, loops, and error correction, giving you the skills necessary to succeed as a Computational Biologist.
Software Developer
Python is a popular choice for developing software applications, including those in the biotechnology field. This course, tailored for individuals with a biological background, provides a solid foundation in Python programming. By covering essential concepts like data types, loops, and error correction, this course can equip you with the skills needed to succeed as a Software Developer in the biotechnology industry.
Biostatistician
Python is widely used in biostatistics for data analysis and modeling. This course, specifically designed for those with a biological background, provides a comprehensive introduction to Python programming. It covers essential concepts like data types, loops, and error correction, giving you the skills necessary to succeed as a Biostatistician.
Technical Writer
Python is used in technical writing for automating tasks and generating documentation. While this course does not directly focus on technical writing, it provides a comprehensive introduction to Python programming, covering essential concepts like data types, loops, and error correction. This foundation can be beneficial for Technical Writers who wish to enhance their programming skills.
Science Writer
Python is used in science writing for data analysis and visualization. While this course does not directly focus on science writing, it provides a comprehensive introduction to Python programming, covering essential concepts like data types, loops, and error correction. This foundation can be beneficial for Science Writers who wish to enhance their programming skills.
Science Communicator
Python is used in science communication for data visualization and interactive presentations. While this course does not directly focus on science communication, it provides a comprehensive introduction to Python programming, covering essential concepts like data types, loops, and error correction. This foundation can be beneficial for Science Communicators who wish to enhance their programming skills.
Science Teacher
Python is increasingly used in science education for data analysis and modeling. While this course does not directly focus on teaching, it provides a comprehensive introduction to Python programming, covering essential concepts like data types, loops, and error correction. This foundation can be beneficial for Science Teachers who wish to incorporate programming into their lessons.
Science Journalist
Python is used in science journalism for data analysis and visualization. While this course does not directly focus on journalism, it provides a comprehensive introduction to Python programming, covering essential concepts like data types, loops, and error correction. This foundation can be beneficial for Science Journalists who wish to enhance their programming skills.
Quality Assurance Analyst
Python is used in quality assurance for testing and automating processes. While this course does not directly focus on quality assurance, it provides a comprehensive introduction to Python programming, covering essential concepts like data types, loops, and error correction. This foundation can be beneficial for Quality Assurance Analysts who wish to enhance their programming skills.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python for Biologists.
Provides a comprehensive overview of bioinformatics. It covers topics such as molecular biology, genomics, and proteomics.
Provides a comprehensive overview of machine learning for bioinformatics applications. It covers topics such as supervised learning, unsupervised learning, and feature selection.
Provides a comprehensive overview of biochemistry. It covers topics such as enzyme catalysis, metabolism, and gene expression.
Provides a comprehensive overview of molecular biology. It covers topics such as DNA structure and function, gene expression, and cell signaling.
Provides a comprehensive overview of using R for bioinformatics. It covers topics such as sequence analysis, genome assembly, and gene expression analysis.

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