Python for Data Science

Learn Python basics and start coding with confidence

Curious about coding or exploring a career in data science or genomics? This introductory course is the perfect first step in your programming journey.

You’ll begin by exploring what programming is and why Python is such a popular and useful language, particularly for scientific and data-driven fields.

Write and test code using Jupyter Notebook

You’ll get hands-on experience using Jupyter Notebook – a user-friendly environment that lets you write, test, and document your Python code in one place.

Build core programming skills

As you work through the course, you’ll learn how to use Python’s basic building blocks: variables, operators, and common data types like lists and dictionaries.

You’ll discover how to control how your program runs using conditionals and loops, and you’ll learn to write your own functions to make your code more efficient and reusable.

Practice with real examples

Each step includes clear explanations, useful examples, and interactive exercises to help you put your new skills into practice.

By the end of the course, you’ll have created your own Python scripts and developed your confidence to take your programming skills further.

This course is an ideal introduction if you’re considering the Online MSc Genomic Medicine with Data Science at the University of Leeds. It offers a practical taster of the programming and analytical skills you’ll develop further in the full Master’s programme.

This course is ideal for professionals or students looking to develop practical programming skills to solve real-world data challenges. It’s particularly suitable for individuals in science-related fields who want to build practical Python skills that will support data-driven work in areas such as biomedical research.

The course also provides valuable preparation for postgraduate study, such as the Online MSc Genomic Medicine with Data Science at the University of Leeds. On the MSc, Python is used to carry out core data analysis tasks, including processing genomic datasets, applying statistical methods, and building computational tools for precision medicine. Gaining confidence in Python now will help learners transition more smoothly into modules that involve high-throughput technologies (e.g. Next-Generation Sequencing), bioinformatics pipelines, and machine learning approaches used to tackle complex biomedical problems.

No prior experience with Python is required, but a basic understanding of statistics or working with data will be helpful.

This course is ideal for professionals or students looking to develop practical programming skills to solve real-world data challenges. It’s particularly suitable for individuals in science-related fields who want to build practical Python skills that will support data-driven work in areas such as biomedical research.

The course also provides valuable preparation for postgraduate study, such as the Online MSc Genomic Medicine with Data Science at the University of Leeds. On the MSc, Python is used to carry out core data analysis tasks, including processing genomic datasets, applying statistical methods, and building computational tools for precision medicine. Gaining confidence in Python now will help learners transition more smoothly into modules that involve high-throughput technologies (e.g. Next-Generation Sequencing), bioinformatics pipelines, and machine learning approaches used to tackle complex biomedical problems.

No prior experience with Python is required, but a basic understanding of statistics or working with data will be helpful.

• Describe the advantages and disadvantages of programming with Notebooks.
• Be able to install Python and/or Anaconda and/or Jupyter Notebook on a computer.
• Assign numerical values to variables.
• Use basic mathematical operators to perform arithmetic calculations
• Distinguish between the basic data types in Python.
• Use appropriate operators with the basic data types in Python.
• Create and manipulate lists, tuples, sets and dictionaries.
• Create conditional statements and understand when to use them.
• Create for and while loops and understand when to use them.
• Create simple functions and understand when to use them.