Getting Started - Lessons Rolling Out in Stages

Welcome to Coding for Beginners!

You’re about to begin an exciting journey into the world of computer programming. Here, you’ll find 5 free lessons for 7 different programming languages—a solid foundation to help you explore, practice, and build your skills.

Coding is much like solving puzzles, which makes it more than just a hobby—it’s a workout for the brain. For people over 40 and older adults (myself included), learning to code can sharpen memory, strengthen problem-solving skills, and provide the same mental challenge as crosswords or Sudoku. Beyond supporting brain health, it also offers a sense of accomplishment and keeps the mind active—both of which are key to healthy aging.

The goal is not to become a “super computer whiz,” but to reach a point where you can write code confidently without needing to refer back to the lessons every time. That takes time, repetition, and commitment. Work through each lesson at your own pace, and revisit them until you’ve memorized the core structures and functions of each language.

You’ll be using Google Colab, which makes coding accessible to nearly everyone through a laptop, tablet, or smartphone. You will need a Google email account, and I recommend also setting up a Google Drive to store your lesson files. Click the Colab button to begin.

Stick with it for 3 months—your persistence will pay off.

Even just 30 minutes a day can give you valuable practice, a sense of progress, and some calming “wind-down” time for your brain.

Below, you can explore each programming language. Start with the one that best matches your goals - or begin with Python if you're unsure.

Python - Foundations for Data & Bioinformatics

Recommended starting point

What you’ll learn:

• Variables, loops, and conditionals

• Reading and writing data files

• Basic data analysis with real health examples

How you’ll practice:

• Interactive Google Colab notebooks

• Short exercises you can repeat anytime

Prerequisites:

• None - beginner friendly

R and R Studio

What you’ll learn:

• R basics: variables, vectors, data frames, and control flow

• Reading, cleaning, and writing datasets (CSV, tables)

• Exploratory data analysis using real public health examples

• Basic statistics commonly used in health research

How you’ll practice:

• Interactive R notebooks (using RStudio / Colab-style environments)

• Hands-on data exercises you can repeat anytime

• Step-by-step examples using real-world health datasets

Prerequisites:

• None - beginner friendly

Linux / Shell Scripting

What you’ll learn:

• Navigating the Linux file system

• Running commands and automating tasks with shell scripts

• Managing files, directories, and permissions

• Working with data files commonly used in health and research

How you’ll practice:

• Interactive command-line exercises

• Guided examples you can repeat any time

• Hands-on practice using real data folders and files

Prerequisites:

• None - beginner friendly

C++

Linux / Shell Scripting

Perl

JAVA

What you’ll learn:

• What C++ is and why it is used in scientific and bioinformatics software

• How compiled languages differ from interpreted languages (Python, R)

• Core C++ concepts at a conceptual level (types, functions, control flow)

• How C++ powers fast tools used in genomics, imaging, and data analysis

• How higher-level languages interface with C++ for performance

How you’ll practice:

• Guided walkthroughs of simple C++ examples (no complex setup required)

• Reading and understanding small, well-commented C++ programs

• Conceptual exercises focused on performance, not memorization

• Examples showing how C++ connects to Python or R workflows

Prerequisites:

• None - beginner friendly

• Prior exposure to Python or R is helpful but not required

What you’ll learn:

• What Perl is and why it is used in bioinformatics

• Processing and parsing large text files (FASTA, VCF, TSV, logs)

• Using regular expressions to extract and clean data

• Writing simple Perl scripts for data transformation

• Understanding and reading legacy bioinformatics scripts

How you’ll practice:

• Interactive Google Colab notebooks using Perl

• Guided examples focused on real bioinformatics file formats

• Short scripts you can run, modify and reuse

• Exercises showing how Perl fits into Linux-based pipelines

Prerequisites:

• None - beginner friendly

• Prior exposure to basic programming is helpful but not required

What you’ll learn:

• What is Java and why it is used in large organizations

• Core Java concepts (classes, objects, methods)

• How object-oriented programming supports large systems

• Where Java is used in healthcare, government, and enterprise software

How you’ll practice:

• Interactive Google Colab notebooks running Java code

• Guided walkthroughs of small, well-commented programs

• Conceptual exercises focused on system design, not memorization

  Examples showing how large applications are structured

Prerequisites:

• None - beginner friendly