Python Programming Fundamentals for Researchers
Build a solid Python foundation through hands-on exercises in Google Colab. Master variables, functions, control flow, and file handling with research-focused examples. Perfect for complete programming beginners.
Workshop Description
This comprehensive 3-day workshop introduces researchers to Python programming from the ground up using Google Colab. Perfect for complete beginners, this course focuses on building a solid foundation in Python programming concepts without overwhelming you with advanced topics.
You'll master essential Python skills including variables, data types, control structures, functions, and file handling through practical, research-oriented exercises. The workshop emphasizes clean coding practices, debugging techniques, and problem-solving approaches that will serve as the foundation for all your future programming endeavors.
By the end of this workshop, you'll be confident writing Python programs, handling data files, creating reusable functions, and debugging your code effectively. All exercises use research scenarios, and you'll leave with practical programming skills ready to tackle real-world problems in your field.
Instructor
Course Fee
$299.0
Maximum Seats
20
Duration
3 half-days
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Time
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A comprehensive 3-day foundation in Python programming for researchers
Format: Each day is approximately 4 hours with hands-on exercises using Google Colab
Prerequisites: No prior programming experience required. You'll need a Google account to access Google Colab and basic computer literacy
What you'll learn: Core Python programming concepts, problem-solving with code, file handling, function design, and debugging techniques essential for research computing
Materials provided: All exercises, datasets, and code templates will be provided. You'll have lifetime access to all workshop materials
Day 1: Python Basics & Data Types
Master the fundamentals of Python programming
Welcome & Environment (30 min): Workshop overview, Google Colab setup, understanding notebooks vs scripts, and saving work to Drive
Variables & Data Types (1 hr): Numbers, strings, booleans, type conversion, variable naming conventions, and the importance of meaningful names in research code
Working with Strings (45 min): String methods, formatting, concatenation, and practical examples with research data (sample IDs, file names, etc.)
Basic Operations (45 min): Arithmetic, comparison, and logical operators with real-world research examples
Input/Output & Debugging (1 hr): Getting user input, print statements for debugging, understanding error messages, and basic troubleshooting strategies
Day 2: Data Structures & Control Flow
Learn to organize and control your data and program flow
Lists Deep Dive (1 hr): Creating, accessing, modifying lists. List methods, slicing, and nested lists with research examples (sample collections, measurement series)
Dictionaries & Sets (1 hr): Key-value relationships, dictionary methods, when to use sets vs lists, and practical applications in research data organization
Conditional Logic (1 hr): If/elif/else statements, combining conditions, and decision-making in data processing workflows
Loops & Iteration (1 hr): For and while loops, iterating over data structures, loop control (break/continue), and common loop patterns in research
Day 3: Functions & File Handling
Create reusable code and work with external data files
Function Fundamentals (1.5 hrs): Defining functions, parameters and arguments, return values, scope and local vs global variables, and designing functions for research workflows
File Operations (1 hr): Reading and writing text files, CSV basics, file paths and organization, and handling common file formats in research
Error Handling (45 min): Understanding common errors, try/except blocks, and defensive programming practices for robust research code
Code Organization & Best Practices (45 min): Writing clean, readable code, commenting strategies, and organizing code for reproducible research
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