The course frames programming in a scientific context: from basic constructs to functions and OOP in Java. Emphasis is on fundamentals, not on a specific tool.
Language of course:
English
Format of the event:
Video lectures
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Course overview
Description generated based on course syllabus and open data.
“Computer Science: Programming with a Purpose” presents core and intermediate programming concepts within the context of computer science. Java is used to illustrate variables, conditionals, loops, arrays, input/output, functions with recursion, and the basics of object-oriented programming. The focus is on problem solving, modularity, and code reuse.
Computer science and programming with a purpose: learning topics
- Syntax foundations: variables, data types, expressions, control flow.
- Data structures: arrays, 2D arrays, simple collections.
- Input/output and working with files and streams.
- Functions and modularity: parameters, scope, recursion, testing.
- OOP: classes, objects, encapsulation, basic patterns.
Fundamentals of computer science: programming with a purpose
The material serves as a toolkit for computational thinking and task analysis across natural and human sciences.
Who it suits / who it does not: computer science and programming with a purpose
Who it suits
- Beginners seeking a systematic path to programming with a purpose in a scientific setting.
- Students and researchers who need computational tools for projects.
- Those who want fundamentals of computer science without dependence on specific frameworks.
Who it does not suit
- Learners expecting a quick course on a narrow framework or purely graphical tools.
- Those aiming for advanced topics such as parallelism or systems programming.
Problem → outcome orientation: programming with a purpose
- “Code is hard to read” → modularity, naming, and functional decomposition.
- “Confusion with loops and conditionals” → consistent patterns and proven idioms.
- “No strategy for tasks” → computational thinking: formalization, invariants, tests.
- “Code duplication” → reuse via functions and classes.
- “Recursion feels hard” → step-by-step tracing and comparison with iteration.
Comparison with alternatives in computer science
- Unstructured self-study: fragmented knowledge vs a coherent path from basics to OOP.
- Visual block languages: quick start vs preparation for general-purpose text-based languages (Java).
- Algorithm theory only: abstractions without practice vs a balance of concepts and coding.
- Tool-locked courses: stack dependency vs focus on universal principles of programming with a purpose.
Covered learning outcomes in computer science and programming
- Build programs using variables, conditionals, loops, and arrays.
- Design functions, apply recursion, and organize code into modules.
- Apply OOP basics: design simple classes and object interactions.
- Work with input/output and perform basic correctness testing.
- Develop approaches to problem solving across domains.
Course Description
This course covers the first half of our book Computer Science: An Interdisciplinary Approach (the second half is covered in our Coursera course Computer Science: Algorithms, Theory, and Machines). Our intention is to teach programming to those who need or want to learn it in a scientific context.
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