https://mklab.gr/ Fri, 16 Oct 2026 23:03:26 +0000 MyBB https://mklab.gr/showthread.php?tid=1999 Tue, 15 Sep 2026 20:29:59 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1999 MIT 6.S095: Programming for the Puzzled, taught by Prof. Srini Devadas during January IAP 2018, is an introductory programming course that teaches computational thinking through algorithmic puzzles rather than through programming syntax alone. The central idea is to present an intriguing puzzle first, explore unsuccessful approaches, and then reach an “aha!” moment involving an algorithm, data structure, search strategy, or mathematical observation. 

Students then translate that insight into Python code, with the necessary Python concepts introduced only when they are needed. The course has no formal prerequisites beyond a basic familiarity with programming and contains 11 puzzles, accompanied by programming exercises and solution code. 

The puzzles cover a broad range of important computational ideas, including optimization, searching, recursion, combinatorial reasoning, chess/queens problems, Sudoku, tiling, scheduling, sorting, and memory-related techniques. Examples include “You Will All Conform,” “The Best Time to Party,” “Keep Those Queens Apart,” “Tile That Courtyard,” “You Won’t Want to Play Sudoku Again,” and “Memory Serves You Well.

 The course is particularly valuable because it connects mathematical problem solving with programming: instead of learning Python as an isolated technical skill, students learn to recognize structure in a problem, develop an algorithm, and then express that algorithm computationally. It is therefore a very good resource for someone interested in the intersection of mathematics, puzzles, algorithms, and Python programming. 


COURSE]]> MIT 6.S095: Programming for the Puzzled, taught by Prof. Srini Devadas during January IAP 2018, is an introductory programming course that teaches computational thinking through algorithmic puzzles rather than through programming syntax alone. The central idea is to present an intriguing puzzle first, explore unsuccessful approaches, and then reach an “aha!” moment involving an algorithm, data structure, search strategy, or mathematical observation. 

Students then translate that insight into Python code, with the necessary Python concepts introduced only when they are needed. The course has no formal prerequisites beyond a basic familiarity with programming and contains 11 puzzles, accompanied by programming exercises and solution code. 

The puzzles cover a broad range of important computational ideas, including optimization, searching, recursion, combinatorial reasoning, chess/queens problems, Sudoku, tiling, scheduling, sorting, and memory-related techniques. Examples include “You Will All Conform,” “The Best Time to Party,” “Keep Those Queens Apart,” “Tile That Courtyard,” “You Won’t Want to Play Sudoku Again,” and “Memory Serves You Well.

 The course is particularly valuable because it connects mathematical problem solving with programming: instead of learning Python as an isolated technical skill, students learn to recognize structure in a problem, develop an algorithm, and then express that algorithm computationally. It is therefore a very good resource for someone interested in the intersection of mathematics, puzzles, algorithms, and Python programming. 


COURSE]]> https://mklab.gr/showthread.php?tid=1998 Tue, 15 Sep 2026 20:27:59 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1998 6.0002 Introduction to Computational Thinking and Data Science is an undergraduate course that builds on introductory Python programming and focuses on using computation to understand and solve real-world problems. The course develops computational thinking through optimization, graph-theoretic models, stochastic processes, random walks, Monte Carlo simulation, probability, confidence intervals, sampling, and experimental data analysis. Rather than treating programming as an end in itself, it emphasizes constructing computational models, running simulations, analyzing results, and using quantitative reasoning to make conclusions. 

The second half introduces the foundations of machine learning, particularly clustering and classification, while also emphasizing the dangers of statistical mistakes and misleading interpretations of data. Students work primarily in Python through five programming problem sets involving applications such as transportation optimization, graph models, robot simulation, disease spread, and global warming. 

Overall, the course provides a strong bridge between programming, mathematics, statistics, and data science, making it particularly useful for learning how computational methods can be used to investigate complex phenomena. 

MIT OpenCourseWare — 6.0002 Introduction to Computational Thinking and Data Science]]> 6.0002 Introduction to Computational Thinking and Data Science is an undergraduate course that builds on introductory Python programming and focuses on using computation to understand and solve real-world problems. The course develops computational thinking through optimization, graph-theoretic models, stochastic processes, random walks, Monte Carlo simulation, probability, confidence intervals, sampling, and experimental data analysis. Rather than treating programming as an end in itself, it emphasizes constructing computational models, running simulations, analyzing results, and using quantitative reasoning to make conclusions. 

The second half introduces the foundations of machine learning, particularly clustering and classification, while also emphasizing the dangers of statistical mistakes and misleading interpretations of data. Students work primarily in Python through five programming problem sets involving applications such as transportation optimization, graph models, robot simulation, disease spread, and global warming. 

Overall, the course provides a strong bridge between programming, mathematics, statistics, and data science, making it particularly useful for learning how computational methods can be used to investigate complex phenomena. 

MIT OpenCourseWare — 6.0002 Introduction to Computational Thinking and Data Science]]> https://mklab.gr/showthread.php?tid=1110 Tue, 14 Jul 2026 17:56:07 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1110
Discrete Mathematics with Python 
by [freecodecamp.org]

Summary

The course covers essential topics such as permutations, combinations, counting principles, set theory, prime numbers, modular arithmetic, graph-related ideas, and the Chinese Remainder Theorem, while also demonstrating many concepts with Python code. Instead of focusing only on formulas, it emphasizes understanding the reasoning behind mathematical ideas and how they are applied to algorithms, programming, and real-world problem solving. 

Created by former mathematics teacher and senior Python developer Karol Kurek, the beginner-friendly course gradually builds mathematical intuition through clear explanations and practical examples. By the end, learners gain a solid understanding of the core concepts that underpin software development, cryptography, data structures, and other areas of computing, making it an excellent starting point for anyone interested in mathematics or computer science.

COURSE PAGE



]]>
Discrete Mathematics with Python 
by [freecodecamp.org]

Summary

The course covers essential topics such as permutations, combinations, counting principles, set theory, prime numbers, modular arithmetic, graph-related ideas, and the Chinese Remainder Theorem, while also demonstrating many concepts with Python code. Instead of focusing only on formulas, it emphasizes understanding the reasoning behind mathematical ideas and how they are applied to algorithms, programming, and real-world problem solving. 

Created by former mathematics teacher and senior Python developer Karol Kurek, the beginner-friendly course gradually builds mathematical intuition through clear explanations and practical examples. By the end, learners gain a solid understanding of the core concepts that underpin software development, cryptography, data structures, and other areas of computing, making it an excellent starting point for anyone interested in mathematics or computer science.

COURSE PAGE



]]> https://mklab.gr/showthread.php?tid=504 Fri, 19 Jun 2026 00:17:57 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=504 College Algebra Course with Python [freecodecamp]

Summary

The freeCodeCamp “College Algebra with Python” course is a free, full-length educational program that combines traditional college algebra concepts with practical Python programming. It teaches topics such as ratios, proportions, equations, functions, graphing, linear systems, quadratic equations, polynomials, interest calculations, exponents, and logarithms while showing how to solve and visualize these ideas using Python code and Jupyter notebooks. 
The course is designed to help learners understand the connection between mathematics and programming, making algebra more interactive through projects like calculators, graphing tools, financial calculators, and data exploration exercises. It is especially useful for students interested in computer science, data science, and technical fields where mathematical thinking and coding skills overlap.

COURSE PAGE]]> College Algebra Course with Python [freecodecamp]

Summary

The freeCodeCamp “College Algebra with Python” course is a free, full-length educational program that combines traditional college algebra concepts with practical Python programming. It teaches topics such as ratios, proportions, equations, functions, graphing, linear systems, quadratic equations, polynomials, interest calculations, exponents, and logarithms while showing how to solve and visualize these ideas using Python code and Jupyter notebooks. 
The course is designed to help learners understand the connection between mathematics and programming, making algebra more interactive through projects like calculators, graphing tools, financial calculators, and data exploration exercises. It is especially useful for students interested in computer science, data science, and technical fields where mathematical thinking and coding skills overlap.

COURSE PAGE]]> https://mklab.gr/showthread.php?tid=501 Fri, 19 Jun 2026 00:04:28 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=501 Summary


freeCodeCamp’s new Python Certification introduces a free, structured way for learners to prove their Python skills through a combination of lessons, workshops, labs, quizzes, projects, and a final exam. The certification focuses on core Python concepts such as variables, functions, loops, data structures, algorithms, and object-oriented programming, with students completing five required projects before taking a 50-question exam. 

The exam process includes measures for integrity while avoiding invasive requirements like webcam monitoring, and once earned, the certification is a permanent credential that can be added to professional profiles. The release is part of freeCodeCamp’s updated curriculum approach, which breaks learning into smaller checkpoint certifications that build toward a larger full-stack developer pathway.

COURSE PAGE]]> Summary


freeCodeCamp’s new Python Certification introduces a free, structured way for learners to prove their Python skills through a combination of lessons, workshops, labs, quizzes, projects, and a final exam. The certification focuses on core Python concepts such as variables, functions, loops, data structures, algorithms, and object-oriented programming, with students completing five required projects before taking a 50-question exam. 

The exam process includes measures for integrity while avoiding invasive requirements like webcam monitoring, and once earned, the certification is a permanent credential that can be added to professional profiles. The release is part of freeCodeCamp’s updated curriculum approach, which breaks learning into smaller checkpoint certifications that build toward a larger full-stack developer pathway.

COURSE PAGE]]> https://mklab.gr/showthread.php?tid=498 Thu, 18 Jun 2026 23:54:57 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=498 6.0001 Introduction to Computer Science and Programming in Python is an undergraduate introductory course designed for students with little or no prior programming experience. Its main goal is not simply to teach Python, but to develop computational thinking: understanding how computation can be used to formulate problems, design solutions, and build useful programs. The course uses Python 3.5 and introduces fundamental programming concepts such as variables, conditionals, iteration, functions, recursion, and basic data structures. 

The course combines lecture videos, notes, slides, code examples, problem sets, programming assignments, and in-class questions with solutions, making it suitable for self-study. As the course progresses, students learn to approach increasingly complex problems algorithmically, with emphasis on abstraction, decomposition, testing, and computational efficiency.

 Overall, it provides a strong foundation in both Python programming and the problem-solving mindset of computer science, making it a particularly good starting point for someone who wants to progress later into algorithms, data structures, AI, or more advanced programming.


COURSE]]> 6.0001 Introduction to Computer Science and Programming in Python is an undergraduate introductory course designed for students with little or no prior programming experience. Its main goal is not simply to teach Python, but to develop computational thinking: understanding how computation can be used to formulate problems, design solutions, and build useful programs. The course uses Python 3.5 and introduces fundamental programming concepts such as variables, conditionals, iteration, functions, recursion, and basic data structures. 

The course combines lecture videos, notes, slides, code examples, problem sets, programming assignments, and in-class questions with solutions, making it suitable for self-study. As the course progresses, students learn to approach increasingly complex problems algorithmically, with emphasis on abstraction, decomposition, testing, and computational efficiency.

 Overall, it provides a strong foundation in both Python programming and the problem-solving mindset of computer science, making it a particularly good starting point for someone who wants to progress later into algorithms, data structures, AI, or more advanced programming.


COURSE]]> https://mklab.gr/showthread.php?tid=493 Thu, 18 Jun 2026 23:21:07 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=493 Learn Calculus by Coding in Python 
by [freecocecamp]

Summary

The freeCodeCamp article “Learn College Calculus and Implement with Python” introduces a comprehensive calculus course taught by mathematics professor Ed Pratowski that combines traditional college-level calculus with hands-on Python programming.
 The course covers fundamental topics such as limits, derivatives, the chain and product rules, implicit differentiation, related rates, the Mean Value Theorem, Riemann sums, integrals, volumes of revolution, arc length, and surface area, while using Python and the SymPy library to graph functions, compute derivatives and integrals, and visualize mathematical concepts. 
By connecting theory with code, the course helps learners develop both a strong understanding of calculus and practical computational skills useful in fields such as engineering, data science, physics, and finance. 

COURSE]]> Learn Calculus by Coding in Python 
by [freecocecamp]

Summary

The freeCodeCamp article “Learn College Calculus and Implement with Python” introduces a comprehensive calculus course taught by mathematics professor Ed Pratowski that combines traditional college-level calculus with hands-on Python programming.
 The course covers fundamental topics such as limits, derivatives, the chain and product rules, implicit differentiation, related rates, the Mean Value Theorem, Riemann sums, integrals, volumes of revolution, arc length, and surface area, while using Python and the SymPy library to graph functions, compute derivatives and integrals, and visualize mathematical concepts. 
By connecting theory with code, the course helps learners develop both a strong understanding of calculus and practical computational skills useful in fields such as engineering, data science, physics, and finance. 

COURSE]]> https://mklab.gr/showthread.php?tid=473 Thu, 18 Jun 2026 08:01:11 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=473 Summary

Το μάθημα «Εισαγωγή στην Python» (CS1.1) του Mathesis είναι ένα ανοικτό διαδικτυακό μάθημα προγραμματισμού που έχει ως στόχο να εισαγάγει τους αρχάριους στις βασικές έννοιες της επιστήμης υπολογιστών μέσα από τη γλώσσα Python. 
Περιλαμβάνει θεμελιώδη στοιχεία όπως μεταβλητές, τύπους δεδομένων, δομές ελέγχου, συναρτήσεις, δομές δεδομένων και τεχνικές επίλυσης προβλημάτων, με πολλά παραδείγματα και ασκήσεις βήμα προς βήμα. Στις πιο προχωρημένες ενότητες ασχολείται με εφαρμογές όπως η επεξεργασία και ανάκτηση πληροφοριών από το διαδίκτυο. 
Το μάθημα προσφέρεται από το Κέντρο Ανοικτών Διαδικτυακών Μαθημάτων Mathesis των Πανεπιστημιακών Εκδόσεων Κρήτης, με διδάσκοντα τον Νίκος Αβούρης, και έχει σχεδιαστεί ώστε να μην απαιτεί προηγούμενη εμπειρία στον προγραμματισμό. 

COURSE]]> Summary

Το μάθημα «Εισαγωγή στην Python» (CS1.1) του Mathesis είναι ένα ανοικτό διαδικτυακό μάθημα προγραμματισμού που έχει ως στόχο να εισαγάγει τους αρχάριους στις βασικές έννοιες της επιστήμης υπολογιστών μέσα από τη γλώσσα Python. 
Περιλαμβάνει θεμελιώδη στοιχεία όπως μεταβλητές, τύπους δεδομένων, δομές ελέγχου, συναρτήσεις, δομές δεδομένων και τεχνικές επίλυσης προβλημάτων, με πολλά παραδείγματα και ασκήσεις βήμα προς βήμα. Στις πιο προχωρημένες ενότητες ασχολείται με εφαρμογές όπως η επεξεργασία και ανάκτηση πληροφοριών από το διαδίκτυο. 
Το μάθημα προσφέρεται από το Κέντρο Ανοικτών Διαδικτυακών Μαθημάτων Mathesis των Πανεπιστημιακών Εκδόσεων Κρήτης, με διδάσκοντα τον Νίκος Αβούρης, και έχει σχεδιαστεί ώστε να μην απαιτεί προηγούμενη εμπειρία στον προγραμματισμό. 

COURSE]]> https://mklab.gr/showthread.php?tid=472 Thu, 18 Jun 2026 07:55:34 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=472 Summary

The Python Tutorial from python-course.eu is a comprehensive free online course designed to teach Python programming from beginner to advanced levels through clear explanations, examples, exercises, and visual aids. It covers essential Python concepts such as syntax, variables, data types, operators, lists, dictionaries, sets, loops, functions, file handling, modules, packages, and error handling, while also introducing more advanced topics like object-oriented programming, generators, decorators, testing, and functional programming. The tutorial is suitable for self-study and aims to help both newcomers and experienced programmers understand Python deeply, not only how to write code but also how Python works internally and how to apply it in real projects.

COURSE]]> Summary

The Python Tutorial from python-course.eu is a comprehensive free online course designed to teach Python programming from beginner to advanced levels through clear explanations, examples, exercises, and visual aids. It covers essential Python concepts such as syntax, variables, data types, operators, lists, dictionaries, sets, loops, functions, file handling, modules, packages, and error handling, while also introducing more advanced topics like object-oriented programming, generators, decorators, testing, and functional programming. The tutorial is suitable for self-study and aims to help both newcomers and experienced programmers understand Python deeply, not only how to write code but also how Python works internally and how to apply it in real projects.

COURSE]]> https://mklab.gr/showthread.php?tid=466 Thu, 18 Jun 2026 07:28:36 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=466 Python απο το κανάλι CodeCrow.

PYTHON COURSE]]> Python απο το κανάλι CodeCrow.

PYTHON COURSE]]> https://mklab.gr/showthread.php?tid=302 Fri, 12 Jun 2026 19:43:05 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=302
An introduction to programming using a language called Python. Learn how to read and write code as well as how to test and "debug" it. Designed for students with or without prior programming experience who'd like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac.


COURSE PAGE itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50P, is entirely focused on programming with Python. You can take CS50P before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x!]]>
An introduction to programming using a language called Python. Learn how to read and write code as well as how to test and "debug" it. Designed for students with or without prior programming experience who'd like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac.


COURSE PAGE itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50P, is entirely focused on programming with Python. You can take CS50P before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x!]]>