#programming fundamentals python

[12neonlit-stage]

I go by no pronouns but not as in my name, more so like my pronouns are an undefined variable in shell coding

[bizarrocloudy]

I'm taking a programming class, and there was this chapter about converting between number systems, and I

[diptisinghblog]

The Fundamentals of Data Science: An Introduction for Aspiring Data Scientists

Embark on a journey into the dynamic world of data science, where insights from big data drive innovation and decision-making across industries. Discover the fundamentals of data science, from data collection and exploratory analysis to machine learning and data visualization, and unlock a world of lucrative career opportunities in this high-demand field...

[nyxbird]

i used codecademy to learn python, but i already had a bit of a background with js. i'm not entirely sure how good it is for cpp (i think i just kind of learned it through like. osmosis). anyway it'll be kind of difficult at first but it (for me at least) ends up being really freeing

If anyone has any c++ tutorials please send them to me. It's my first language and it's so hard to understand because of the way it's bring presented in class.

It's literally a problem solving class now with no help or input from other students or professors so you don't know it's wrong until you get a 0 for it for turning it in

I need major help like bad

[tanadrin]

i feel like. hmm. look. i'm a purely hobbyist, with no education or professional training in the field, so i might be talking out of my ass. but the fact that running python programs is extraordinarily sensitive to the specific installation of python you have, to the point where it now makes sense more often than not to create bespoke environments for each python program you want to run represents some kind of structural or conceptual failure. like. certainly the strength of python is the ability to create and easily use various modules/packages/whatever. but the fact that python scripts will cry and shit the bed and fail to work unless you have exactly the right little walled garden for them to frolick in seems like a problem. a fundamental failure of what is supposed to be a highly portable language. do other programming languages have this problem and i'm just not aware of it? or does it all come down to the fact that python isn't a compiled language like c++?

[thatdehydratedmedic]

Recent pursuits of knowledge - Mid June

Recently I've fallen into the typical academic rabbit hole where one thing leads to another and suddenly both your hands are full of more and more things to learn.

By a chance occurrence, a random google search led me to find a Maldivian scientific journal. Since my male audacity (I'm a girl, btw) has long convinced me that I'm in fact capable of anything and everything, I decided I would do a little research and try to publish it in the little time I have before I start medical school. (Which, by the way, is already less than 2 months.)

As it goes, dominoes fell, one by one. First I thought of doing a small study on medicinal herbs of the nation. Something along the lines of testing the effectiveness of 'Dhivehi beys'. However, a little voice in the back of my head would not stop whispering in my ear, begging me to try doing one about dreams. At this point in time, I had no idea what exactly I was looking for. I presented this barely-thought-out plan to my dear friend. She was on board, and she also shared that she also thought dreams were a lot more interesting of a subject than medicinal herbs.

So with a theme in mind, I began looking into past research and well established theories on our subject of study. I learnt quite a few interesting things about Bion's dream theory. Unfortunately, in the midst of preparing our google doc, something else caught my eye. Having shared the google doc containing everything I had found at that point with my friend, I decided to start learning the programming language R. I told myself it would prove to be useful in our study. (For reference, I had begun this adventure at around 10 PM, it was now 2 AM.)

Having learnt all the fundamentals and syntax of the language, I finally went to sleep at 4 AM. Next morning, I slept in. However, as soon as I woke up, I did some more reading on dreams and updates our shared google doc. After begging my friend to please read the damn document, I started working on R again.

For the next few days, I did not do any more reading on dreams. I have since forgotten about the matter if I'm being completely honest. On the other hand, I had found it much more interesting to tinker with R, trying out the different graphical features R had to offer.

You would think I stuck with perfecting R, and would have learnt it to some degree by now, but you could not possibly be more wrong because I am plagued by my interest in way too many things. A jack of all trades, a master of none. I am a museum of abandoned hobbies. R did not escape this cruel fate, because as luck would have it, I soon rediscovered my love for python.

I downloaded python and pygame. It was as if I had reconnected with an old friend.

I am contemplating on whether I should mention my brief encounter with html and css. Some of the very first languages I learnt. I started a simple and goal-less project website as I waited for python to install. It seems useless to mention as this only lasted 5 minutes - or maybe even less.

Today, it's been a week since all of this has occurred. And today, I have no interest in any of these. Which is why I painted today. I am a museum of abandoned hobbies, and sometimes I revisit my old friends.

[loser-female]

Hi! You mentioned there are online tutorials/sites for learning Comp Sci and OSINT - any recommendations? Thanks!

Hi! You got lucky that today is the day I check my inbox lol. So. For comp-sci: - CS50X from Harvard is a public avaiable course on youtube I'd 100% recommend. These are the undedited (so far) lectures of 2024: https://www.youtube.com/watch?v=3LPJfIKxwWc&list=PLhQjrBD2T381WAHyx1pq-sBfykqMBI7V4&ab_channel=CS50

This gives you all the basics you need to understand how coding and pcs in general works. (I had comp sci in my university course)

- Freecodecamp is a website full of FREE tutorials on several programming language. Advice: learn python. You will understand later on.

- The rest really depends on what you actually want to learn and why - each programming language has its own purpose and application. OSINT: -There is this full course: https://www.youtube.com/watch?v=qwA6MmbeGNo&t=205s&ab_channel=TheCyberMentor (But you can find more on youtube.) - Bellingcat's resources: https://www.bellingcat.com/category/resources/ -IntelTecniques: https://inteltechniques.com/ - Osint Newesletter: https://osintnewsletter.com/

But here is the catcher: if you plan to do osint it heavily depends on where you live. I'm in Europe, so it means I'm under GDPR, therefore I must abid to stricter regulations than a US OSINT analyst. A lot of data that might be considered public domain in the US(big one: conviction records) are not in europe, and you won't be able to access it unless you are a registered private investigator at least (but in my case it's rare that I go after people, that's not a part of any task I might encounter at work). Not only that, but a lot of the avaiable tools are designed to work only with specific countries in mind for various reasons and there is a big bias on US-based investigations. If youre' not in the US I recommend you reach out to your local OSINT or cybersecurity professionals association, they usually have resources and specific information, a lot of times for free. Also keep in mind: OSINT has a lot of different applications and it depends on what you're doing with it. Journalists might work more with satellites and images (a thing I know nothing about), debunkers will definitely understand social media more, if you do business intelligence you will look more at news sites, trademarks and deposits and so on to reach your conclusion. You did your course... Now what? I recommend getting on CTFs, like tracelabs that I've linked above, but there many of them (osint dojo for example) or Kase Scenarios. These are safe environments to practice on (except for tracelabs since it deal with actual cases of missing people and it can lead to... not so good leads, allow me to leave it there) You should also understand how intelligence (as the discipline) works. There are several resources, but my favourite is definitely Psychology of Intelligence Analysis. It's a series of declassified training documents from CIA analyst Richard Bauer, that was based on Daniel Kahneman (yes, the "thinking fast and slow" author, and I also do recommend this book) research on euristics. Intelligence is fundamental because OSINT might be helpful to gather the data, but the data then needs to be processed, analysed and you also need to get a conclusion from that analysis. Studying intelligence will help you avoid a lot of pitfalls that happens when you do an investigation, such as not understanding when you know enough, if you're being a victim of your own bias, if you're missing something or if you're going with the right approach. But I have to admit that the best of training I've received so far is from my local OSINT association because I've been able to train with people that work(ed) in the military, get their advice and have a deep understand of the work itself (and the reason why I have decided to actually make cyber threat intelligence my job, even if I work for a private company and I have no interest in working for the government). And yes ethics is a big thing for the OSINT community.

I hope this is helpful enough!

[womaneng]

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Learning to code and becoming a data scientist without a background in computer science or mathematics is absolutely possible, but it will require dedication, time, and a structured approach. ✨👌🏻 🖐🏻Here’s a step-by-step guide to help you get started:

1. Start with the Basics:

- Begin by learning the fundamentals of programming. Choose a beginner-friendly programming language like Python, which is widely used in data science.

- Online platforms like Codecademy, Coursera, and Khan Academy offer interactive courses for beginners.

2. Learn Mathematics and Statistics:

- While you don’t need to be a mathematician, a solid understanding of key concepts like algebra, calculus, and statistics is crucial for data science.

- Platforms like Khan Academy and MIT OpenCourseWare provide free resources for learning math.

3. Online Courses and Tutorials:

- Enroll in online data science courses on platforms like Coursera, edX, Udacity, and DataCamp. Look for beginner-level courses that cover data analysis, visualization, and machine learning.

4. Structured Learning Paths:

- Follow structured learning paths offered by online platforms. These paths guide you through various topics in a logical sequence.

5. Practice with Real Data:

- Work on hands-on projects using real-world data. Websites like Kaggle offer datasets and competitions for practicing data analysis and machine learning.

6. Coding Exercises:

- Practice coding regularly to build your skills. Sites like LeetCode and HackerRank offer coding challenges that can help improve your programming proficiency.

7. Learn Data Manipulation and Analysis Libraries:

- Familiarize yourself with Python libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization.

For more follow me on instagram.

[mojop24]

Why Learning Python is the Perfect First Step in Coding

Learning Python is an ideal way to dive into programming. Its simplicity and versatility make it the perfect language for beginners, whether you're looking to develop basic skills or eventually dive into fields like data analysis, web development, or machine learning.

Start by focusing on the fundamentals: learn about variables, data types, conditionals, and loops. These core concepts are the building blocks of programming, and Python’s clear syntax makes them easier to grasp. Interactive platforms like Codecademy, Khan Academy, and freeCodeCamp offer structured, step-by-step lessons that are perfect for beginners, so start there.

Once you’ve got a handle on the basics, apply what you’ve learned by building small projects. For example, try coding a simple calculator, a basic guessing game, or even a text-based story generator. These small projects will help you understand how programming concepts work together, giving you confidence and helping you identify areas where you might need a bit more practice.

When you're ready to move beyond the basics, Python offers many powerful libraries that open up new possibilities. Dive into pandas for data analysis, matplotlib for data visualization, or even Django if you want to explore web development. Each library offers a set of tools that helps you do more complex tasks, and learning them will expand your coding skillset significantly.

Keep practicing, and don't hesitate to look at code written by others to see how they approach problems. Coding is a journey, and with every line you write, you’re gaining valuable skills that will pay off in future projects.

FREE Python and R Programming Course on Data Science, Machine Learning, Data Analysis, and Data Visualization

[hackeocafe]

How to use ChatGPT in 2024 full tutorial

Begin your journey to being a ChatGPT Pro with our 12-hour ChatGPT Masterclass. This video covers everything from basics to advanced, starting with the fundamentals of ChatGPT, Generative AI, and Large Language Models (LLMs). You'll learn how to navigate ChatGPT's interface, delve into Prompt Engineering, and master effective prompting strategies. We introduce different ChatGPT versions (3.5, 4, 4o), their differences, and usage. You'll build programs, handle exceptions, test codes, and create Python apps and websites using ChatGPT 4o. Additionally, you'll analyze data with Python and Excel, simplify tasks in Excel and PowerPoint, create diverse content, and use ChatGPT for SEO, digital marketing, and finance. Finally, learn to create custom GPTs tailored to your needs

[ranjitha78]

The Complete Manual for Understanding Ethical Hacking

In order to evaluate an organization's defenses, ethical hacking—also referred to as penetration testing or white-hat hacking—involves breaking into computers and other devices lawfully. You've come to the correct spot if you're interested in finding out more about ethical hacking. Here's a quick start tutorial to get you going.

1. "Getting Started with the Basics"

Networking and computer science principles must be thoroughly understood before getting into ethical hacking. Here are some crucial aspects to pay attention to: Operating Systems: Acquire knowledge of several operating systems, with a focus on Linux and Windows. Learning Linux is essential because a lot of hacking tools are made to run on it. Networking: It's essential to comprehend how networks operate. Find out more about

protocols include HTTP, HTTPS, DNS, TCP/IP, and others. Understanding data flow across networks facilitates vulnerability detection. Programming: It's crucial to know at least a little bit of a language like Python, JavaScript, or C++. Writing scripts and deciphering the code of pre-existing tools are made possible by having programming expertise.

2. Making Use of Internet Resources To learn more about ethical hacking, there are a ton of internet resources available. Here are a few of the top ones: Online Education: Online learning environments such as Pluralsight, Coursera, and Udemy provide in-depth instruction in ethical hacking. "Penetration Testing and Ethical Hacking" on Pluralsight and "The Complete Ethical Hacking Course: Beginner to Advanced" on Udemy are two recommended courses. Channels on YouTube: HackerSploit, The Cyber Mentor, and LiveOverflow are just a few of the channels that offer helpful tutorials and walkthroughs on a variety of hacking tactics.

3. Exercising and Acquiring Knowledge The secret to being a skilled ethical hacker is experience. Here are some strategies to obtain practical experience:

Capture the Flag (CTF) Tournaments: Applying your abilities in CTF tournaments is a great idea. CTF challenges are available on websites like CTFtime and OverTheWire, with difficulty levels ranging from novice to expert. Virtual Labs: It is essential to set up your virtual lab environment. You can construct isolated environments to practice hacking without worrying about the law thanks to programs like VMware and VirtualBox. Bug Bounty Programs: Websites such as HackerOne and Bugcrowd link corporations seeking to find and address security holes in their systems with ethical hackers. Engaging in these initiatives can yield practical experience and financial benefits.

Dedication and ongoing education are necessary to learn ethical hacking. You can become a skilled ethical hacker by learning the fundamentals, using internet resources, and acquiring real-world experience. Always remember to hack wisely and ethically. Cheers to your hacking! I appreciate your precious time, and I hope you have an amazing day.

[souhaillaghchimdev]

Getting Started with Industrial Robotics Programming

Industrial robotics is a field where software engineering meets automation to drive manufacturing, assembly, and inspection processes. With the rise of Industry 4.0, the demand for skilled robotics programmers is rapidly increasing. This post introduces you to the fundamentals of industrial robotics programming and how you can get started in this exciting tech space.

What is Industrial Robotics Programming?

Industrial robotics programming involves creating software instructions for robots to perform tasks such as welding, picking and placing objects, painting, or quality inspection. These robots are typically used in factories and warehouses, and are often programmed using proprietary or standard languages tailored for automation tasks.

Popular Robotics Programming Languages

RAPID – Used for ABB robots.

KRL (KUKA Robot Language) – For KUKA industrial robots.

URScript – Used by Universal Robots.

Fanuc KAREL / Teach Pendant Programming

ROS (Robot Operating System) – Widely used open-source middleware for robotics.

Python and C++ – Common languages for simulation and integration with sensors and AI.

Key Components in Robotics Programming

Motion Control: Programming the path, speed, and precision of robot arms.

Sensor Integration: Use of cameras, force sensors, and proximity detectors for adaptive control.

PLC Communication: Integrating robots with Programmable Logic Controllers for factory automation.

Safety Protocols: Programming emergency stops, limit switches, and safe zones.

Human-Machine Interface (HMI): Designing interfaces for operators to control and monitor robots.

Sample URScript Code (Universal Robots)

# Move to position movej([1.0, -1.57, 1.57, -1.57, -1.57, 0.0], a=1.4, v=1.05) # Gripper control (example function call) set_digital_out(8, True) # Close gripper sleep(1) set_digital_out(8, False) # Open gripper

Software Tools You Can Use

RoboDK – Offline programming and simulation.

ROS + Gazebo – Open-source tools for simulation and robotic control.

ABB RobotStudio

Fanuc ROBOGUIDE

Siemens TIA Portal – For integration with industrial control systems.

Steps to Start Your Journey

Learn the basics of industrial robotics and automation.

Familiarize yourself with at least one brand of industrial robot (ABB, KUKA, UR, Fanuc).

Get comfortable with control systems and communication protocols (EtherCAT, PROFINET).

Practice with simulations before handling real robots.

Study safety standards (ISO 10218, ANSI/RIA R15.06).

Real-World Applications

Automated welding in car manufacturing.

High-speed pick and place in packaging.

Precision assembly of electronics.

Material handling and palletizing in warehouses.

Conclusion

Industrial robotics programming is a specialized yet rewarding field that bridges software with real-world mechanics. Whether you’re interested in working with physical robots or developing smart systems for factories, gaining skills in robotics programming can open up incredible career paths in manufacturing, automation, and AI-driven industries.

[izicodes]

Any tips on learning python? I already know Java, C++, and JavaScript.

Hiya! 💗

Since you already know those other languages, Python will be literally a piece of cake. It'll be easy for you, in my opinion.

Tips? I would say:

Start with the Basics: Begin by understanding the fundamental syntax and concepts of Python. After learning those, you can basically apply the languages you know logic into Python code and you'll be done. You can use online tutorials on YouTube or free online pdf books to get a good grasp of the basics.

Leverage Your Programming Experience:Like I mentioned Python shares similarities with many languages, so relate Python concepts to what you already know. For example, understand Python data types and structures in comparison to those in Java, C++, or JavaScript.

Projects and Practice:I sing this on my blog but practice is crucial. Start small projects or challenges to apply your Python knowledge. Depends what you want to build e.g. console apps, games, websites etc. Just build something small every so often!

Hope this helps! More tips I made: ask 1 | project ideas | random resources

⤷ ♡ my shop ○ my mini website ○ pinned ○ navigation ♡

[simerjeet]

Mastering Data Structures: A Comprehensive Course for Beginners

Data structures are one of the foundational concepts in computer science and software development. Mastering data structures is essential for anyone looking to pursue a career in programming, software engineering, or computer science. This article will explore the importance of a Data Structure Course, what it covers, and how it can help you excel in coding challenges and interviews.

1. What Is a Data Structure Course?

A Data Structure Course teaches students about the various ways data can be organized, stored, and manipulated efficiently. These structures are crucial for solving complex problems and optimizing the performance of applications. The course generally covers theoretical concepts along with practical applications using programming languages like C++, Java, or Python.

By the end of the course, students will gain proficiency in selecting the right data structure for different problem types, improving their problem-solving abilities.

2. Why Take a Data Structure Course?

Learning data structures is vital for both beginners and experienced developers. Here are some key reasons to enroll in a Data Structure Course:

a) Essential for Coding Interviews

Companies like Google, Amazon, and Facebook focus heavily on data structures in their coding interviews. A solid understanding of data structures is essential to pass these interviews successfully. Employers assess your problem-solving skills, and your knowledge of data structures can set you apart from other candidates.

b) Improves Problem-Solving Skills

With the right data structure knowledge, you can solve real-world problems more efficiently. A well-designed data structure leads to faster algorithms, which is critical when handling large datasets or working on performance-sensitive applications.

c) Boosts Programming Competency

A good grasp of data structures makes coding more intuitive. Whether you are developing an app, building a website, or working on software tools, understanding how to work with different data structures will help you write clean and efficient code.

3. Key Topics Covered in a Data Structure Course

A Data Structure Course typically spans a range of topics designed to teach students how to use and implement different structures. Below are some key topics you will encounter:

a) Arrays and Linked Lists

Arrays are one of the most basic data structures. A Data Structure Course will teach you how to use arrays for storing and accessing data in contiguous memory locations. Linked lists, on the other hand, involve nodes that hold data and pointers to the next node. Students will learn the differences, advantages, and disadvantages of both structures.

b) Stacks and Queues

Stacks and queues are fundamental data structures used to store and retrieve data in a specific order. A Data Structure Course will cover the LIFO (Last In, First Out) principle for stacks and FIFO (First In, First Out) for queues, explaining their use in various algorithms and applications like web browsers and task scheduling.

c) Trees and Graphs

Trees and graphs are hierarchical structures used in organizing data. A Data Structure Course teaches how trees, such as binary trees, binary search trees (BST), and AVL trees, are used in organizing hierarchical data. Graphs are important for representing relationships between entities, such as in social networks, and are used in algorithms like Dijkstra's and BFS/DFS.

d) Hashing

Hashing is a technique used to convert a given key into an index in an array. A Data Structure Course will cover hash tables, hash maps, and collision resolution techniques, which are crucial for fast data retrieval and manipulation.

e) Sorting and Searching Algorithms

Sorting and searching are essential operations for working with data. A Data Structure Course provides a detailed study of algorithms like quicksort, merge sort, and binary search. Understanding these algorithms and how they interact with data structures can help you optimize solutions to various problems.

4. Practical Benefits of Enrolling in a Data Structure Course

a) Hands-on Experience

A Data Structure Course typically includes plenty of coding exercises, allowing students to implement data structures and algorithms from scratch. This hands-on experience is invaluable when applying concepts to real-world problems.

b) Critical Thinking and Efficiency

Data structures are all about optimizing efficiency. By learning the most effective ways to store and manipulate data, students improve their critical thinking skills, which are essential in programming. Selecting the right data structure for a problem can drastically reduce time and space complexity.

c) Better Understanding of Memory Management

Understanding how data is stored and accessed in memory is crucial for writing efficient code. A Data Structure Course will help you gain insights into memory management, pointers, and references, which are important concepts, especially in languages like C and C++.

5. Best Programming Languages for Data Structure Courses

While many programming languages can be used to teach data structures, some are particularly well-suited due to their memory management capabilities and ease of implementation. Some popular programming languages used in Data Structure Courses include:

C++: Offers low-level memory management and is perfect for teaching data structures.

Java: Widely used for teaching object-oriented principles and offers a rich set of libraries for implementing data structures.

Python: Known for its simplicity and ease of use, Python is great for beginners, though it may not offer the same level of control over memory as C++.

6. How to Choose the Right Data Structure Course?

Selecting the right Data Structure Course depends on several factors such as your learning goals, background, and preferred learning style. Consider the following when choosing:

a) Course Content and Curriculum

Make sure the course covers the topics you are interested in and aligns with your learning objectives. A comprehensive Data Structure Course should provide a balance between theory and practical coding exercises.

b) Instructor Expertise

Look for courses taught by experienced instructors who have a solid background in computer science and software development.

c) Course Reviews and Ratings

Reviews and ratings from other students can provide valuable insights into the course’s quality and how well it prepares you for real-world applications.

7. Conclusion: Unlock Your Coding Potential with a Data Structure Course

In conclusion, a Data Structure Course is an essential investment for anyone serious about pursuing a career in software development or computer science. It equips you with the tools and skills to optimize your code, solve problems more efficiently, and excel in technical interviews. Whether you're a beginner or looking to strengthen your existing knowledge, a well-structured course can help you unlock your full coding potential.

By mastering data structures, you are not only preparing for interviews but also becoming a better programmer who can tackle complex challenges with ease.