Youcef Slimani

Data scientists are the new superheroes, unlocking the secrets hidden in big data basing on statistics. The statistics is the grammar of the science. With statistics you can't prove anything, but you can provide evidence.

Here are some key points on how data scientists can learn statistics effectively:

👉Foundation in Probability: Start by building a strong foundation in probability theory. Probability concepts, such as random variables, probability distributions, and conditional probability, form the basis for statistical inference and modeling.

👉Descriptive Statistics: Learn about descriptive statistics, which involve summarizing and describing data.

👉Statistical Inference: Master the techniques of statistical inference, which involves drawing conclusions about a population based on a sample. This includes understanding concepts like hypothesis testing, confidence intervals, p-values, and Type I and Type II errors.

👉Regression Analysis: Gain proficiency in regression analysis, which is used for modeling the relationship between variables. Learn about linear regression, multiple regression, and logistic regression.

👉Experimental Design: Familiarize yourself with experimental design principles. Learn how to design experiments, including randomization, control groups, and sample size determination.

👉Time Series Analysis: Explore time series analysis, which deals with analyzing and forecasting data that is collected over time.

👉Machine Learning and Statistical Models: Understand the relationship between machine learning algorithms and statistical models.

👉Practice with Real-World Data: Apply your statistical knowledge to real-world datasets.

👉Continuous Learning: Stay updated with the latest developments in statistics and data science.

A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician 👌👌👌

The real problem is not whether machines have intelligence but what men do with AI and especially whether programmers help them blindly to implement what they want do 🙅‍♂️

🚨Every profession has an order (order of doctors, order of engineers, orders of health workers…) but as programmers we don’t have in our countries an order which regulate our job and establish ethical rules to organize and to regulate to protect next generations. I think that in the era of AI we need it more than anytime😞

⏩What is the risk of AI for programmers ❓

⛔️Job Displacement: AI automation could reduce the need for human programmers.

⛔️Skill Obsolescence: Rapid AI advancements may make current skills outdated.

⛔️Ethical Dilemmas: Programmers might face conflicts in creating potentially harmful AI.

⛔️Complexity Management: Understanding and maintaining advanced AI systems can be challenging.

⛔️Security Risks: AI introduces new vulnerabilities that programmers must address.


⏩ What are responsibilities of programmers ❓

👉Say No to Harm: Refuse to create AI that can cause harm.

👉Combat Bias: Actively work to eliminate bias in AI systems.

👉Promote Transparency: Make AI decisions clear and understandable.

👉Follow Laws: Adhere to all relevant regulations and laws.

👉Support Sustainability: Develop environmentally friendly AI systems.

👉Protect Privacy: Ensure robust protection of user data.

👉Own Outcomes: Take responsibility for the impact of AI systems.

👉Prioritize Safety: Implement strict safety measures in AI.

👉Encourage Collaboration: Work with others to solve AI challenges.


Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Programming is a journey, not a destination👌

Enjoy the process. Every line of code you write is a step towards mastering your craft and every bug that you meet will empower your experience.

Below what make difficult daily programming life :

⛔️Tight deadlines and project pressures.
⛔️Complex or poorly documented codebases.
⛔️Scope creep and changing requirements.
⛔️Debugging elusive or difficult-to-find bugs.
⛔️Balancing multiple projects or tasks simultaneously.
⛔️Constantly evolving technologies and tools.
⛔️ Lack of support or resources from management or colleagues.

To deal with this daily situations take every task and every bug as a challenge and an opportunity to grow :

✅ Keep in your mind that setbacks are temporary, and challenges can be overcome with persistence and resilience.

✅Utilize relaxation techniques like deep breathing or mindfulness.

✅Prioritize tasks and focus on what's most critical.

✅Break down larger tasks into smaller, manageable steps.

✅Communicate openly with your team about challenges and progress.

✅Practice time management techniques to stay organized.

✅Take regular breaks to recharge and avoid burnout.

✅Keep a positive mindset and focus on solutions rather than dwelling on problems.

✅Seek support from colleagues or mentors when feeling overwhelmed.

✅Set realistic expectations and deadlines for yourself and others.

✅Learn from mistakes and use them as opportunities for growth.

✅Maintain a healthy work-life balance to prevent excessive stress.

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

From pixels to processors, JavaScript is the thread that weaves our tech. n the grand symphony of technology, JavaScript plays a melody heard across devices

JavaScript is a versatile programming language with a wide range of uses.

👉Web Development:JavaScript is primarily used for creating interactive and dynamic web pages. It allows developers to add behaviors and features that respond to user actions, such as form validation, animations, and real-time updates.

👉Front-End Development:It's an essential component of front-end web development, enabling the creation of user interfaces, handling user interactions, and enhancing the user experience.

👉Back-End Development:With technologies like Node.js, JavaScript can be used for server-side programming, allowing developers to build scalable and efficient back-end systems.

👉Web Applications:JavaScript frameworks and libraries like React, Angular, and Vue.js are used to build complex single-page application (SPAs) that offer a smooth user experience.

👉Mobile App Development:Technologies like React Native and Apache Cordova enable developers to use JavaScript to build cross-platform mobile applications.

👉Game Development:JavaScript, along with HTML5 canvas or WebGL, can be used to create browser-based games and interactive multimedia experiences.

👉Server-Side Scripting:Node.js allows developers to run JavaScript on the server, enabling real-time applications, chat applications, and APIs.

👉Browser Extensions:JavaScript is used to create browser extensions that enhance the functionality of web browsers.

👉Data Visualization:Libraries like D3.js help developers create dynamic and interactive data visualizations on websites.

👉Web APIs:JavaScript is used to interact with various web APIs, allowing websites to pull in data from external sources or provide data to other services.

👉Automation:JavaScript can be used for automating tasks in web browsers, like filling out forms or interacting with web pages programmatically.

👉IoT (Internet of Things):JavaScript can be used to program IoT devices and interact with them through web interfaces.

👉Web Servers and APIs:Technologies like Express.js allow developers to create web servers and APIs using JavaScript for routing, handling requests, and managing data.

👉Chatbots:JavaScript can power chatbot interactions on websites, providing instant responses and assistance to users.

👉Real-Time Applications:JavaScript's event-driven nature makes it suitable for building real-time applications such as messaging platforms and collaborative tools.

Warnings are the programmer’s early detection system. Ignore them at your peril 👌

Why Warnings Matter ❓

👉Prevent Bugs: Warnings highlight potential issues that could escalate into significant bugs if left unaddressed. Fixing them early prevents future problems.

👉Enhance Security: Addressing warnings promptly can close security gaps that might be exploited by attackers.

👉Improve Maintenance: A codebase with fewer warnings is cleaner and easier to understand, reducing technical debt and making future updates simpler.

Treating warnings as errors is a proactive way to keep your codebase healthy:

✅Enable All Warnings: Configure your development environment to show all warnings. This ensures you are aware of all potential issues.

✅Treat Warnings as Errors: Adjust settings to treat warnings as errors during the build process, forcing immediate resolution.

✅Regular Code Reviews : Incorporate warnings checks as part of code reviews to ensure that all warnings are addressed before merging.

✅Automated Testing: Integrate static analysis tools and linters into your CI/CD pipeline to automatically catch and address warnings.

✅Use Linting Tools: Implement linters specific to your programming language to automatically identify and fix warnings.


✅Develop a coding standard document that includes rules for handling and resolving warnings, ensuring everyone on the team adheres to these guidelines.

Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. S.Jobs

Managing a programming career is an ongoing process that requires adaptability and a commitment to continuous improvement.


👉Stay updated on industry trends.

👉Regularly acquire new programming languages and frameworks.

👉Engage in online courses, workshops, and attend conferences.

👉Understand data structures and algorithms thoroughly.

👉Choose a niche or specialization based on interest or demand.

👉Explore diverse projects to broaden your skill set.

👉Showcase your projects on platforms like GitHub.

👉Seek feedback from peers and mentors to identify areas for improvement.

👉Be open to constructive criticism and use it for growth.

👉 Attend meetups, conferences, and online forums. Connect with peers, mentors, and potential employers.

👉Hone communication and teamwork skills. Learn to explain technical concepts to non-technical stakeholders.

👉Embrace change and be open to new technologies.Be willing to pivot to new roles or industries if needed.

👉Set short and long-term career goals.

👉 Regularly reassess and adjust your career plan.

👉Avoid burnout by maintaining a healthy work-life balance.

👉Prioritize self-care, including exercise and adequate sleep.

The right debugging tool can transform complex debugging into simple routine, Choose the right tools, and the problem becomes just a matter of time 👌

👉Integrated Debugging Tools :

Visual Studio Code, PyCharm, IntelliJ IDEA: Offer breakpoints, step execution, and variable inspection for various languages.

👉Logging and Monitoring:

🔦ELK Stack (Logstash, Kibana), Splunk: Centralized logging and analysis to trace issues.

🔦Sentry, New Relic: Real-time error tracking and performance monitoring.

👉Remote Debugging

🔦 Chrome DevTools: Debug web apps directly from the browser.

🔦Visual Studio Code Remote Debugging:Debug applications running on remote servers or containers.

🔦gdb:Remote debugging for C/C++ and embedded systems.

👉Memory and Performance Profiling:

🔦Valgrind, Perf (Linux): Tools for memory leak detection and performance analysis.

🔦YourKit: Java profiler for CPU and memory usage.

👉Unit Testing and TDD:

✅JUnit, PyTest, Mocha: Frameworks for unit testing, integrated with CI tools like Jenkins or GitHub Actions.

👉Automated Error Reporting

🔦BugSnag, Raygun: Automatically captures and reports application errors.

👉Distributed Tracing
🔦Jaeger, Zipkin : Trave requests across microservices to debug latency and performance issues.

🔦OpenTelemetry: Collects and traces observability data.

👉Interactive Debugging:

🔦IPython Debugger (pdb): Tools for interactive and inline code debugging.

👉Static Analysis and Linting:

🔦SonarQube,ESLint, Pylint: Static code analysis tools to catch bugs and enforce coding standards.

⏩Think to approaches below for an efficient debugging :

✅Proactive Debugging:Incorporate logging and monitoring early.

✅Version Control: Use Git to track changes and identify bug origins.

✅Scientific Method: Test hypotheses by isolating variables.

✅Collaborative Debugging: Pair programming and code reviews.

✅Automate Tests: Integrate testing into CI/CD pipelines.

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

The art of programming is the skill of controlling complexity. This skill take time to develop, below some advices for this long journey to become better programmer:

👉Start by the Fundamentals: Ensure you have a strong grasp of the fundamentals of programming, including data structures, algorithms, and core concepts in your chosen programming language.

👉Set your Goals: Define specific, measurable, and achievable learning goals. What skills or projects do you want to accomplish in the short term and long term?

👉Choose a Specialization: Decide on a specific area or technology to specialize in, such as web development, mobile app development, data science, machine learning, etc. Focus on what genuinely interests you.

👉Select the Right Resources: Find high-quality learning resources such as online courses, textbooks, tutorials, and documentation relevant to your chosen specialization. Platforms like Coursera, edX, Udacity, and freeCodeCamp offer many advanced courses.

👉Practice Problem Solving: Work on algorithmic problems and coding challenges on platforms like LeetCode, HackerRank, or CodeSignal. These platforms help improve your problem-solving skills, which are crucial in advanced programming.

👉Seek Mentorship: If possible, find a mentor or join a community of like-minded learners. Mentorship can provide valuable guidance and support.

👉Learn from Mistakes: Don't be afraid to make mistakes. Learning from errors is a crucial part of growth. Analyze what went wrong and how you can improve next time.

👉Teach Others: Teaching is a powerful way to reinforce your own learning. Share your knowledge through blog posts, tutorials, or mentoring junior developers.

👉Stay Disciplined: Maintain a consistent study schedule and set aside dedicated time for learning and practice. Avoid procrastination.


👉Practice Regularly: The key to mastering advanced skills is consistent practice. Work on coding projects, challenges, or exercises regularly to reinforce your knowledge and skills.

👉Work on Real Projects: Apply what you learn by building real-world projects. This practical experience will deepen your understanding and demonstrate your abilities to potential employers or collaborators.

👉Collaborate and Seek Feedback: Collaborate with others on coding projects or contribute to open-source projects. Receiving feedback from experienced developers can help you learn faster and improve your skills.

👉Stay Updated: Technology evolves rapidly. Stay current by following industry news, blogs, podcasts, and attending conferences or meetups related to your specialization.

👉Measure Progress: Periodically assess your progress toward your learning goals. Adjust your approach if necessary.

👉Stay Curious: Cultivate a curious mindset. Explore new technologies and programming languages. Experiment and tinker with different tools and frameworks.

Bug doesn’t matter ?

Or also Bugs doesn’t matter (if grammar doesn’t matter😁)

One of the golden rule in programming is :

🔥 Don’t code today what you can’t debug tomorrow.🔥

Below some of advices to improve yourself every day :

👉Master Your Tools: Become proficient in the programming languages, frameworks, and tools relevant to your field.

👉Problem-Solving Skills: Develop strong problem-solving skills to efficiently tackle coding challenges.

👉Debugging Proficiency: Sharpen your debugging skills to identify and fix issues quickly.

👉Algorithmic Understanding: Develop a strong understanding of algorithms and data structures for efficient problem-solving.

👉Code Readability: Write clean and readable code; it helps you and others understand and maintain it.

👉Time Management: Prioritize tasks, set deadlines, and manage your time effectively to stay productive.

👉Continuous Learning: Stay updated with industry trends, new technologies, and best practices to enhance your skills.

👉Testing: Embrace testing methodologies to ensure the reliability and correctness of your code.

👉Communication Skills: Effectively communicate with team members, stakeholders, and document technical decisions.

👉Collaboration: Foster a collaborative mindset, sharing knowledge and learning from your peers.

👉Stay Organized: Keep your codebase organized, use consistent naming conventions, and structure your projects logically.

👉Attention to Detail: Pay attention to details to avoid introducing errors and to produce high-quality code.

Efficiency comes from a combination of technical expertise, good practices, and effective collaboration.

ChatGPT can assist, but the ingenuity, intuition, and expertise of a human programmer remain irreplaceable. Use AI for boring and repetitive tasks and focus on innovation and most complex stuffs ⚡️

Consider folllowing points about ChatGPT :

⛔️Complex Contexts : Struggles with large codebases.

⛔️Complex Logic: Struggles with intricate algorithms.

⛔️Innovation: Suggests conventional solutions

⛔️Framework Depth: Shallow on specific frameworks.

⛔️Real-time Debugging: Can't debug live code.

⛔️Outdated Knowledge: Lacks recent updates.

⛔️No Code Execution: Can't run or test code.

⛔️Niche Libraries: Limited with custom libraries.

⛔️Generic Errors: Provides broad error advice.

⛔️Security: May not follow best practices.

⛔️Optimization: Offers less efficient code.


So consider some ajustements :

✅Verify with current resources.

✅ Consult official docs and forums.

✅ Brainstorm for creative solutions.

✅ Utilize advanced IDE features.

✅ Investigate with detailed error messages.

✅ Security: Follow updated security practices.


Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Almost every program will work again and every bug will be solved if you unplug it for a few minutes, including you 🧘🏻‍♂️🧘‍♀️🧘

Avoid burnout because your life and family is must important than other things.

👉Take Regular Breaks ⏲️: Use techniques like the Pomodoro method to rest and recharge.

👉Set Boundaries 🚧:Define clear work hours and stick to them to maintain work-life balance.

👉Prioritize Health 🏋️‍♀️: Exercise regularly, eat healthily, and get sufficient sleep.

👉Manage Workload 📅:Break tasks into manageable chunks and avoid multitasking.

👉Stay Organized📋:Use tools like to-do lists and project management software to stay on track.

👉Seek Support 🤝:Talk to colleagues or mentors when feeling overwhelmed.

👉Learn to Say No❌:Don’t overcommit to tasks and projects.

👉Take Time Off 🌴:Use vacation days to fully disconnect from work.

👉Engage in Hobbies 🎨: Pursue non-work-related interests to relax and unwind.

👉Practice Mindfulness 🧘:Incorporate stress-reducing activities like meditation or yoga into your routine.

Remember that Burnout is not the price you have to pay for success🤙

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Automation liberates developers from manual testing shackles, enabling them to focus on innovation and code craftsmanship.

Automating tests is an essential practice for Python developers to ensure the quality and stability of their code. Here's a general process for automating tests:

👉Choose a Testing Framework: Python offers several testing frameworks such as pytest, unittest, and nose. Choose the one that suits your needs and preferences.

👉Write Test Cases: Create test cases that cover different aspects of your code. Test cases should be comprehensive, focusing on edge cases, input validation, and expected behaviors.

👉Implement Test Fixtures: Test fixtures are pre-defined setup and teardown steps that help create a consistent testing environment.

👉Write Assertions: Assertions are statements that verify the expected behavior of your code. Use assertions to compare actual results with expected values.

👉Run Tests Locally: Execute your test suite locally to validate that your tests are running correctly and providing the expected results.

👉Continuous Integration (CI): Set up a CI system, such as Jenkins, Travis CI, or GitHub Actions, to automate the execution of your tests on every code commit or pull request. CI ensures that tests are run in a consistent environment and helps catch any regressions introduced by new code changes.

👉Test Coverage: Monitor test coverage to ensure that your tests adequately cover your codebase. Use tools like coverage.py or pytest-cov to measure the percentage of code covered by your tests.

👉Test Maintenance: As your codebase evolves, update and maintain your tests accordingly.

👉Test Suites and Test Organization: Organize your tests into logical test suites based on modules, features, or components.

👉Test Driven Development (TDD): Consider adopting Test Driven Development as a practice. With TDD, you write tests before implementing the actual code. This approach ensures that your code is testable, improves code design, and helps you think through requirements and edge cases before implementation.

So Automate your tests to catch bugs before they catch you 👌👌👌

Web development is not just about creating pretty layouts. It’s about understanding the user, solving problems, and building an experience that leaves an impression 👌

Below some challenges of learning web development with a JavaScript stack:

👉The JavaScript ecosystem is vast, with numerous libraries, frameworks, and tools available.

👉JavaScript frameworks and tools evolve rapidly. Keeping up with the latest updates and best practices can be challenging, leading to potential obsolescence of skills if not consistently maintained.

👉 Web development often involves both frontend and backend technologies. Learning the full stack, including server-side technologies, databases, and deployment processes, can be daunting for beginners.

👉Dealing with browser-specific quirks and ensuring cross-browser compatibility can be time-consuming.

👉JavaScript heavily relies on asynchronous programming, especially with the advent of Promises and async/await.

👉Debugging JavaScript applications, especially in complex frameworks, may pose difficulties.

👉Web developers need to be aware of security best practices to prevent common vulnerabilities. This includes understanding concepts like Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF).

👉Organizing code in a scalable and maintainable way is crucial.

👉Popular frontend frameworks like React, Angular, or Vue.js have their learning curves.

👉Relying on community support and documentation is essential, but sometimes the information can be overwhelming or outdated.

Certainly! Here are some advice for better learning in web development with a JavaScript stack:

✅Start with Fundamentals to Ensure a strong foundation in HTML, CSS, and JavaScript basics.

✅Hands-On Practice is key to reinforcing concepts and gaining confidence.

✅Build Real Projects: and your knowledge by working on real-world projects.

✅Understand How Things Work and avoid code memorization .

✅Follow a Learning Path:** and Progress systematically.

✅Give importance to documentation.

✅Join Developer Communities:

✅Stay Updated.

✅Break Down Problems.

✅Consistency is Key so allocate consistent time to learning it.

✅Learn to Debug Effectively.

Good judgment comes from experience, experience comes from bad judgment and the only thing worse than testing in production is not testing in production with user context.

Common Mistakes Before final user Deployment

👉Happy Path Focus: Ignoring failure scenarios.

🔦 Messaging app fails in low connectivity, leading to unsent messages in production.


👉Inadequate UAT: Not involving real users.

🔦Users find the final interface confusing because real user feedback wasn't incorporated during testing.

👉Incomplete Test Coverage: Missing edge cases.

🔦 Missing tests for invalid credit card entries leads to failed transactions in production.

👉Unrealistic Test Data: Not reflecting real-world scenarios.

🔦 App crashes in production when users input special characters not tested during development.

👉Environment Mismatch: Testing in non-production environments.

🔦 A web app works in testing but times out in production due to slower real-world networks.

👉Lack of Integration Testing: Missing inter-component issues.

🔦 API fails in production due to untested data structure mismatches with the existing database.

👉Skipping Non-Functional Testing: Overlooking performance and security.


🔦Website crashes when hit with unexpected high traffic after a promotion.

Other cases :
🔦A minor change triggers a major system crash in production.

🔦Unpatched vulnerabilities are exploited after going live.

🔦An untested edge case causes database corruption during live transactions.

🔦Server overload leads to unexpected downtime due to lack of load testing.

🔦Users abandon the app due to unresolved performance issues discovered post-launch.

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Show me the code you write, tell you which kind of programmer you are.

Clean and robust has characteristics below, try to follow those critera for a better software craft:

👉Readability: Easily understandable code ( clear names…)

👉Modularity: Divided into reusable components.

👉Consistency: Uniform coding style.

👉Simplicity: Avoidance of unnecessary complexity.

👉Efficiency: Optimized performance.

👉Error Handling: Effective handling of exceptions.

👉Testing: Comprehensive test coverage(unit tests, end to end …)

👉Scalability: Adaptable to changing requirements (Solid principles, design patterns…)

👉Security: Protection against vulnerabilities( Sql injection,XSS…)

👉Dependency Management: Proper handling of dependencies.

👉Maintainability: Ease of maintenance and updates.

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Debugging is like being a doctor, but for software. Except software can’t tell you where it hurts😉

Below how to proceed during debugging sessions :


👉Stay Calm : Keep composed to think clearly.

👉Prioritize : Focus on critical issues first.

👉Break It Down: Divide the problem into smaller parts.

👉Use Tools: Utilize debugging tools efficiently.

👉Review Changes: Check recent code changes.

👉Consult Documentation: Refer to relevant resources.
👉Pair Programming: Collaborate with a colleague.

👉Take Breaks: Step away briefly to regain focus.

👉Stay Positive: Believe in your problem-solving abilities.

👉Manage Time: Set time limits for tasks.

👉Ask for Help: Don't hesitate to seek assistance.

After fixing bug :

👉Test Solutions: Ensure fixes work without new issues.

👉Document Findings: Keep notes for future reference.

👉Reflect Afterwards: Learn from the experience for improvement.

The only thing worse than training your programmers and having them leave is not training them and having them stay HF👌

Distribute knowledge and responsibilities, making the team more resilient and less dependent on a few key developers :

👉Regularly hold knowledge-sharing sessions. Knowledge increases by sharing but not by saving 🤙

👉Train developers to work across different parts of the codebase. If a programmers want something new, they have to stop doing something old ✔️


👉 Ensure backup personnel for key roles and responsibilities 🛟

👉Rotate team roles and responsibilities periodically 🔄

👉Maintain detailed and up-to-date documentation 📜

👉Implement mandatory peer code reviews 🧐

👉Automate repetitive tasks to reduce reliance on specific individuals 🤖

👉 Encourage pair programming to share expertise.

👉Establish mentorship programs for continuous learning.

👉Use collaborative tools like wikis and shared repositories.

👉Hold regular team meetings to discuss ongoing projects and issues.

👉 Foster a culture of inclusivity and collective responsibility. Diversity is being invited to the party, inclusion is being asked to dance 🕺

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Saying that programmers write only code is like saying doctors just write prescriptions 👌

What can a programmer do else then writting code ?

👉Debugging: Identifying and fixing bugs in the software.

👉Testing: Writing and running tests to ensure the software functions correctly.


👉Code Review: Reviewing peers' code to ensure quality and adherence to coding standards.

👉Documentation: Writing documentation for code, APIs, and software usage.

👉Meetings: Participating in meetings for planning, progress updates, and team collaboration.

👉Learning and Training: Keeping up with new technologies, tools, and best practices.

👉Design and Architecture: Planning and designing software architecture and systems.

👉Version Control Management: Using version control systems like Git to manage code changes and collaboration.

👉Project Management: Using tools like JIRA or Trello to track project progress and tasks.

👉Collaboration: Working with other team members, including designers, product managers, and other developers.


Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Using i and j in loop is not a programning law that you have to know but you should know laws and rules below :

👉DRY (Don't Repeat Yourself): Avoid duplicating code to promote maintainability and reduce errors.

👉YAGNI (You Aren't Gonna Need It): Only implement features when they are needed, rather than speculating about future requirements.

👉 Postel’s law : Be conservative in what you send, be liberal in what you accept. This means that when sending data, one should be strict and careful in producing that data. However, when receiving data, one should be more lenient and tolerant, accepting a broad range of inputs.

👉KISS (Keep It Simple, Stupid):Keep code simple and straightforward, avoiding unnecessary complexity.

👉Law of Demeter (LoD): A module should not know about the internal details of the objects it manipulates, fostering loose coupling.

👉Boy Scout Rule: Leave the code cleaner than you found it.

👉 Pareto Principle (80/20 Rule): Focus on the most critical 20% that yields 80% of the results.

👉 Composition Over Inheritance: Favor composition of objects over class inheritance for better flexibility.

👉 Fail Fast: Identify and report errors as soon as they occur, making debugging easier.

👉 CQRS (Command Query Responsibility Segregation): Separate the read and write operations for improved system scalability.

👉 Write the code and the test : Testing leads to failure, and failure leads to understanding.

Keep in your mind: A good programmer is someone who always looks both ways before crossing a one-way street 🔥

You don’t have to be a genius to get started, but you do need a strong foundation in the basics.



⚙️Mathematics:

👉Linear Algebra: Vectors, matrices, and operations.

👉Calculus: Differentiation and integration, especially for optimization.

👉 Probability and Statistics: Distributions, Bayes theorem, and hypothesis testing.

⚙️Programming Skills:

👉Python: The most commonly used language for machine learning.

👉Libraries: NumPy, pandas, scikit-learn, TensorFlow, Keras, PyTorch.

⚙️ Data Handling:

👉Data Cleaning: Handling missing data, outliers, and transformations.

👉Exploratory Data Analysis (EDA): Using visualization tools like Matplotlib and Seaborn.

⚙️Machine Learning Algorithms:

👉Learn the basics of supervised (e.g., linear regression, decision trees) and unsupervised learning (e.g., k-means, PCA).

👉Understanding of Machine Learning Concepts:

👉 Overfitting vs. Underfitting

👉Model Evaluation: Accuracy, precision, recall, confusion matrix, cross-validation.

⚙️ Basic Software Tools:

👉Jupyter Notebooks: For writing and running Python code interactively.

👉 Git: For version control and collaboration.

⚙️Datasets:

👉Access to real-world datasets for practice (e.g., Kaggle, UCI Machine Learning Repository).

Finally you need : Patience and Practice 👌



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Debugging is like being the detective in a crime movie where you are also the murderer. It is like untangling a knot; sometimes you have to take a step back, look at the bigger picture, and approach it from a different angle.

The art of debugging lies in the ability to embrace uncertainty, stay curious, and persevere until the problem is solved.

Below some ideas:

👉Rubber Duck Debugging: This technique involves explaining your code or the problem you're facing in detail to an inanimate object like a rubber duck. By verbalizing the issue step-by-step, you often gain insights and identify the solution yourself. The process of explaining the problem helps you understand it better.

👉Take Breaks and Seek Help: If you're feeling stuck or frustrated, taking breaks can provide mental clarity and help you approach the problem with a fresh perspective. Additionally, don't hesitate to seek help from colleagues, online communities, or forums when you've exhausted your options.


👉Breakpoint Debugging: Set breakpoints in your code at specific points where you suspect an issue might be occurring. By running the program in a debugger and stepping through the code, you can track the execution flow, inspect variable values, and identify the precise location of the bug.

👉Logging and Print Statements: Inserting print statements or using logging frameworks in your code helps you trace its execution. By examining the output or log messages, you can identify unexpected behavior or incorrect values and narrow down the bug's location.

👉Code Review: Engage a colleague or fellow developer to review your code. A fresh pair of eyes can often spot issues or suggest alternative approaches. Code reviews can help identify logical errors, bad practices, or inefficiencies in your code.

👉Divide and Conquer: If you have a large and complex codebase, divide the problem into smaller parts and isolate specific sections for testing. This approach allows you to narrow down the scope of your investigation and focus on specific modules or functions.

👉Unit Testing: Writing comprehensive unit tests for your code can help catch bugs early in the development process. Well-designed tests can validate the expected behavior of individual components or functions and provide quick feedback on any regressions or issues introduced.

👉Debugging Tools: Utilize debugging tools specific to your programming language or development environment. These tools often provide features like stepping through code, examining variables, and inspecting memory to aid in locating and fixing bugs.

👉Pair Programming: Collaborate with another developer to work together on solving the problem.

👉Review Documentation and External Resources: Consult relevant documentation, official documentation for libraries or frameworks you are using, and online resources such as forums or Q&A websites.

The strength of JavaScript is that you can do anything. The weakness is that you will. So be careful when you write Javascript code.


Below some tips for a better JavaScript programming experience :

🚨See the last section for Typescript 🚨

👉Use Strict Mode: Always enable strict mode at the top of your scripts or functions. It helps catch common coding mistakes and prevents the use of potentially problematic features.

👉Declare Variables Properly: Always use `let`, `const`, or `var` (reduce uses of var when possible) to declare variables. Avoid relying on implicit global variables.

👉 Do your best effort when naming to make your code readable.

👉Avoid Global Variables:Minimize the use of global variables to prevent unintended interactions between different parts of your code.

👉Use Descriptive Variable and Function Names: Choose meaningful names for variables and functions. This improves code readability and makes it easier to understand your code's purpose.

👉Comment Your Code: Add comments to explain complex sections of your code, especially if it's not immediately obvious what the code does.

👉Indentation and Formatting: Maintain consistent indentation and formatting. This makes your code easier to read and reduces the risk of syntax errors.

👉Avoid Magic Numbers and Strings: Replace magic numbers and strings with named constants or variables to make your code more maintainable.

👉Error Handling: Implement proper error handling using `try...catch` blocks to gracefully handle exceptions and prevent crashes.


👉Use Functions for Code Reusability: Write functions to encapsulate and reuse code. Avoid duplicating code blocks.

👉Check Types and Use Type Coercion Wisely: Be aware of JavaScript's type system, and use `typeof`, `instanceof`, or functions like `isNaN()` to validate data types before operations.

👉Avoid Asynchronous Callback Hell: Use promises, async/await, or libraries like `async.js` to manage asynchronous code and avoid deeply nested callback structures.

👉Test Your Code: Implement unit tests and automated testing frameworks like Jest to catch bugs early and ensure your code behaves as expected.

👉Code Reviews: Encourage code reviews within your team. Fresh perspectives can help identify potential issues.

👉Use a Linter: Employ a JavaScript linter like ESLint to enforce coding standards and catch common mistakes.

👉Keep Dependencies Updated: Regularly update your project's dependencies to patch security vulnerabilities and ensure compatibility.

👉Optimize for Performance: Be mindful of performance bottlenecks and optimize critical sections of your code when necessary.

Finally :

Did you know that according to a study carried out in 2017, 15% of bugs fixed in Javascript projects on Github could have been avoided with Typescript?

Better yet, a six-month post-mortems survey at AirBnb revealed that 38% of them could have been avoided by Typescript.

So why hesitate to embark on a javascript migration to typescript?

Tests allow developers to modify code without fear of breaking existing functionality. Isolated failures in tests can pinpoint exactly where bugs occur. Automated tests prevent old bugs from reappearing after updates 👌

Reasons why programmers should write tests again and again :

👉 Catch bugs early: Tests help detect issues during development, not after deployment.

👉 Ensure code quality: They provide a safety net, ensuring code behaves as expected.

👉 Confidence in refactoring: Tests allow developers to modify code without fear of breaking existing functionality.

👉 Document behavior: Tests serve as living documentation for how the system is supposed to work.

👉 Faster debugging: Isolated failures in tests can pinpoint exactly where bugs occur.

👉 Better collaboration: Tests define clear expectations for how code should behave, aiding teamwork.

👉 Reduce regression issues: Automated tests prevent old bugs from reappearing after updates.

👉 Improve design: Writing tests often leads to cleaner, more modular code structures.

👉 Boost productivity: Though upfront time is required, testing saves time in the long run by reducing manual QA efforts.

👉 Meet business requirements: Tests ensure that critical business functionality works as intended.


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Code reviews should be a conversation, not a critique. The goal is to improve the code, not to prove who’s right.

A good code review isn’t just about finding bugs, it’s about fostering a culture of continuous learning and improvement, lets share some key rules to master the art of code review:

👉Prioritize Clarity and Simplicity: If the code isn't clear at first glance, it needs refactoring.

🔦 Clear code is easier to maintain and reduces the risk of introducing bugs.

👉Be Mindful of the Reviewer’s Time:
Submit manageable chunks of code for review, not huge, monolithic commits.

🔦Smaller reviews are quicker and less prone to errors, making them easier for the reviewer.


👉Avoid Nitpicking: Focus on the significant aspects of the code rather than minor issues like formatting.

🔦Nitpicking can slow down the review process and frustrate the coder.

👉Keep Reviews Timely:Perform code reviews promptly.

🔦Delayed reviews can block progress and slow down the development cycle.

👉Focus on the Code, Not the Coder: Critique the code, not the person.

🔦Keeping the feedback objective helps maintain a collaborative and respectful atmosphere.

👉Be Constructive, Not Just Critical: Offer suggestions for improvement instead of only pointing out flaws.

🔦 Constructive feedback helps developers grow and find better solutions.

👉Enforce Coding Standards Consistently: Adhere to your team's coding standards without exception.

🔦Consistent coding styles improve readability and reduce cognitive load.

👉Consider Performance and Security: Always check if the code could be optimized or if there are any potential security vulnerabilities.

🔦Early identification of performance issues or security risks saves time and resources later.

👉Test Coverage and Quality: Ensure that new code is well-tested with meaningful tests.

🔦High test coverage ensures that the code works as expected and helps catch future bugs.

👉Encourage Knowledge Sharing:
Use code reviews as an opportunity to share knowledge and best practices.

🔦This helps junior developers learn and promotes a culture of continuous improvement.

i know that sometimes its confusing for programmers so see the picture to refresh the memory… An image is better than 1000 words👌

Every expert was once a beginner. You never get a second chance to make a first impression.

First day at work is always stressful so let’s share some advices :


👉 Arrive Early: For a 9 a.m. start, aim for 8:45 a.m.—a small cushion lowers stress.

👉 Ask for Clarification: If someone mentions “CI/CD pipelines,” ask, “Could you explain how we handle deployments here?”

👉 Don’t Overpromise: If asked to debug legacy code, say, “I’ll need to review this and get back to you.”

👉 Engage with Teammates: Join lunch if invited; it helps you feel part of the team faster.

👉 Ask Questions Early: For example, “What’s the priority for my onboarding tasks?” clarifies expectations.

👉 Listen Actively: When someone explains their workflow, repeat it back

👉 Stay Patient: If a tool doesn’t work, ask calmly, “Who can help with setting this up?”

👉 Take Feedback Gracefully: If told, “We typically format this differently,” respond with, “Thanks! I’ll adjust.”

👉 Stay Organized: Use a checklist for onboarding tasks—e.g., set up Git, configure APIs, and review coding standards.

👉 End on a Positive Note: At the end of the day, thank your manager and teammates—e.g., “Thanks for all the guidance today!”

So The journey of a thousand miles begins with a single step 👌👌👌

When it comes to creating software documentation, here are some technical advice to keep in mind:

👉Define the scope: Determine what the software documentation will cover and what it will not cover. This will help you create a clear and concise document that is easy to read and understand.

👉Use a consistent structure: Use a consistent structure throughout your software documentation to make it easy for readers to navigate and find the information they need. Use headings, subheadings, bullet points, and numbered lists to organize the information.

👉Keep it concise: Keep your software documentation concise and to the point. Use clear and simple language, and avoid using technical jargon unless it is necessary.

👉Include visuals: Use visuals, such as screenshots and diagrams, to help readers understand complex concepts and processes. Visuals can also break up long blocks of text and make the documentation more engaging.


👉Provide examples: Provide examples to help readers understand how to use the software and its features. Examples can help users visualize how to perform specific tasks and can also help troubleshoot issues.

👉Update regularly: Keep your software documentation up to date as the software evolves.

👉Update the documentation whenever there are changes to the software or new features are added.


👉Get feedback: Get feedback from users and stakeholders on the software documentation to ensure that it is helpful and effective. Use their feedback to make improvements and revisions as necessary.


By following these technical advice, you can create software documentation that is clear, concise, and helpful to users.

#software #help #language #documentation

Measure twice, code once, fixing mistakes is the biggest inefficiency.


Cultivate qualities below :

👉 Precision – Write clean, efficient, and bug-free code.

👉 Clarity – Keep code readable and self-explanatory.

👉 Problem-solving – Break down complex issues effectively.

👉 Adaptability – Learn and apply new technologies quickly.

👉 Efficiency – Automate, optimize, and eliminate waste.

👉 Deep focus – Minimize distractions for high-impact work.

👉 Collaboration – Work well with teams and share knowledge.

👉 Proactive mindset – Identify and fix issues before they escalate.

👉 Resilience – Debug patiently and push through challenges.

👉 Big-picture thinking – Balance immediate tasks with long-term code health.

👉 Attention to detail – Catch small mistakes before they become big problems.

IWith ChatGPT, you get answers in the context you need – no scrolling through search results required. Google doesn’t just answer questions, it shows you every angle from blogs, tutorials, and documentation.

👉 Quick Explanations
ChatGPT helps clarify concepts fast.

🔦 “What is a Python list comprehension?” → Immediate, concise answer.

👉 Detailed Documentation
Google is ideal for official docs and in-depth guides.

🔦 Searching “Java Streams API” gives Oracle’s full documentation.

👉 Real-Time Solutions
Google connects to recent community posts and answers.

🔦 Finding the latest Stack Overflow discussion on a new React.js error.

👉 Debugging Assistance
ChatGPT can help identify and fix basic code issues.

🔦 Paste an error message, get possible solutions instantly.

👉 Niche or Rare Issues
Google excels at finding solutions for unique, less common problems.

🔦 Searching “SQL Server timeout error in Kubernetes.”

👉 Learning Paths
Google offers varied tutorials and learning materials.

🔦 Searching for “Node.js beginner guide” provides tons of learning options.

👉 Tool Pairing
Combine ChatGPT’s insights with Google’s broad search.

🔦 Use ChatGPT to understand OAuth basics, then Google for OAuth implementation details.

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Command line interface is simple. It just takes a genius to understand its simplicity.

Use command line to understand more the background logic and to do more depth things :

👉Efficiency: Command-line tools are powerful and efficient.

👉Automation: Scripting automates repetitive tasks.

👉Version Control: Git and other tools are commonly used via the command line.

👉Debugging: Essential for many debugging and system administration tasks.

👉Flexibility: Offers greater control than GUIs.

👉Remote Access: Necessary for managing remote servers.

👉Resource Management; Better control over system resources and processes.

👉Learning Curve: Enhances understanding of underlying system operations and others.


Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Legacy code is like a million dollars - easy to make, hard to maintain.

Below some reasons that make programmers hesitating to touch legacy code :

👉 Fear of the unknown: Legacy codebases are often inherited from previous developers or teams, and programmers may feel intimidated by the prospect of working with code they did not write.

👉Lack of understanding: Legacy codebases are often complex and poorly documented. Programmers may find it challenging to comprehend the code's logic, structure, and dependencies.

👉 Fragile nature: Legacy code is often fragile and prone to breaking. Making changes in one part of the code can have unexpected impacts on other areas.

👉 Technical debt: Legacy code is typically associated with a significant amount of technical debt. It may have accumulated over time due to shortcuts, outdated practices, or lack of refactoring.

👉Lack of resources and support: Legacy codebases may not have sufficient documentation, tests, or tooling to assist programmers in their work.

Here are some key steps to work and deal with legacy code :

✅ Start by identifying the areas of code that need to be changed or maintained.

✅Legacy code often lacks proper test coverage, making it challenging to make changes without introducing bugs.

✅Refactoring is an essential step to improve the code's structure and maintainability. However, in a legacy codebase, it's crucial to refactor in small, incremental steps while running tests frequently to ensure you don't introduce regressions.

✅ Characterization tests are tests that capture the behavior of the existing codebase. These tests act as a safety net while making changes. Start by writing characterization tests to understand the current behavior and ensure that changes you make do not alter the existing functionality.

✅ Legacy code often has strong dependencies that make it difficult to work with. Identify areas where you can break dependencies, introduce interfaces, or encapsulate external systems to make the code more modular and testable.

✅ The Strangler Fig pattern involves gradually replacing parts of the legacy code with new, improved code.

✅ Continuously invest time and effort in refactoring, adding tests, and improving the codebase.

Legacy code is a rite of passage for every programmer. It’s where we learn humility and the value of perseverance👌

Choose your data types wisely, they lay the foundation of your software’s stability and efficiency.


Below points to consider when choosing data type :

✅Accuracy: Ensure it accurately represents the data.

✅Efficiency: Optimize storage and resources.

✅Performance:Facilitate efficient data manipulation.

✅Compatibility: Ensure it works with the software ecosystem.

✅Scalability: Accommodate future growth.

✅Security:Prioritize built-in security features.

✅Interoperability: Enable seamless integration with other systems.

✅Ease of Use: Make it easy to understand and manipulate.

✅Constraints: Enforce necessary limitations for data integrity.

Understand the nature and volume of data your software will handle then select data types that accurately represent your data while optimizing for storage and performance. Ensure compatibility with the chosen programming language and database system and Opt for data types that enforce constraints and prevent data corruption or loss 👌

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Windows runs everything, Linux runs everywhere, and macOS runs beautifully.

✅ Windows

👉 Pros:
• Compatible with most software and hardware.

• Great for .NET, C#, and game development.

• User-friendly and widely supported.

👉 Cons:
• Heavier on system resources.

• Limited package management compared to Linux.

• Frequent updates can interrupt workflows.

✅ Linux

👉 Pros:
• Highly customizable and open-source.

• Ideal for servers, DevOps, and web development.

• Lightweight, fast, and secure.

👉 Cons:
• Steeper learning curve for beginners.

• Some software (e.g., Adobe tools) isn’t natively supported.

• Hardware compatibility issues on rare occasions.

✅macOS

👉 Pros:
• Excellent for iOS/macOS development.

• Unix-based, great for terminal workflows.

• High-quality hardware and seamless integration.

👉 Cons:
• Expensive hardware.

• Less flexibility in customization.

• Limited gaming and certain software support.

So so :
• Windows: Best for generalists, game devs, and corporate setups.

• Linux: Best for servers, open-source lovers, and system admins.

• macOS: Best for mobile development, creative fields, and polished workflows.

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Before letting tester test you code do your unit tests first.

Unit testing is not about finding bugs, it’s about preventing them. Every time you write a unit test, you are protecting your future self from introducing bugs. Test early, test often, test automatically 👌

Below some advices for a better unit test experience:

👉Focus on testing small, isolated units of code.
👉Test behavior, not implementation details.
👉Use descriptive test names for clarity.
👉Keep tests simple and independent of external dependencies.
👉Cover edge cases and boundary conditions.
👉Maintain a fast test suite for quick feedback.
👉Integrate tests into your CI/CD pipeline for automation.
👉Refactor tests alongside production code.
👉Prioritize test coverage for critical code paths.
👉Treat test code with the same care as production code.
👉Use test doubles to isolate units during testing.
👉Run tests frequently to catch regressions early.
👉Continuously improve test readability and maintainability.

Learn programming for free with 🤖 Travis | 📚 150K free learning resources | 🔀 interactive roadmaps | 🤖 AI-powered tutoring. LearnEngine.com.

Code refactoring is not about perfection, it’s about continuous improvement.A small refactor today saves a big headache tomorrow.

Below how to proceed ;

👉 Thoroughly review the codebase and document key functionality before making changes.

👉 Focus on sections of the code that are hardest to maintain or prone to bugs.

👉 Ensure critical functions are covered by tests to detect regressions.

👉 Add tests that ensure components work together as expected before and after refactoring.

👉 Make small, safe changes, and test frequently.

👉 Focus on improving the code’s internal structure without altering functionality.

👉 Use meaningful names that improve readability and better explain the code.

👉 Split long functions into smaller, reusable, and testable pieces.

👉 Identify and eliminate any code that is no longer in use or needed.

👉 Refactor repeated code into reusable methods or classes.

👉 Replace inefficient data structures with better alternatives, keeping functionality intact.

👉 Improve performance where necessary without changing behavior.

👉 Organize code into smaller, independent modules that are easier to maintain and test.

👉 Make sure refactored code integrates with existing components or APIs without breaking them.

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DeepSeek shows that AI innovation is not just about power; its about doing more with less. ChatGPT shines in complexity, but DeepSeek dominates where simplicity meets performance.


⚙️When to Use ChatGPT

👉 Broad knowledge and general-purpose assistance.

👉Reliable for complex and nuanced queries.

👉Ideal for ethical or sensitive contexts.

👉Excellent multi-language support.

👉Rich developer tools and API integrations.

👉Seamless integration into existing OpenAI ecosystems.



⚙️When to Use DeepSeek



👉Cost-effective for budget-conscious projects.

👉Optimized for speed-critical tasks.

👉 Great for lightweight, task-specific applications.

👉Focused on emerging and experimental use cases.

👉Suited for localized markets like China.

👉Simplified deployment for smaller infrastructures.

👉Best for niche or industry-specific applications.



🔦🔦🔦Other AI Solutions



✅Llama 2 (Meta): Open-source, flexible, and privacy-friendly.

✅ Falcon: Cost-effective, high-performance open-source model.

✅ Mistral: Lightweight and task-optimized.

✅Bard (Google): Internet-connected with real-time data.

✅ Hugging Face: Open-source NLP powerhouse.

✅ Ernie Bot (Baidu): Tailored for Chinese language and markets.

✅ Jasper: Built for content creation and marketing.

Refactoring is a must but not before some conditions :

✅Strong Test Coverage: Before refactoring, ensure that the code has comprehensive test coverage. Having a robust suite of unit tests, integration tests, or automated acceptance tests helps validate that the code behaves as expected before and after refactoring.

✅Use Refactoring Techniques and Patterns: When refactoring, adhere to established refactoring techniques and patterns.

✅Observe and monitor : During and after refactoring, closely monitor the behavior and performance of the code


✅Clear Understanding of Requirements: Make sure you have a thorough understanding of the requirements and expected behavior of the code you're refactoring.

✅ Git and co : Utilize a version control system like Git to track changes and have the ability to roll back if needed.

✅Small and step by step Changes: It's generally safer to perform refactoring in small, incremental steps rather than attempting massive changes all at once.

✅Code Review and Collaboration: Engage in code review and collaborate with other developers or team members.

Most popular refactoring patterns :

👉Extract Method.
👉Rename Variable/Method/Class.
👉Extract Class.
👉Replace Conditional with Polymorphism.
👉Extract Interface.
👉Replace Magic Numbers with Named Constants.
👉Introduce Design Patterns.

Data science is tough to master due to its mix of technical complexity, vast toolsets, and the need for continuous adaptation to new data and methods.

👉 Complex Math: Involves statistics, calculus, and linear algebra.

👉 Data Cleaning: Raw data is often messy and unstructured.

👉 Tool Variety: Many programming languages and libraries to learn.

👉 Model Selection: Choosing the right algorithm can be challenging.

👉 Understanding Results: Interpreting data insights correctly is tough.

👉 Continuous Learning: The field evolves rapidly with new techniques and tools.

How to learn. :

👉 Start with Basics: Learn Python or R, SQL, and core statistics.

👉 Work on Projects: Apply concepts to real-world datasets.

👉 Focus on Data Cleaning: Practice handling missing data and outliers.

👉 Learn Algorithms: Understand key machine learning algorithms and their applications.

👉 Practice Regularly: Use platforms like Kaggle to build hands-on skills.

👉 Follow Experts: Read tutorials, blogs, and courses by industry leaders.

👉 Join Communities: Engage in data science forums and groups for continuous learning.

The best Data Science & Developer 🔀 roadmaps you have ever seen! With AI-powered explanations and free learning resources. https://lnkd.in/g-raAnai

In software, the most important rule is to never allow a mistake to reach the production environment.

If you don’t set rules, you invite chaos... Below how to set rules on githubactions and jenkins before merge code :


👉Code Formatting: Set up auto-formatting (e.g., Prettier, Black) to enforce style rules before commits.

👉 Linting: Use linters (e.g., ESLint for JavaScript, Flake8 for Python) to catch syntax and style issues early in GitHub Actions and Jenkins pipelines.

👉 Code Review Checks: Require peer reviews in pull requests, mandating approvals before merging.

👉 Static Analysis: Integrate tools like SonarQube to catch potential bugs, vulnerabilities, and code smells.

👉 Test Coverage Requirement: Enforce minimum test coverage with tools like Codecov; fail builds if coverage drops below the set threshold.

👉 Automated Testing: Run unit, integration, and end-to-end tests in CI pipelines, ensuring all tests pass before merging.

👉 Dependency Checks: Use tools (e.g., Dependabot) to keep dependencies up-to-date and secure.

👉 Security Scanning: Integrate security checks (e.g., Snyk, OWASP Dependency-Check) to identify vulnerabilities in dependencies.

👉 Commit Message Standards: Use tools like CommitLint to enforce consistent commit message structure.

👉 Code Complexity Metrics: Set thresholds for cyclomatic complexity and other metrics, and use tools to warn or fail builds if exceeded.

👉 Branch Protection Rules: Enable branch protection to prevent direct commits to main branches, requiring PRs with passing checks.

👉 Fail Fast & Notify: Configure builds to fail fast and notify teams immediately (e.g., Slack, email) for prompt fixes.

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Sometimes you need to step outside, get some air, and remind yourself of who you are and who you want to be.


Disconnect is necessary:

👉Reduces distractions, boosting coding concentration.

👉Encourages deeper thinking away from online stimuli.

👉Prevents mental exhaustion with regular breaks.

👉Offline time fosters skill-building through books and tutorials.

👉 Reduces anxiety, promoting work-life balance.

👉Stepping away offers fresh perspectives.

👉Breaks foster meaningful in-person interactions.

👉Avoids scrolling, creating space for productivity.

👉Offline activities restore energy and inspiration.

We become what we repeatedly do. Excellence, then, is not an act but a habit. Aristotle



Fixing a bad habit is like debugging a program it’s not fun, but its necessary.

Below some bad habits and reflexes to fix :



👉 Delaying challenging work only makes it harder later.



👉Ignoring constructive criticism stunts growth and team progress.



👉 Struggling alone wastes time when solutions are often a conversation away.



👉 Burning out by ignoring work-life balance reduces long-term productivity.



👉 Forgetting deadlines, losing track of tasks, or managing priorities poorly.



👉Focusing only on coding while ignoring communication and teamwork.



👉Constantly shifting focus lowers efficiency and creates unfinished work.



👉Shying away from leadership or accountability limits career growth.



👉Rushing for quick fixes rather than carefully solving problems.



👉 Neglecting health, sleep, or exercise affects performance and creativity.



👉Thinking you know everything can lead to missed learning opportunities.

Manually managing dependencies is a recipe for disaster… Let dependency managers handle the complexity👌

Below some points :

👉 Use Virtual Environments : Isolate each project using venv or conda.

🔦 Global installs lead to version conflicts.

👉 Pin Dependency Versions : Lock versions with requirements.txt or pyproject.toml.

🔦 Skipping versions can cause future incompatibilities.

👉 Use Dependency Managers : Tools like pipenv or poetry simplify management.

🔦 Manually managing dependencies is error-prone.

👉 Regularly Update Dependencies : Use tools like pip-review to stay current.

🔦 Outdated dependencies introduce security risks.

👉 Check for Compatibility Conflicts : Run pip check to find conflicts early.

🔦 Ignoring conflicts can crash your code in production.

👉 Remove Unused Dependencies : Audit and clean up with pip-autoremove.

🔦 Unnecessary packages bloat environments.

👉 Use .env Files for Configurations : Keep environment-specific variables separate with python-dotenv.

🔦 Hardcoding secrets is a security risk.

👉 Leverage Dependency Groups :
Separate dev/test/prod dependencies in setup.py or pyproject.toml.

🔦 Overloading all environments slows builds.

👉 Test Dependency Updates in CI/CD : Ensure updates don’t break your code via automated tests.

🔦 Unchecked updates can cause hidden bugs.

The best Data Science & Developer 🔀 roadmaps you have ever seen! With AI-powered explanations and free learning resources. https://lnkd.in/g-raAnai

Dear devs,

Answer honneslty 😉

When you meet any issue during writting code, How much time do you spend debugging before going to see on Stack Overflow or ChatGpt ?

Good models are built on great data, and great data is understood through statistics.

Data without statistics is like a compass without direction, why ?

👉 Data Understanding: Statistical methods summarize and interpret large datasets for machine learning models.

👉 Probability Theory: AI relies on probabilistic models, with probability distributions crucial for predictions
.
👉 Hypothesis Testing: Validates model assumptions to ensure reliable inferences from machine learning models.

👉 Regression Models: Linear and logistic regressions, core to machine learning, predict continuous or categorical outcomes.

👉 Optimization: Deep learning uses statistical methods like gradient descent to minimize error.

👉 Overfitting/Underfitting: Cross-validation helps avoid overfitting and ensures generalization to unseen data.

👉 Feature Selection: Statistics guides feature selection through methods like PCA, improving model performance.

👉 Model Evaluation: Metrics like accuracy, precision, and recall are used to assess model performance.

👉 Uncertainty Quantification: Statistics helps quantify prediction uncertainty using concepts like confidence intervals.


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Release and deployment are two distinct concepts in software development and deployment processes:

✅Release: A release refers to a specific version of a software application or system that is made available to users or customers. It represents a milestone in the software development lifecycle and typically includes a set of new features, bug fixes, and improvements. A release is often associated with a version number or a release label, such as “Version 1.0“ or “Release Candidate 2.“

✅Deployment: Deployment, on the other hand, refers to the process of installing or making the software application available on a target environment or platform, such as a production server or cloud infrastructure. It involves transferring the necessary files, configurations, and resources to the deployment environment, configuring the system, and ensuring that it runs correctly. Deployment may involve activities like setting up databases, configuring network settings, and installing dependencies.

When you deploy your code, it can be enabled for a group of users or clients using a feature flag…. Hiding a release behind a feature flag is a common practice in software development and deployment. A feature flag, also known as a feature toggle or feature switch, is a mechanism that allows developers to enable or disable certain features or functionalities in a software application without having to deploy a new version or make code changes.

By hiding a release behind a feature flag, the release can be deployed to the production environment but remain disabled or hidden from end users.

Here are a few benefits of hiding a release behind a feature flag:

👉Controlled Rollout: Feature flags enable a controlled rollout of new features.

👉Risk Mitigation: Hiding a release behind a feature flag allows for easy rollbacks or disabling of features in case of issues or unexpected behavior. If a problem arises, the feature flag can be turned off, effectively reverting to the previous state without requiring a full redeployment or rollback of the entire application.

👉Continuous Delivery: Feature flags align with the principles of continuous delivery by decoupling the deployment of code changes from their release.

👉A/B Testing and Experimentation: Feature flags facilitate A/B testing and experimentation by enabling different variations of a feature to be tested with a subset of users. This allows for data-driven decision-making and validation of new features before their full release.

👉Gradual Feature Adoption: By gradually enabling the feature flag for different user groups, organizations can gather user feedback, monitor performance, and ensure a smooth transition before making the feature available to all users.

There are a lot of solutions for implementing feature flag (feature toggle) like Split,LanchDarkly and Optimizely.

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Below how to build a solid, flexiblw and sustainable software :

👉Clear Requirements: Ensure a thorough understanding of project objectives to guide development.

👉Modular Design: Break down the software into reusable, manageable components for easier maintenance.

👉Design Patterns: Apply proven design patterns to solve common problems efficiently.

👉Coding Standards: Enforce consistent coding styles and practices for readability and maintainability.

👉Automated Testing: Implement tests to catch bugs early and ensure code quality.

👉Scalability: Design architecture to handle increased loads and future growth.

👉Dependency Management: Manage dependencies carefully to minimize conflicts and bloat.

👉Error Handling: Implement robust error handling to gracefully handle failures.

👉Security Measures: Integrate security practices to protect against vulnerabilities.

👉Feedback Loops: Gather feedback from users and stakeholders to guide improvements.

👉Continuous Improvement: Regularly review and refine code and processes to adapt to changing needs.

👉Performance Optimization:Identify and address performance issues to ensure responsiveness.

👉Documentation: Maintain clear documentation to aid understanding and future development.

👉CI/CD: Automate build, test, and deployment processes for rapid delivery.

👉Version Control: Use Git or similar tools to track changes and collaborate effectively.

Modern devs use AI and high-level tools for speed, but may miss deep system understanding that older devs mastered.



Comparison between old and actual devs :



👉 Control vs. Abstraction:

Old-School: Full control over performance and now : Abstracted away details with tools.



👉 Optimization:

Manual code optimization then actuallyAI and tool-based optimization.



👉 Algorithm Creation:

Built algorithms from scratch replaced by Using pre-built AI models.



👉 Debugging:

Debugged deeply for old school actual use AI tools for debugging.



👉 Core Logic vs. Features:

In the past Focused on core logic and now : Focused on feature delivery.



Actually should :



✅ Learn the Basics: Understand the core concepts behind tools.

✅ Question Tools: Don’t rely blindly on AI outputs.

✅ Optimize Manually: Sometimes, manual optimization still matters.

✅ Keep Learning: Never stop improving your understanding of programming fundamentals.



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In the world of programming, change is constant. The challenge is to channel that change into progress. An investment in knowledge pays the best gains.

Embrace a mindset of continuous learning as a programmer is the most important way to become better over the years. Below some advices :

👉Curiosity is Key: Stay eager to explore new technologies, languages, and frameworks.

👉Embrace Challenges: Overcoming obstacles in coding fosters growth and resilience.

👉Stay humble : Acknowledge that there is always more to learn.

👉Seek Feedback Actively:Constructive criticism is a powerful tool for improvement.

👉Update Your Skill Set: Regularly update your skills to remain relevant in the ever-evolving tech landscape.

👉Build a Learning Routine: Dedicate time to learning regularly, whether it's reading tech blogs, working on side projects, or taking online courses.

👉Networking and Collaboration: Networking and collaborating expose you to diverse perspectives and new insights.


👉Explore Different Domains: Exploring diverse domains enhances your versatility and problem-solving skills.

👉Set Realistic Goals : Break down larger objectives into smaller, manageable tasks to track progress effectively.

👉Embrace change and be adaptable. Technologies evolve, and a flexible mindset ensures you can navigate shifts in the industry.

👉Teach Others: Teaching is a powerful way to solidify your understanding and contribute to the community.

The more I live, the more I learn. The more I learn, the more I realize, the less I know.