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Not once or twice have I heard things like, “Why should I follow this? It works just fine for me,” or, “I know what’s best for this project, why should I follow their conventions?”
It’s easy to ignore coding standards and conventions as unnecessary, especially when everything seems to be working well for you. But here’s the thing: a codebase with consistent styles and patterns isn’t just about personal preferences - it’s about creating an environment that’s easier to work with, understand, and maintain.
So, what’s the difference between coding standards and coding conventions? In simple terms, standards are formal rules that ensure code quality & consistency across projects, while conventions are agreed-upon practices for style and structure of a language or a project.
For example, a language convention in JavaScript or TypeScript is to use camelCase for variables, so you wouldn’t use kebab-case. On the other hand, a project convention might be to use camelCase for folder names, even though folder naming doesn't have a universal standard, unlike variables or functions. If the project adopts this convention, you should follow it.
An example for coding standards in action is using simple tools like ESLint (a linting tool that analyzes your code and provides feedback as you type) and Prettier (a formatter for consistent style). While these might seem trivial to you, they enforce coding standards and help catching common errors early, and it's much easier to review and maintain code that is formatted and well-organized.
But why does this matter? consistent code isn’t just about aesthetics - it makes the code easier to read & review. Imagine working on a large team where everyone uses their own styles. Reviewing code would become a nightmare, and onboarding new developers would take longer. Tools like linters catch issues early, and formatted code saves precious time during code reviews.
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In the end, following coding standards and conventions isn’t about limiting creativity, it’s about making collaboration easier and creating software that’s simpler to maintain and grow. Think of it as a shared language - one that makes sure everyone on the team is speaking the same "dialect". That’s how great software gets built.
- Machine Learning's key trait is its capacity to adapt and learn based on new data through experience. - Features, or measurable traits, enable Machine Learning to learn and make predictions. - Supervised Learning, akin to studying with a tutor, allows the machine to learn from previous data and make predictions. - Unsupervised Learning allows the machine to infer patterns and relationships in data with no prior guidance. - In healthcare, Machine Learning uses features like symptoms and health indicators to aid diagnosis and treatments, enhancing patient care and accelerating drug discovery. - Feature Selection is the process of choosing most useful data for ML algorithms, enhancing their speed and accuracy. - Features in Machine Learning are categorized into numerical and categorical. Numerical features have values in a number sequence, whereas categorical features have label-type values.
- Azure DevOps, by Microsoft, is a tool with five main parts for project management, including code sharing, software building, and more. - It has two versions: Azure DevOps Server, which is hosted on-premises, and Azure DevOps Services, a cloud-based system. - Key features of Azure DevOps include Azure Board for planning, Azure Pipelines for automated builds and testing, Azure Repos for storing code, Advanced Security for project data safety, Test Plans for managing testing efforts, and Artifacts for package sharing. - Azure DevOps is beneficial for both teams and individuals, easily set up with an email for organizations and can manage every step of a project. - Azure DevOps Server offers more control, while Azure DevOps Services offers latest features and easy scaling. The choice depends on specific project needs. - Cost of Azure DevOps varies based on need and includes costs for setup, license pricing and additional charges for certain features. - Azure DevOps offers certifications, with levels based on experience. Free resources and online courses are widely available. - Azure DevOps integrates with tools like Jira, Terraform, Jenkins, Kubernetes, Python, and Github Actions, enhancing project management capacities.
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Backend developers handle servers, data, and logic using tools like Python and JavaScript. Future trends include AI, cloud, microservices, and DevOps.
