Coder Legion - Recent posts tagged tips

Create Scroll-stopping Cybersecurity professional LinkedIn headline

Mon, 15 Jun 2026 14:40:58 +0000

canonical_url: https://proresumes.io/cybersecurity-professional-linkedin-headline/

Are you done with your bachelor’s or master’s degree in cybersecurity? It’s time to step into the professional world as a cybersecurity newbie! Trying to craft the perfect resume to submit for the job application or trying to optimize that forgotten LinkedIn profile?

It’s time to polish your LinkedIn profile and especially that default LinkedIn headline that’s nowhere near good! In this guide, we will focus on the importance of LinkedIn for IT professionals. We will talk about the need and tips to craft a cybersecurity professional LinkedIn headline.

Moreover, we will share an exclusive template plus cybersecurity professional LinkedIn headline examples to help you write your own. So, ready for an information-filled ride to make your job-hunting process a little more exciting? Buckle up and explore.

Why do cybersecurity professionals need LinkedIn?
No matter if you’re a newbie or an expert in the Cybersecurity profession, being on LinkedIn is always useful. According to the LinkedIn statistics, around 122 million people get the opportunity to be interviewed through their LinkedIn profiles.
Since it’s a professional networking platform, cybersecurity professionals can connect with like-minded people, reach recruiters and organizations.

An optimized LinkedIn profile speeds up your job search process and provides unlimited career advancement opportunities. Moreover, it also helps you attract career opportunities when you aren’t even actively applying.

There are lots of perks for having an optimized LinkedIn profile, such as building connections, getting job offers, and more.

However, LinkedIn Profile Optimization isn’t an easy task when you have to manage studies or work. No worries, certified LinkedIn experts at ProResumescan optimize your profile to help you achieve your goals.

Why is it important to have an effective LinkedIn headline for IT professionals?
We’ve discussed why having a LinkedIn profile is significant for cybersecurity professionals. Let’s explore the purpose served by a cybersecurity professional LinkedIn headline and why you should’ve one:

Preview of your professional account
It’s a mini preview of your LinkedIn account, particularly of who you are as a person and professional.
We don’t think much about the headline of our LinkedIn profiles and end up missing the chances of getting noticed. Whenever your profile shows up in a recruiter’s search list, your headline differentiates it.

If it fails to grab the attention of a cybersecurity recruiter, then your profile is surely getting skipped.

Pro Tip: A cybersecurity recruiter should instantly get the idea of your expertise and skills as soon as they read your headline.

Show your professional expertise
It should be able to highlight your profession, specialization, and years of experience.
The purpose of a good cybersecurity professional LinkedIn headline is to show what they can offer to an employer. It is important to add your job title to signal recruiters for the kind of work position you’re available for.

Pro Tip: Leaving your LinkedIn headline with just the job title at your current workplace is ineffective!

Enhances Visibility to potential employers
The better your cybersecurity professional LinkedIn headline is, the higher the chances of showing up in recruiters’ searches.

Your LinkedIn headline is actually an SEO tool that helps you rank in the hiring manager’s search list. Embedding the cybersecurity professional LinkedIn headline with industry-specific and technical words will train the LinkedIn algorithm.

Validate your Skills
Your headline on LinkedIn is limited to 220 characters. Crafting it intelligently will help you with skill validation as well.

You can’t write multiple sentences in your LinkedIn headline. Just strategic technical terms and keywords placed appropriately do wonders.

How to create a cybersecurity professional LinkedIn headline?
Here’s a list of some easy-to-follow tips to help you create the most effective headline as a cybersecurity professional:

Show your position in the field of cybersecurity
Simply mention your current or aspiring designation in the cybersecurity field.
Here’s a list of some cybersecurity job positions in any organization:

Cybersecurity technical architect
Penetration tester
Security administrator
Vulnerability analyst
Information security analyst
Network security engineer
Digital forensic examiner
IT auditor
Cryptography engineer
Show your technical expertise

Adding hard skills in the cybersecurity professional LinkedIn headline helps with SEO and attracts attention.

Here’s a list of technical skills to add to your headline:

Threat Intelligence
Risk Management
Data Analysis
Machine Learning
Computer Networks
Operating Systems
Automation
Cloud Engineering
Malware Analysis
Intelligence Analysis
Endpoint security
Vulnerability management
Cryptography
Incident Response
Cybersecurity Application development

Show the industry or domain you help
It helps a recruiter understand the direction of your professional expertise.
Many cybersecurity professionals work in the following domains:

Healthcare
Finance
Law and Government sectors
Technology
Telecommunications
Business

Show your non-technical expertise
Be creative and add a slight touch of your personality that can help a recruiter evaluate your soft skills. It also helps you stand out among the rest of the cybersecurity professionals.

Add relevant cybersecurity certifications
If you don’t have enough professional expertise, share one or two certifications.
Here’s a list of some certifications added by IT professionals on their profiles:

Certified Network Defender (CND)
Certified Information Systems Auditor (CISA)
Cloud Security Certification (CCSP)
Certified Ethical Hacker (CEH)
Certified Information Security Manager (CISM)

Mention future aspirations in the field
Adding your future aspirations gives the employer an idea of your future-oriented approach and continuous learning mindset. If you want to pursue a future cybersecurity role, mention it.

Use concise and clear language
You get a maximum of 20 words to attract a cybersecurity recruiter and make an impression. It is important to incorporate keywords, verbs, and concise language to mention all important details.

Perks of having an effective LinkedIn headline for a Cybersecurity professional
Here’s the list of perks that you can enjoy if you have an effective LinkedIn headline as a cybersecurity professional:

•The cybersecurity professional LinkedIn headline shows employers their level of understanding related to their field.
•It helps a cybersecurity professional demonstrate professionalism and strategic thinking.
•It shows the worth of an IT professional by highlighting key skills, certifications, and achievements.
•It works as a value differentiator and increases the chances of being remembered by a recruiter.
•It also portrays your personality and nudges a recruiter to know more about you.
Cybersecurity professional LinkedIn headline (Template & Examples)
Template LinkedIn headline for a cybersecurity professional
We’ve discussed the importance and best tips to craft a cybersecurity professional LinkedIn headline. However, it’s not easy to write one as a beginner, and we understand it.
Here’s an easy-to-implement template structure to make an effective headline for your LinkedIn profile:
•Add current or aspiring designation in cybersecurity.
•Mention years of experience or company name
•Add up to 3 skills
•Explain what you do with an action verb and a keyword.
[ (position) + (expertise or company) + (skill) + (skill) + (skill) + (action verb + industry-specific keyword for responsibility)]

10 Examples of a Cybersecurity professional LinkedIn headline
Here’s a list of strategic and intelligently crafted LinkedIn headlines examples by various cybersecurity professionals:

Craft a linked headline that attracts Cybersecurity Recruiters like a magnet
The best way to craft the cybersecurity professional LinkedIn headline is to:
•Be strategic.
•Add industry-specific keywords for SEO.
•Incorporate key cybersecurity skills, certifications, and work experience.
•Keep your headline precise and clear.
Frequently Asked Questions

Q. How is a hacker different from a security professional?
Hackers are the bad actors who purposely steal and exploit sensitive user information for reasons like demanding ransom or more. They are also known as cybercriminals who carry out specialized attacks in cyberspace.
Security professionals are responsible for protecting networks, systems, and data from being exploited by cybercriminals. These individuals work strategically with a proactive approach to counter threats.

Q. What are the different specialized sub-niches within the cybersecurity niche?
The field of cybersecurity is complex and vast. It is divided further into seven specialized sub-types, such as the following:

Cloud security
Internet of Things (IoT) security
Endpoint security
Zero Trust
Mobile security
Application security
Network security

Q. What is the difference between cybersecurity professionals and information security professionals?
Cybersecurity professionals are particularly specialized in handling security concerns of cyberspace. They work with organizations to ensure the security of their digital assets.
Meanwhile, information security professionals are responsible for protecting all business assets, including physical and digital. These professionals safeguard all sorts of assets from unauthorized access and exploitation.

Preserving Scroll Position and Cursor State During Navigation Transitions

Sun, 25 Jan 2026 16:57:20 +0000

Have you ever noticed the difference between a simply good app and one that feels "native," polished, and premium? Often it's not about design or animations, but about small details we take for granted until they break. One such critical detail is an app's ability to remember where you left off. You're reading a long article, minimize the app to respond to a message, come back a minute later, and boom - you're thrown back to the very beginning of the text. Annoying, right? Or you're writing a note, switch to the browser, come back, and the keyboard is hidden with the cursor lost. In this article, we'll break down how to technically implement scroll position and cursor state preservation in iOS apps properly. We'll go beyond banal advice and examine real scenarios with navigation, tabs, and asynchronous data loading, so your users never feel lost.

Why Losing Context Hurts

Let's be honest: users don't know terms like "controller lifecycle" or "memory unloading." But they can absolutely feel when an app behaves stupidly. Losing scroll position or cursor during navigation is exactly that kind of stupid behavior. It destroys the interaction flow.

Imagine you're working in a code editor on an iPad. You scroll a file to line 500, place the cursor in the middle of a complex function, then open the side menu to check the project structure. You close the menu - and bam! - you're back at line one. At this moment, the app transforms from a professional tool into a toy.

Another classic example is news feeds or product catalogs. The user has scrolled down through a hundred items, drilled into a product detail screen, tapped Back, and their list reloaded, throwing them back to the top. This isn't just inconvenient, it's wasting the user's time. In a world with cutthroat competition for attention, such UX mistakes are unforgivable. An app that doesn't remember my state feels like a cheap web wrapper, even if it's written in pure Swift.

The Foundation: State Restoration in iOS

Apple has long provided us with a powerful mechanism to solve these problems - UI State Restoration. But in my observations, many developers ignore it, preferring to reinvent the wheel by saving data in UserDefaults or singletons. I was guilty of this myself for a long time, until I understood how the system works under the hood.

The built-in mechanism is good because it's systemic. It triggers not only when you push a controller onto the navigation stack, but also when the system kills your app in the background due to memory pressure, and then the user relaunches it.

The cornerstone of the entire mechanism is the restorationIdentifier property.

// In viewDidLoad
self.restorationIdentifier = "MyDetailViewController"

If a controller (or any UIView) doesn't have this identifier set, the system won't even attempt to save its state. This is your entry ticket. Once the identifier is set, two methods from the UIStateRestoring protocol come into play, which every UIViewController already has:

override func encodeRestorableState(with coder: NSCoder) {
    super.encodeRestorableState(with: coder)
    // Here we encode what we want to save
    coder.encode(someImportantData, forKey: "dataKey")
}

override func decodeRestorableState(with coder: NSCoder) {
    super.decodeRestorableState(with: coder)
    // And here we retrieve it back
    if let data = coder.decodeObject(forKey: "dataKey") as? DataToRestore {
        self.someImportantData = data
    }
}

Sounds simple, but the devil is in the implementation details. State Restoration doesn't save your model data (that's what Core Data or files are for). It saves interface state, which helps display that data exactly as it was before the user left.

Saving Scroll Position (ScrollView Offset)

The most common case is UITableView or UICollectionView. When the user leaves the screen (for example, pushes the next controller), we want to remember the contentOffset.

Many try to save the offset in viewWillDisappear. This is a working strategy for simple transitions within the navigation stack, when the controller stays in memory. But if we're talking about full-fledged State Restoration (with app termination), it's better to use specialized methods.

Let's say we have a UITableViewController. Fortunately, UITableView and UICollectionView already know how to save a lot themselves, if they (and their cells!) have restorationIdentifier set. But sometimes we need more control, especially if the scroll view is complex.

I prefer an explicit approach. Let's create a structure to store our screen's state:

struct ListViewState: Codable {
    let contentOffsetY: CGFloat
    // Can add selected cell ID, etc.
}

Now in the controller:

class MyListViewController: UIViewController, UITableViewDelegate {
    
    @IBOutlet weak var tableView: UITableView!
    private let stateKey = "ListViewControllerState"

    override func viewDidLoad() {
        super.viewDidLoad()
        // Critical for the mechanism to work
        self.restorationIdentifier = "MyListVC"
    }

    // MARK: - State Restoration

    override func encodeRestorableState(with coder: NSCoder) {
        super.encodeRestorableState(with: coder)
        
        // Save current offset
        let state = ListViewState(contentOffsetY: tableView.contentOffset.y)
        do {
            let data = try JSONEncoder().encode(state)
            coder.encode(data, forKey: stateKey)
        } catch {
            print("Failed to encode state: \(error)")
        }
    }

    override func decodeRestorableState(with coder: NSCoder) {
        super.decodeRestorableState(with: coder)
        
        guard let data = coder.decodeObject(forKey: stateKey) as? Data,
              let state = try? JSONDecoder().decode(ListViewState.self, from: data) else {
            return
        }
        
        // HERE'S THE MAIN GOTCHA
        // You can't just set contentOffset right now.
        // The table data might not be loaded yet.
        self.restoreScrollPosition(to: state.contentOffsetY)
    }
    
    private var pendingScrollOffset: CGFloat?

    private func restoreScrollPosition(to offset: CGFloat) {
        // Save the offset "for later"
        self.pendingScrollOffset = offset
    }
    
    // Call this method when data is definitely loaded and table has redrawn
    func dataDidFinishLoading() {
        tableView.reloadData()
        
        if let offset = pendingScrollOffset {
            // Sometimes need to give the Layout engine time to work
            DispatchQueue.main.async {
                self.tableView.setContentOffset(CGPoint(x: 0, y: offset), animated: false)
                self.pendingScrollOffset = nil
            }
        }
    }
}

Notice the pendingScrollOffset. This is a key moment when working with async data. If you try to restore contentOffset in decodeRestorableState when your table has 0 rows, the scroll will simply reset to 0. You must apply the saved offset only after the data is received and reloadData() has executed.

Hunting for the Cursor in UITextView

With text editors, things get even more interesting. Here we need to save not only scroll, but also cursor position (or text selection). This is handled by the selectedRange property of UITextView.

The problem is that cursor position is inextricably linked to keyboard focus. If during state restoration we simply set selectedRange but don't make the text field active (becomeFirstResponder), the user will see the text, possibly even scrolled to the right place, but without the cursor and keyboard. They'll have to tap again. This isn't the "seamless" experience we're aiming for.

Here's how I typically approach solving this in an editor controller:

class EditorViewController: UIViewController {
    
    @IBOutlet weak var textView: UITextView!
    private let selectionKey = "EditorSelectionRange"
    private let textKey = "EditorTextContent" // If text isn't saved in model

    override func viewDidLoad() {
        super.viewDidLoad()
        self.restorationIdentifier = "EditorVC"
        // Important: the textView itself also needs an ID if we want
        // the system to help us with scroll
        self.textView.restorationIdentifier = "EditorTextView"
    }
    
    override func viewDidAppear(_ animated: Bool) {
        super.viewDidAppear(animated)
        // If we restored state and should show keyboard
        if shouldRestoreFocus {
            textView.becomeFirstResponder()
            shouldRestoreFocus = false
        }
    }

    // MARK: - State Restoration

    override func encodeRestorableState(with coder: NSCoder) {
        super.encodeRestorableState(with: coder)
        
        // Save range. NSRange encodes perfectly.
        let range = textView.selectedRange
        if range.location != NSNotFound {
             coder.encode(NSValue(range: range), forKey: selectionKey)
        }
        
        // Save whether this field had focus
        coder.encode(textView.isFirstResponder, forKey: "wasFirstResponder")
    }

    private var shouldRestoreFocus = false

    override func decodeRestorableState(with coder: NSCoder) {
        super.decodeRestorableState(with: coder)
        
        // Restore text (if needed)
        // textView.text = ...

        // Restore range
        if let value = coder.decodeObject(forKey: selectionKey) as? NSValue {
            let range = value.rangeValue
            // Critical moment: need to ensure range is valid for current text
            if range.upperBound <= textView.text.count {
                textView.selectedRange = range
                
                // Often need to force scroll to cursor,
                // as automatic scroll might not work during restoration
                textView.scrollRangeToVisible(range)
            }
        }
        
        // Remember if we need to restore focus
        shouldRestoreFocus = coder.decodeBool(forKey: "wasFirstResponder")
    }
}

I deliberately moved becomeFirstResponder() to viewDidAppear. Calling it in decodeRestorableState or viewDidLoad is often premature, since the view hierarchy isn't fully ready yet, and the system might ignore the keyboard request. Plus, keyboard appearance changes the UITextView insets, which can affect scroll. By doing this in viewDidAppear, we let the interface "settle."

Tabs, Modals, and App Backgrounding: A Unified Strategy

Developers often implement different mechanisms for different navigation types. For UINavigationController they use viewWillDisappear to save offset. For tab bar - UITabBarControllerDelegate methods. For app backgrounding - they subscribe to UIApplication.didEnterBackgroundNotification notifications.

This is a path to code duplication and bugs. I'm a proponent of a unified strategy.

The State Restoration mechanism described above (encodeRestorableState/decodeRestorableState) works perfectly for the "kill and resurrect" app scenario.

But what about simple navigation within a live app? Switching tabs or push/pop controllers. Here State Restoration in its pure form doesn't quite fit, since it's about "cold restart."

For "hot" navigation I prefer using lifecycle methods viewWillDisappear and viewWillAppear as the most reliable triggers.

Leaving the screen (viewWillDisappear): This is the perfect place to save current "ephemeral" state (scroll, focus) to a local controller variable or associated ViewModel. Doesn't matter why the screen is disappearing - new one being pushed, tab switching, or modal presenting. This method will be called.
Returning to screen (viewWillAppear / viewDidAppear): Here we check if there's saved state and apply it.

Example for "hot" saving in a list:

class HotListViewController: UIViewController {
    @IBOutlet weak var tableView: UITableView!
    
    // Store offset right in controller for quick transitions
    private var lastScrollOffset: CGPoint?

    override func viewWillDisappear(_ animated: Bool) {
        super.viewWillDisappear(animated)
        // Leaving? Remember where we were.
        lastScrollOffset = tableView.contentOffset
    }
    
    override func viewWillAppear(_ animated: Bool) {
        super.viewWillAppear(animated)
        
        // Returned? If there's something to restore, and data hasn't changed critically.
        if let offset = lastScrollOffset {
            // Important: check table isn't empty, otherwise crash or glitch
            if tableView.contentSize.height > offset.y {
                 tableView.setContentOffset(offset, animated: false)
            }
        }
    }
}

This approach works great when switching tabs in UITabBarController. Controllers in tabs usually aren't unloaded from memory, so the local lastScrollOffset variable will hold the value indefinitely, as long as the app session is alive.

If you're using custom navigation where controllers are recreated, you'll need to save this "hot" state somewhere external - for example, in a navigation service or global state storage tied to screen ID.

Testing: Breaking What We Built

The biggest mistake when implementing these features is improper testing. Simply minimizing the app with the Home button and reopening it is not a State Restoration test. In this case, the app simply "sleeps" in RAM, and when awakened, all views and controllers remain in place untouched. No decodeRestorableState is called.

To test for real, you need to simulate system actions:

Simulating memory pressure: In iOS Simulator, select Debug -> Simulate Memory Warning from the menu. If your controller is deep in the navigation stack or in an inactive tab, and it's "heavy," the system might unload it (call didReceiveMemoryWarning, and then possibly free its view). When returning to it, the restoration mechanism should trigger.
App termination in background: This is the main test.
- Launch the app from Xcode.
- Minimize it (go to Home Screen).
- In Xcode, press the "Stop" button (square). This kills the process, but as if the system did it in the background (saving state), not like a crash.
- Launch the app again from the icon on simulator/device (not the Run button in Xcode, otherwise the saved state will be wiped).
- If you did everything right, the app should launch not from scratch, but restore the controller hierarchy and their state, including scroll and cursors.

Common Pitfalls: Asynchronicity Is My Enemy

I've touched on this before, but I'll repeat it, as this is the most frequent cause of bugs.

The Problem: You saved contentOffset.y = 2000. When the app launches, decodeRestorableState fires, and you dutifully try to do tableView.setContentOffset(CGPoint(0, 2000)). But at this moment, your ViewModel has just started an asynchronous network request for data. The table is empty. Its contentSize is zero. Setting the offset is either ignored or gets reset to zero at the first data reload.

The Solution: Never restore scroll position (or text cursor) until the content is ready.

In decodeRestorableState, only decode the parameters and save them to temporary properties of the controller.
Wait for the data loading to complete.
Call reloadData().
Only after this (sometimes it's useful to wrap in DispatchQueue.main.async to let the layout cycle pass) apply the saved parameters.

If you're using reactive frameworks like RxSwift or Combine, this is convenient to do in a subscription to data updates, right after new data has been passed to the diffable data source or table reload has been called.

Careful attention to state preservation is what distinguishes a mature product from an MVP. It's care for the user that they may not consciously notice, but will definitely feel its absence if you decide to cut corners on this.

Why Building Side Projects is the Fastest Way to Learn Tech

Mon, 14 Jul 2025 12:15:08 +0000

There’s no shortcut in tech, but if there’s one proven accelerator, it’s this: build side projects.

Not academic ones. Not guided tutorials. Real, personal, slightly chaotic side projects. They stretch you, expose your blind spots, and force you to think like a problem solver not just a coder.

Below are the core reasons side projects are your fastest and smartest way to grow.

1. You Move From Passive Learning to Active Learning

Watching tutorials feels productive, but it's still passive. You're absorbing someone else’s decisions. You're not solving real-world issues, you're just mimicking workflows.

But when you build your own project, you actively decide what features to implement, which tech to use, how to structure your app, and how to solve bugs. You begin asking questions like:
“How should I store this data?”
“What happens when the user logs out halfway?”
“What’s the simplest way to deploy this?”

These questions are never answered in tutorials you only ask them when you're building for yourself. That’s how you go from theory to instinct.

2. You Learn Faster Because You're Emotionally Invested

When you care about the outcome, you naturally put in more effort. If it’s your idea, your vision, your baby — you’ll debug harder, research deeper, and push longer.

This emotional connection creates momentum. You’re not building for a grade or certificate. You’re building because you want it to exist. And that desire sharpens your focus, boosts your curiosity, and makes the entire learning experience stickier.

3. You Get Exposed to More Tech in Less Time

When building from scratch, you realize how many moving parts go into real applications. You want authentication? You learn JWT or sessions. You want your users to upload images? You learn file storage and maybe CDN. You want to deploy it? You touch Docker, hosting platforms, or CI/CD.

No course can simulate this kind of exposure. But in a single side project, you might work with:

A frontend framework like React or Vue
A backend like Django, Nexios, or FastAPI
A database like PostgreSQL or MongoDB
API tools like Postman or Insomnia
Deployment tools like Netlify, Vercel, or Docker
Git, GitHub, CI pipelines
And maybe even Redis, queues, or caching

Each tool teaches you something new. And they stick because you’re using them in your own context not a controlled tutorial.

4. You Learn Real Debugging and Error Solving

In a tutorial, the instructor's code just works. In a side project, your code never works on the first try.

That’s a blessing.

You’ll get weird errors, deployment bugs, version mismatches, null pointer exceptions, CORS issues, data inconsistency, auth failures the full package. And every one of them will teach you something deep. You’ll go through logs, scan Stack Overflow, rewrite entire functions, and finally fix the bug.

That process builds real confidence. Not confidence from watching, but from solving. And trust me a developer who can debug well is more valuable than one who just writes perfect code.

5. You Learn to Make Trade-Offs and Design Decisions

Side projects force you to decide between options.
Should you store images in the database or on a CDN?
Should you use class components or functional hooks?
Should you normalize your tables or denormalize for speed?

These decisions make you think like an architect. You start to consider performance, scalability, ease of use, and simplicity. You learn there's never one “perfect” choice — only what's best for your current context.

Making these choices repeatedly builds your product sense a key skill most juniors never develop until they're on the job.

6. You Get Comfortable With “Incomplete” Knowledge

Many devs wait to build until they “know enough.” That’s a trap. In tech, you’ll never feel like you know everything.

But with side projects, you’re forced to move forward despite not knowing it all. You hit a problem, research it, learn it just in time, and apply it. This builds learning agility, which is more valuable than memorizing frameworks.

You begin trusting yourself. Even if you don’t know the answer now, you believe you can find it and apply it when needed. That’s real developer confidence.

7. You Build a Portfolio While Learning

Every side project becomes a line on your resume, a GitHub repo, a tweet, a product demo. It shows potential employers or collaborators that you don’t just study you build.

It doesn’t have to be perfect. Even an ugly MVP shows:

You can think through features
You can integrate tools
You can get things to production
You know how to ship and iterate

That alone puts you ahead of 90% of tutorial-only devs.

8. You Understand Tech in Context, Not Isolation

Reading the Django docs will teach you what ModelSerializer does. But building your own API, connecting it to your frontend, protecting routes with JWT, and deploying to production? That teaches you how Django works in the real world.

Side projects force you to connect technologies across the stack. You stop learning tools in isolation and start understanding the flow: from request to response, from database to UI, from bug to fix.

That holistic understanding is what makes you a strong dev.

9. You Can Accidentally Build a Business or Open Source Tool

Some of the most useful startups and OSS projects started as side projects. You might build a tiny app for personal use, post it online, and discover that 500 other devs want it too.

Suddenly you’re maintaining a tool, gaining stars, getting feedback, learning how to scale. That’s how you accidentally stumble into real-world impact. And it’s only possible because you started building early.

10. You Learn What You Actually Enjoy Doing

Only by building can you discover what lights you up. Maybe you love APIs but hate CSS. Maybe real-time WebSocket apps excite you more than static sites. Maybe you enjoy solving deep backend problems, or maybe you prefer crafting beautiful UIs.

Side projects help you uncover your developer identity your strengths, preferences, and creative direction.

Knowing this shapes your career path, your tech stack choices, and even the kind of jobs you apply for.

Conclusion: Stop Waiting. Start Building.

Courses are useful. Docs are important. Bootcamps can help.
But if you're serious about getting better fast — about becoming a confident, versatile, and creative developer — then you need to build side projects.

Even if they suck. Even if they crash. Even if you never finish them.

They teach what no course can: real-world problem solving, full-stack awareness, creative thinking, and confidence from repetition.

You don’t become a developer by consuming more.
You become one by creating.

So stop waiting for permission.
Open your code editor.
Start building that idea in your head.
And watch your skill level skyrocket.

Python Best Practices and Tips - Python Performance Optimization

Sun, 23 Mar 2025 10:49:10 +0000

Python has seen a huge increase in popularity in recent years. This is because of its simple syntax, a vast range of third-party libraries, and a great community. Python is a powerful language for trending technologies like Artificial intelligence, machine learning, and data science. It has many powerful features, but with that, some other factors need to be considered as a Python developer to bring out the maximum power in our applications.

Common Performance Bottleneck in Python

There are certain factors that cause performance bottlenecks while working on Python code. One of the factors is its dynamic nature, whereas the other is the use of the GIL (Global Interpreter Lock) mechanism. As we know, in Python code, type checking is performed at run time rather than at compile time, which can cause execution slowdown. Conversely, CPython is an interpreter in which the GIL mechanism is used to ensure the execution of a single thread at any time; however, it has inconsistencies while working on a multithreaded environment by limiting the performance of CPU-bound programs.
Therefore, in this article, I will cover the detailed strategies that encourage you to achieve strong performance in your code, optimization practices, and the selection of correct data and built-in libraries.

Profiling Python Code

Finding the cause and location of the problem is crucial before trying to implement a solution. If your Python program consists of many functions and is running too slowly and using too much RAM, then you will want to fix those parts of your code. You may directly try to fix it, but sometimes you often end up fixing the wrong part. Therefore, it is better to at least define the direction of the issue before making changes. Here, profiling comes into place to optimize our code.

1. Introduction to Profiling Tools

Software profiling is the concept of finding the bottlenecks or loopholes in the running program so that we can do the least amount of work to obtain the optimal performance gain. This software will help your code run fast enough based on your needs and requirements. It consists of the following profiling tools:

The cProfile module is a built-in profiling tool of the Python standard library. It hooks into the machine to measure the time taken to run each program function. Furthermore, it provides CPU information, along with additional details such as how long the program is executed and how many times each function is called within the program.
line_profiler is another tool for identifying the cause of CPU-bound problems in Python code. It works by profiling individual functions on a line-by-line basis. Therefore, the goal is to determine the slow parts that may need improvement.

Note: line_profile is the annotating version of the output as you modify your code, which means it has a record of changes that you can quickly check for loopholes.

2. Identifying Performance Bottlenecks using Profiling

Now, let's identify the bottlenecks using coding examples. Here, I define the same function with two different approaches to determine which is more efficient.

import cProfile
def addition(num1, num2):  #Naive approach for addition function
    result = 0
    result += num1
    result += num2
    return result

def addition_Opt(num1 ,num2): #optimized approach for addition function
    return num1 + num2 

if __name__ == "__main__":
    cProfile.run(addition(8, 0)) 
    cProfile.run(addition_Opt(9,9))

In the above code, two different approaches are used for the addition function. The first approach is the traditional method of adding two numbers, which takes a considerable amount of time to calculate the result of each variable. It starts from the result with a value of 0 by adding num1 first and then num2 and then returning the result. The second approach is more optimized and takes two numbers and returns the sum of these values. I am using the cProfile here to track bottlenecks and measure which functions take significantly more time than others.

3. Analyzing Profiling Results to Prioritize Optimization Efforts

Now, I will analyze the results obtained after performing addition using two different approaches. The output is as follows:

Output

The result shows that the compiler calls the two functions, and the first function takes 0.001 sec more than the other function. This is not a good example for demonstrating cProfile; therefore, we need to work on a real-time project that contains a large dataset.

Choosing Efficient Data Structures and Algorithms

One of the most important aspects of writing efficient programs is understanding the correct data structure you use. A large part of program performance depends on the data you have and on selecting the proper data structure for solving the problem.

1. Understanding the Performance Characteristics of Built-in Data Structures

List and dictionary are the classes of the array data structure. An array is simply a flat list of data with some intrinsic ordering. You can implement arrays in multiple ways, representing lists, dictionaries, and tuples differently. Therefore, the list and dictionary are dynamic arrays, whereas tuples are static arrays. Lists are mostly suitable for performing sequential access and indexing but are less efficient for insertion and deletion operations because of their arbitrary position. However, the dictionary is implemented using a hash table that is ideal for key-pair loops with constant time complexity. In addition, you can efficiently perform insertion and deletion operations because of the dynamic resizing property.

2. Choosing the Appropriate Data Structure for Specific Tasks

Choosing the correct data structure for a specific task can significantly impact the performance optimization of a Python program. Here, I discuss the use case of the list, dictionary, and set:

List: When you need to store the collection of elements that may change in size and where the order of the elements matters. Then, the list is the appropriate data structure.
Dictionary: When you need to map the unique keys to the corresponding values for fast lookup, then a dictionary is preferable because it implements using a hash table that provides the fastest lookups with an average time complexity of o(1).
Set: When you need to store the collection of unique values and perform the set operations such as union, intersection, and difference. Then, the set data structure is appropriate. It is also implemented using hash table like a dictionary but with unique values and no mapping.

Tip: Avoid unnecessary loops and use a single loop instead of a nested loop. Choose the right data structures such as dictionaries, sets, and lists to optimize memory usage and access times.

3. Optimizing Algorithms for Time and Space Complexity

Let's understand the optimization algorithm for time and space complexity using a coding example of a list. Suppose we have a list of elements and want to retrieve an element similar to our target value. The problem is that the elements are not in the correct order for linear search and the target is not in the list. Now, We move to the coding part:

import time 
my_list = [9,18,28,88,29,61,56,42,19,95, 100, 46, 27, 28, 10, 38, 58, 38, 58 ,79, 27 ,69, 17, 48, 69, 26, 3,85, 64] 
def linear_Search(elem, array): #take element and array 
    for i, item in enumerate(array): #pick the value at index i 
        if (item == elem): #matchs the elem with the value 
            return i #return if found 
    return -1 #return if not found 

linearSearch_Time = time.time() #to measure the time taken for search 
result = linear_Search(2,my_list) #pass the target value and list
linearSearch_Time = time.time() - linearSearch_Time  #minus the staring time with the current time taken by linearsearch function 
print(f"Time Taken by linear Search {linearSearch_Time:.4f} seconds") 
def binary_Search(array, elem, low = 0, high = None): 
    if high is None:  
        high  = len(array) - 1 
    while low <= high: 
        mid = (low + high) // 2  #measure the mid point  
        if mid >= 0 and mid < len(array) and array[mid] == elem: #check for target within the array #here check the number at mid-left 
            return mid 
        elif elem < array[mid]: #check value at mid-right 
            return binary_Search(array, elem, low, mid-1) 
        else: 
            return binary_Search(array, elem, mid+1, high) 
        return -1 

#sorted list 
my_list2 = [9,18,28,38,49,51,66,72,89,95] 
binarySearch_Time = time.time() 
result2 = binary_Search(my_list2, 42) 
binarySearch_Time = time.time() - binarySearch_Time #minus the starting time with the function start time 
print(f"Time Taken by binary Search {binarySearch_Time:.4f} seconds")

In the above code, the time complexity for the linear_Search function is o(n), which is best for small datasets. However, if the list is an unsorted large dataset, linear search is not an efficient approach because as the size increases the complexity also increases. Therefore, binary search is a better option with a time complexity of o(log(n)) that does not affect performance even if the list size increases.

Note: Select algorithms with better time complexity to significantly improve performance.

Utilizing Built-in Functions and Libraries

1. Leveraging Built-in Functions for Performance-Critical Operations

Python is a versatile programming language with many built-in functions and libraries. Utilization of these built-in functions will optimize the Python code. Whenever possible, opt for using these libraries instead of manually writing the code from scratch. Doing so will not only save you time but also optimize the performance of your program.

2. Exploring Optimized Libraries for Numerical Computations

Python has introduced many optimized libraries for performance-critical operations. These libraries are specifically designed to provide numerical computations and scientific calculation tasks. Let's explore them with the following well-structured table:

Adhering to these built-in libraries and their function will help you to write efficient and performant code to cover a wide range of tasks in Python including data analysis, math calculations, and some numerical computations.

3. Using Standard Libraries for I/O Operations

Just like numerical computation libraries, some other libraries are defined for I/O operations. They are the key libraries in Python where each library serves a specific purpose related to file handling, management, directory operations, and some system-specific interactions. Leveraging these libraries can help you manage the input-output operation in your program efficiently. These are:

Memory Management and Optimization Techniques

1. Understanding Python's Memory Management Model

Memory management is a prominent topic in all programming languages for writing efficient programs. We should manage memory efficiently. Therefore, understanding the deep knowledge of the memory management model of Python is necessary:

Memory Allocation and Deallocation

Python manages memory dynamically using the private heap space during runtime instead of the traditional method. It consists of two parts: memory allocation and deallocation. Memory allocation occurs during program execution, in which a certain amount of memory is assigned to an object by the memory allocator. After execution, the built-in garbage collector deallocates the memory by scanning the heap to reclaim the memory from objects that are not used.

Garbage Collection

Python removes objects that have no reference. When the object is left idle and not in use, the garbage collector removes the object to free up space. Garbage collection also deals with the reference cycle, in which objects are referenced to each other in a circular motion. In addition, it scans the memory for objects that are not in use and removes them to free the space associated with them.

Reference Counting

One of the garbage collection algorithms is reference counting. It is a primary memory management strategy in which each object has a reference count. When the reference count of an object drops to zero, the garbage collector removes that object to deallocate the memory associated with that object. This is how optimal memory management is achieved in Python.

2. Managing Memory Leaks and Circular References

To prevent excessive memory consumption and ensure optimal performance managing memory leaks and circular reference is crucial.

Utilization of the profiling concept will help identify the memory leakage problems in your code. As discussed above, profiling will give you the loopholes in your code with detailed information on memory usage by each function or object. It will help you track memory over time and identify objects that are sitting idle.
After resource utilization, it is vital to release the resources to avoid the halt conditions. Most specifically, while working with resources like files, databases, or APIs. The incorporation of a context manager will explicitly ensure that the resources are released.
While using circular references between the object references, be attentive because sometimes they will prevent the object from being considered unused even if they are not in use which will waste memory. Use weak references so that it is easy for garbage collectors to break the references when necessary.

Warning: Be cautious while optimizing the performance to ensure that the code remains readable and maintainable for future development and debugging purposes.

FAQs

Q: Why performance optimization is necessary?
A: Performance optimization is necessary because it makes your program execution faster with less memory usage which improves the overall efficiency of your code.

Q: Name of common performance optimization strategies?
A: Following are the common performance optimization strategies such as choosing the right data structure, avoiding nested loops, minimizing function calls, and utilizing built-in modules.

Wrapping Up

Python has provided us with many strategies to ensure performance optimization in our program. Profiling is the first choice of every developer because it helps you identify the loopholes that minimize your code performance. We also learned some built-in data structures. If incorporated into the program correctly, optimization is guaranteed. It is better to use these built-in modules instead of writing from scratch. It is also important to test how fast your program is working and try different ways to make it faster. Do not forget the readability and maintainability of the program, and keep learning to speed up your code. Thank you for reading this guide. Happy coding!

Reference

cProfile Library
timeit Library
High Performance Book: Python Performance Optimization Tips