Forem: Sarah Williams

How to Start a Career in Data Science Without a Technical Degree

Sarah Williams — Tue, 07 Apr 2026 06:53:40 +0000

Unless you’ve been living under a rock, you’d know about tools like OpenAI’s ChatGPT, Google’s Gemini, X’s Grok, and other tools, which are too many to keep up with. What do all of them have in common? They are built using artificial intelligence, which is a subcategory of data science. That’s how valuable data science is.

Given its importance, many people believe that entering the field of data science requires a degree in computer science, statistics, or a related technical discipline. That belief holds some truth, but does not tell the full story. In fact, growing numbers of data science roles are open to individuals who develop the right skills through non-traditional paths. This blog shows you how to build a data science career even if you don’t have a technical degree.

Why Data Science Careers Are in High Demand

Data science is no longer confined to tech companies. Nearly every sector, including healthcare, finance, retail, energy, and public policy, is pulling insights from data to drive decisions.

The U.S. Bureau of Labor Statistics (BLS) projects that employment of data scientists will grow 34% between 2024 and 2034, much faster than the average for all occupations.
On average, about 23,400 job openings per year are expected for data scientists over that same period, due both to growth and replacement needs.
Globally, the data science tools & platforms market is expected to grow at a compound annual growth rate (CAGR) of around 26-27% in recent years, reflecting expanding organizational investment in analytics and AI infrastructure.

Because companies collect more data, expect complex datasets, improved computational tools, and a stronger competitive advantage from data insights, the demand for people who can interpret, visualize, model, and explain data will likely increase.

Myth vs. Reality: Do You Really Need a Technical Degree?

The Traditional Path

Traditionally, data science roles favored candidates with degrees in:

Computer science or software engineering
Mathematics, statistics, or applied mathematics
Data engineering or information technology

These degrees provide formal exposure to algorithms, rigorous mathematical theory, data structures, and possibly even research work.

The Modern Path

However, many employers now emphasize demonstrable skills and results over a specific degree. If you can show proficiency in data manipulation, clear visualizations, domain knowledge, and the ability to collaborate across disciplines, you can be competitive.

Major educational platforms like Coursera note that individuals with non-technical degrees have successfully transitioned into data science roles by using online courses, bootcamps, and projects.
Research indicates that for AI and related technical roles, some employers are reducing strict requirements for formal educational credentials, instead valuing skill-based hiring. For example, studies in the UK showed that mentions of university education requirements in job adverts for AI roles declined by around 15% between 2018-2023, while skill requirements rose.

So, while a technical degree can still open doors and may make some paths easier, it is not strictly necessary.

Essential Skills You Can Learn Without a Degree

You will need to acquire several core skills. Here are those that matter most.

Basic Statistics & Mathematics

You should understand:

Probability theory (distributions, Bayes’ theorem)
Descriptive and inferential statistics (mean, median, standard deviation; hypothesis testing)
Calculus and linear algebra basics (especially for machine learning)

Programming & Tools

You do not need to become a software engineer, but certain programming skills are essential.

Python or R for data analysis
SQL for querying databases
Familiarity with version control (e.g., Git)
Tools like Jupyter Notebooks, or cloud platforms for data storage and processing

Data Visualization & Storytelling

Converting data into actionable insight depends not just on numbers, but on how you communicate them.

Learn visualization libraries (Matplotlib, Seaborn, ggplot2)
Use tools like Tableau or Power BI
Practice writing or presenting your findings to non-technical audiences

Domain Knowledge & Communication

Understanding context (finance, healthcare, marketing, etc.) helps. You will often translate business questions into data problems. Strong written and verbal communication skills are essential.

Problem-Solving & Critical Thinking

Data science is full of ambiguity. You will encounter dirty data, conflicting requirements, or unclear metrics. Being able to break a problem down, ask the right questions, and validate your assumptions will serve you well.

How to Build Your Own Path

There are multiple ways to gain the skills needed for a data science career, even without formal education in the field.

Online Learning

Educational platforms offer comprehensive courses covering everything from beginner statistics to advanced machine learning. Many programs include hands-on projects, which allow you to practice with real datasets.

Bootcamps

Intensive training programs focus on practical skills and often include career support. These are suitable for individuals who prefer structured, fast-paced learning with a clear outcome.

Self-Study

For self-motivated learners, free resources such as open datasets, tutorials, and open-source projects provide endless opportunities to practice. Many professionals build their skills by experimenting independently and sharing their work publicly.

Building a Portfolio

A strong portfolio is the most effective way to demonstrate ability. Collect your projects, case studies, and visualizations into one place. A portfolio proves technical skill and showcases creativity and problem-solving ability. Adding personal branding elements, such as designing with a professional logo maker or having a responsive website, can enhance credibility.

Networking and Community

Skills alone may not guarantee opportunities. Networking plays an equally important role in career growth.

Joining Communities

Online forums, professional groups, and local meetups allow you to learn from others, share work, and stay updated on trends. Being part of a community also provides accountability and support during the learning process.

Finding Mentors

Mentorship shortens the learning curve. Experienced professionals can provide guidance, review your projects, and offer career advice. This relationship helps you avoid common mistakes and accelerates progress.

Participating in Competitions

Challenges and competitions are excellent ways to practice under real conditions. Working with teams on problem statements gives you exposure to collaboration, time management, and presenting results, all of which are valuable professional skills.

Common Challenges and How to Overcome Them

Breaking into data science without a technical degree is not without obstacles.

Knowledge Gaps: Without formal coursework, you may lack a structured foundation. Overcome this with a curated learning plan that balances theory and practice.
Employer Skepticism: Some recruiters may still favor candidates with degrees. Address this by showcasing a strong portfolio and certifications.
Imposter Syndrome: Entering a technical field can feel intimidating. Building small wins, seeking peer feedback, and engaging in supportive communities can help.

Conclusion

You can launch a career in data science without a technical degree if you build relevant skills, create demonstrable work, engage with communities, and communicate clearly. Organizations are looking increasingly for results, not just credentials.

Start by choosing one skill to master—maybe Python, maybe visualization. Build a project, share it, get feedback. Over time, your portfolio, connections, and experience will speak louder than any degree. Clarity of work, effort consistency, and output relevance will matter more than the letters after your name.

I Built My First WooCommerce Website — Here’s Everything I Did Wrong

Sarah Williams — Mon, 05 Jan 2026 10:49:56 +0000

When I built my first WooCommerce website, I thought I was making a safe choice. WordPress was familiar. WooCommerce was popular. There were thousands of plugins and themes. It felt flexible and cost-effective. “What can go wrong?”

That confidence did not last long.

WooCommerce is powerful, but it is not simple. Many of the problems I ran into were not bugs. They were mistakes caused by poor assumptions and a lack of planning. Today, I will break down those mistakes, explain why they matter, and share practical learnings for anyone building a WooCommerce website for the first time.

Mistake No. 1 - Underestimating WooCommerce Complexity

WooCommerce looks simple on the surface, but it introduces deep architectural and performance considerations that many first-time users do not anticipate.

WooCommerce Is Not “Just a Plugin”

WooCommerce installs like a plugin, but it behaves like a full eCommerce system. Once activated, it adds custom post types for products and orders, new database tables, background processes, scheduled tasks, and a large number of hooks and filters.

This matters because WooCommerce changes how WordPress behaves under load. Product pages, carts, and checkouts rely on dynamic data and database queries. According to WooCommerce documentation, even basic features like cart sessions and tax calculations involve multiple requests per page load. I treated it like a blog plugin. That assumption caused performance and maintenance problems early on.

Learning Curve Compared to Shopify

Platforms like Shopify hide most of the complexity. Hosting, caching, and security are handled for you. WooCommerce gives you control, but that control comes with responsibility. For beginners, this difference is easy to miss. WooCommerce does not fail loudly. Instead, it slows down, breaks quietly, or behaves inconsistently.

Mistake No. 2 - Choosing the Wrong Hosting Environment

The hosting environment plays a critical role in WooCommerce performance, stability, and scalability, and poor hosting choices amplify problems as the store grows.

Shared Hosting Performance Issues

My first WooCommerce site ran on cheap shared hosting. It worked fine during setup. Problems started once products, plugins, and traffic increased.

WooCommerce is resource-heavy. It uses PHP, MySQL, AJAX requests, and REST API calls. On shared hosting, these compete with other sites on the same server. This often leads to slow admin panels, delayed cart updates, and timeouts during checkout.

Hosting providers like Kinsta and WP Engine clearly state that WooCommerce requires higher memory limits and better CPU allocation than a standard WordPress blog.

Not Optimizing Server Settings

I also ignored PHP settings. The default PHP memory limit was too low. The PHP version was outdated. There was no object caching in place. These issues are not advanced optimizations. They are basic requirements for running WooCommerce reliably.

This is one area where an experienced WooCommerce development service can add immediate value by configuring PHP, caching layers, and security correctly from day one.

Mistake No. 3 - Installing Too Many Plugins

Excessive plugin usage increases load time, security risk, and maintenance complexity, often creating more problems than it solves.

Plugin Conflicts and Load Time

One of my biggest mistakes was adding plugins to solve every problem. Need reviews? Add a plugin. Want faster pages? Add another plugin.

Each plugin adds PHP execution time, database queries, and frontend assets. According to WordPress performance studies, poorly optimized plugins can increase Time to First Byte by several hundred milliseconds. Over time, the site became slower and harder to debug.

Security and Maintenance Risks

Plugins also increase risk. Every plugin needs updates. Every update can break something. Unmaintained plugins are a common attack vector in WordPress security reports. Fewer plugins mean fewer problems.

Mistake No. 4 - Ignoring Performance Optimization Early

Delaying performance optimization leads to slow page loads and poor user experience, especially as product catalogs and traffic increase.

Unoptimized Images and Assets

Product images were the largest issue. I uploaded high-resolution images straight from a camera. I did not compress them. I did not use modern formats like WebP.

Google’s web performance guidance is clear: images are often the largest contributor to page weight on e-commerce sites. This alone slowed down product pages and affected mobile users.

No Caching Strategy

Caching was another blind spot. Page caching helps, but WooCommerce pages like cart and checkout cannot be cached normally.

WooCommerce sites benefit from layered caching:

Object caching for database queries
Browser caching for static assets
CDN for images and scripts

Without this setup, performance problems appear as traffic grows.

Mistake No. 5 - Poor Theme Selection

Choosing a visually appealing but poorly optimized theme can negatively affect WooCommerce functionality, performance, and long-term maintainability.

Choosing Design Over Compatibility

I chose a theme based on how it looked in the demo. I did not test how it handled carts, checkouts, or mobile shopping. Some themes are not built with WooCommerce best practices.

This leads to layout issues, broken templates, and poor accessibility. WooCommerce officially recommends themes that follow its template structure.

Bloated Themes and Page Builders

The theme also relied heavily on a page builder. This added large JavaScript bundles and unused CSS. Over time, this hurt Core Web Vitals, especially Largest Contentful Paint. A lighter theme would have reduced complexity and improved long-term performance.

Mistake No. 6 - Not Understanding Checkout and Cart Behavior

WooCommerce’s dynamic cart and checkout processes require special handling, and misunderstanding them often leads to caching and usability issues.

Cart Fragments and AJAX Issues

WooCommerce uses AJAX cart fragments to keep cart data updated across pages. This feature often bypasses caching and creates extra requests.

I did not understand this behavior. As a result, performance tools showed inconsistent results, and fixes were applied in the wrong places.

Payment Gateway Surprises

Payment gateways also introduced issues. Sandbox environments behaved differently from production. Some gateways added hidden fees or redirected flows, affecting the user experience. These are not edge cases. They are normal parts of WooCommerce that require planning.

Mistake No. 7 - Weak Security Setup

Inadequate security practices put customer data and business operations at risk, making WooCommerce sites attractive targets for attacks.

Default Admin and Login Settings

WooCommerce stores personal and order data. That makes it a target. At first, I used default admin usernames and weak access controls. This is risky. Security guides from OWASP and WordPress both stress the importance of strong authentication and limited privileges.

Missing SSL and Backups

SSL was enabled late. Backups were inconsistent. Updates were done without safety nets. For an e-commerce site, this is unacceptable. SSL and regular backups are basic requirements, not optional features.

Mistake No. 8 - Not Planning for SEO and Product Structure

Without a clear product structure and SEO strategy, WooCommerce stores struggle with discoverability, usability, and duplicate content issues.

Poor Product Categorization

Products were added without a clear category structure. Navigation became confusing. Search engines also struggled to understand the site hierarchy. Clear categories improve user experience and crawlability.

Duplicate Content Issues

Product variations and filtered URLs created duplicate content. Without proper canonical tags and noindex rules, this diluted search visibility. SEO problems are harder to fix later than during setup.

Summing it Up

WooCommerce is flexible, but it demands planning. Most early problems come from assumptions, not from the platform itself. If you treat WooCommerce like a full eCommerce system, it performs well. If you treat it like a simple plugin, it will fail slowly and quietly.

Mistakes are part of the process. Learning from them early saves time, money, and frustration later.

Top Code Editor Extensions That Save Time

Sarah Williams — Wed, 22 Oct 2025 07:24:03 +0000

Modern software development often involves juggling many tools, repetitive tasks, and frequent context switching. A well-chosen set of code editor extensions can reduce friction, speed up workflows, and let you focus more on solving problems rather than fighting your environment. Let’s discuss the key categories of extensions and how to decide what to install.

Why Extensions Matter for Developers

When you spend your day writing, debugging, or refactoring code, even small savings per task add up. Extensions help by:

Catching errors before runtime (saving debugging time)
Automating formatting, linting, or code style enforcement
Providing useful shortcuts for repetitive tasks
Embedding tools that avoid switching out of the editor

For example, a developer who formats code automatically with each save can avoid spending 15-30 minutes per code review just ensuring style consistency.

Categories of Time-Saving Extensions

Here are six categories of extensions that tend to offer real value. I list examples in each, with how they help, and when to use them.

1. Code Formatting and Linting

Code style inconsistencies cause friction during reviews. Linters find issues (unused variables, style violations) before pushing code. A formatter (auto or on-save) ensures consistency.

Options to Install

Prettier (for JavaScript/TypeScript, CSS, etc.): Formats automatically; removes debates about indent style or semicolons.
ESLint: Highlights potential bugs (e.g., misuse of variables) early.

When to Use It

In any team setting or open-source project where style consistency and maintainability matter. Also useful for solo developers to avoid errors that only show up later.

2. Debugging and Error Checking

Instead of running your full test suite or deploying, you catch issues in the editor: syntax errors, type problems, and missing imports. Also, debugging tools let you step through code without leaving the editor.

Options to Install

SonarLint: Catches potential security or code quality issues as you type.
Quokka.js: Shows live evaluation of expressions in JavaScript/TypeScript while you write code. It helps during prototyping and quick experiments.

When to Use It

When you are working on code where stability matters (backend, production services) or during exploratory work, where you want fast feedback.

3. Productivity and Workflow Automation

These extensions remove repetitive overhead: inserting boilerplate, managing imports, scaffolding, snippets, etc. They free mental space for bigger logic.

Options to Install

Import Cost: Shows the size of an imported library immediately. That helps you avoid adding large dependencies unnecessarily.
Auto Rename Tag or Auto Close Tag: Good for HTML / XML editing, reducing repetitive typing.
Emmet: Expands abbreviations into full HTML/CSS code; very useful in frontend work. (Several lists of top extensions include Emmet among the essentials.

4. Version Control & Collaboration

Working with git or other version control inside the editor means fewer context switches. It’s easier to view history, blame, diff, and merge right where the code is.

Options to Install

GitLens: Shows who last changed each line, commit history inline, and helps in code reviews. Developers often say it helps them understand code ownership quickly.
Extensions that allow previews of Pull Requests, inline diffs, or resolving merge conflicts inside the editor.

5. Testing & API / HTTP Tools

Running tests, inspecting API responses, or mocking requests directly in the editor avoids jumping between the editor, terminal, and external tools.

Options to Install

REST Client extension: Send HTTP requests from a file in your editor, view responses, and test endpoints without switching to a browser or Postman.
Extensions that integrate test runners or allow inline test feedback (e.g., showing which tests passed/failed as you save code).

6. UI / Theme Enhancements

A clean, well-configured editor interface reduces visual noise, helps you stay organized, and improves readability. That can reduce mental fatigue over long coding sessions.

Examples

Material Icon Theme, File Icons (icons per file type) make projects easier to scan.
Workspace color highlighting (for example, when working on multiple project windows) to avoid confusion.

How External Design Resources Fit In

While extensions speed up coding, design and visual consistency matter, too. Websites offering pre-designed templates can be useful complements.

For example, an online design tool provides ready-made website builders to edit. These templates save time in the early stages. Rather than designing from scratch, you can pick a design template, customize the layout or branding, and integrate it into your site. A well-designed template gallery allows selecting by industry, adjusting styles, and downloading assets, which helps both solo developers and teams.

Using such a resource in combination with editor extensions (for code consistency, formatting, linting, etc.) gives a more efficient end-to-end workflow: design + code.

How to Choose the Right Extensions (and Avoid Overload)

Installing many extensions can slow your editor or introduce conflicts. Use this checklist when choosing:

Match your project’s needs. If you work mostly in JavaScript, extensions for JS/TS linting, formatting, and auto imports will matter more than tools for other languages.
Check maintenance. An extension that hasn’t been updated in months may not support the latest versions of your language or editor.
Observe performance cost. Some extensions use more CPU or memory; the monitor editor starts up, and typing lags after installing.
Trial period. Try an extension for a week. Measure whether it saves time rather than creating friction.
Avoid duplication. If two extensions do similar things (e.g., two linters or two theme switchers), choose one.

Conclusion

Good code editor extensions can make everyday tasks smoother. They help you avoid errors, reduce repetitive typing, streamline debugging, and maintain consistency. When you also complement your workflow with design resources like ready-made templates or editable website themes, you cover both code and visual design efficiently.

Choose extensions that match your work, test them, and remove what slows you down. Over time, your workspace will reflect the tools that save you minutes, if not hours, every week.

The Technical Challenges of Remote Work (and How to Solve Them)

Sarah Williams — Fri, 13 Jun 2025 12:16:47 +0000

Remote work, which started as a trend, has now become a standard for tech companies around the globe. Yes, this flexibility is worth celebrating. But it also comes with different challenges that developers, DevOps engineers, and IT managers must face head-on.

Today, we’ll discuss the challenges and offer solutions. This will help you manage with collaborate with your remote development team, as you can refine your infrastructure, improve workflow continuity, and strengthen your team’s productivity. Let's begin.

Unstable or Slow Internet Connections

Why It’s a Problem

Stable connectivity is what keeps your remote team connected and running. If you’re working from a rural area, shared housing, or some other location where the internet quality isn’t that great, you can face dropped calls, sluggish code pills, or lagging remote desktop sessions.
Additionally, when it comes to the platforms, your cloud IDE's responsiveness is likely to be impacted. It can lead to slow work delivery and increased frustration.

How to Solve It

Optimize for Offline Work

Encourage workflows that reduce dependence on real-time cloud access. Local repository clones and test environments can maintain productivity when connectivity drops.

Preload Resources

When working with large assets, you might want to pre-download and catch what you can.

Use Resilient Access Tools

Remote access tools like Mosh, Apache Guacamole, and more are designed for efficiency over poor connections that can make a huge difference. The team doesn’t need to wait for a VM to load or a server to respond.

Security and Data Privacy Concerns

Why It’s a Problem

Security is harder to manage when team members are on a different network. Office environments offer perimeter-based security with firewalls, VLAN segmentation, and controlled physical access. In contrast, home networks are often shared, undersecured, and inconsistent.

Accessing sensitive systems or data without proper safeguards increases the risk of breaches, leaks, and compliance violations.

How to Solve It

Enforce Zero Trust Principles

Remote security works best when every connection is treated as untrusted. Implement policies that require verification at every step: identity, device, and access scope. Multifactor authentication (MFA) and role-based access control are essential here.

Use Encrypted Connections by Default

Ensure all remote work connections use end-to-end encryption. SSH, VPN, or HTTPS should be enforced for all remote services.

Use Purpose-Built Remote Access Platforms

Rather than relying solely on VPNs or SSH tunnels, use secure access platforms like HashiCorp Boundary and StrongDM. These systems offer managed authentication, logging, and session isolation, making auditing and securing access easier without extensive IT overhead.

Inconsistent Access to Development Environments

Why It’s a Problem

Not all developers use the same operating system, tools, or setup. When working remotely, this inconsistency becomes more than a mild inconvenience, creating friction during onboarding, debugging, and collaborating. One person’s “it works on my machine” becomes another team member’s full-day blocker.

Remote workers also struggle with accessing private infrastructure (e.g., internal APIs or protected databases) from their location. VPN access is often slow, and server configurations may not be portable.

How to Solve It

Use Containerized Environments

Containerized development (with tools like Docker) allows you to ship a fully defined environment to anyone, regardless of OS. Everything from dependencies to runtime versions can be codified.

Provide Remote Access to Configured Environments

If local containers aren't enough, offer cloud-based workstations or VMs configured identically to your production-like dev environment. Access platforms like Visual Studio Code Remote and Coder let developers connect to these environments securely and quickly from anywhere.

This reduces the inconsistency between setups and ensures that teams have a uniform place to debug or test.

Reduced Ability to Test in Shared Infrastructure

Why It’s a Problem

Testing often relies on shared environments. Developers may have limited or delayed access to the necessary infrastructure when working remotely, especially for staging, integration, or hardware-specific tests. This leads to delays in deployment cycles and confidence gaps in releases.

Even cloud-based test environments can be unstable or uncoordinated if too many users share them.

How to Solve It

Automate Test Infrastructure

Set up on-demand test environments that spin up with each pull request using CI/CD services like GitHub Actions, GitLab CI, or CircleCI. These environments are ephemeral, consistent, and isolated.

Use Environment Management Tools

Tools that manage resource usage and access, like feature flags or test resource schedulers, can help mitigate bottlenecks.

Centralize Access

By routing access through services like Teleport and Ngrok, teams can avoid manual coordination. Developers connect to staging environments without worrying about VPN issues, access lists, or port forwarding.

Collaboration and Tool Fragmentation

Why It’s a Problem

Remote teams often use an overload of tools: one for messaging, another for code review, a third for task tracking, and yet another for screen sharing. While each tool serves a purpose, juggling between them can lead to lost context, duplicate communication, and confusion about who owns what. This hampers collaboration and slows decision-making.

How to Solve It

Consolidate Where Possible

Choose tools that integrate natively with one another. For example, Slack, GitHub, and Linear offer cohesive workflows where pull requests, deployments, and issue tracking coexist.

Define a Tooling Protocol

Set expectations for how and when each tool should be used. This avoids duplication (e.g., code review comments in GitHub vs. Slack messages about the same thing).

Centralize Access to Infrastructure

When it comes to infrastructure access, like code environments, servers, or assets, minimize the number of entry points. Tools like GitHub Codespaces and Gitpod function as gateways to all the needed tools and environments, reducing the workload on team members.

Onboarding Remote Developers Is Hard

Why It’s a Problem

Bringing a new developer onto a remote team requires many steps, like paperwork and setting them up with everything they need to code, test, and deploy. When onboarding requires dozens of manual steps like setting up local environments, provisioning access, and configuring APIs, it slows productivity from day one.

How to Solve It

Automate Onboarding Steps

As part of your onboarding playbook, use scripts to automate the setup of the development environment. Installing dependencies, clone repositories, configuring files, and initialize containers are a few examples.

As important as the onboard process is, having a smooth offboarding process is equally necessary. After revoking access to platforms and environments, human resources can opt for the services of a company offering employee equipment retrieval. This ensures a hassle-free return of the organization’s assets and prevent data leaks.

Simplify Sharing Credentials

Make it easy to grant (and revoke) access to infrastructure through centralized tools. There are cloud sharing platforms like Google Drive, OneDrive, and Dropbox to help new hires get secure access to credentials and assets with predefined roles and permissions, without much hassle.

Pair with Asynchronous Support

Tools like Loom, Notion, and GitHub Discussions can replace over-the-shoulder guidance. They allow new developers to get context and answers at their own pace.

Conclusion

The flexibility of remote work comes with its share of challenges, as we’ve talked about here. But in addition to knowing what to expect, we've also told you potential solutions to deal with these possible issues you can encounter.

From streamlined access to dev environments to secure connections and simplified onboarding, tools built for the realities of remote work help development teams remain productive, collaborative, and secure, no matter where they’re located.