Mastering AI and Data Skills: Building Legitimate Work Experience From Home

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Introduction

It is often said that experience is the best teacher, and this holds particularly true in the field of AI and data. Many job opportunities posted on job platforms require some level of experience, even for entry-level positions where 1-2 years of experience is increasingly the norm. As an entry-level applicant or fresh graduate, you may find yourself wondering how to gain experience when you’re just starting out. You might feel tempted to fabricate work experience to meet these requirements, but it’s important to remember that this is illegal and unethical. Instead, let’s focus on building a portfolio of genuine experience that you can be proud of and confidently discuss in interviews. The goal of this article is to help you develop credentials filled with practical experience, all from the comfort of your home or through internship opportunities.

Why Experience Matters More than Certificates

While having a related degree in AI and data or completing online training courses with certifications are valuable assets, they don’t necessarily prove to employers that you can apply your knowledge to solve real-world business problems. Employers want assurance that they are hiring someone who has successfully tackled similar challenges before. This is why candidates with work experience are often preferred over those with only academic qualifications. Although lab projects during training provide some level of experience, they are typically guided and may not fully prepare you for the complexities of real-world scenarios. In contrast, real-world experience requires initiative, problem-solving skills, and the ability to navigate various challenges independently. Employers recognize this distinction and prioritize candidates with practical experience. Therefore, your focus should be on applying the skills and concepts you’ve learned to real-world situations to demonstrate your capabilities effectively.

Gaining Work Experience

Before delving into this article, you were likely familiar with the conventional routes to gaining work experience, such as internships, volunteering, or securing a job. However, due to high demand and low supply, obtaining work experience through these avenues has become increasingly challenging. You may have found yourself conforming to the norms of the job market, relentlessly applying for opportunities on platforms like LinkedIn, Indeed, and Glassdoor in hopes of securing a position. If you’re currently employed, unpaid internships and volunteering may not be feasible options. In such cases, working on personal projects becomes a viable alternative. Similarly, as a graduate or student, combining personal projects with internships and volunteering services should be your preferred approach.

In the following sections, I will outline a simple three-step framework for gaining work experience effectively from home while simultaneously holding onto your current job. While this framework is tailored for remote work, it can also be applied to create internship and volunteering opportunities within a company.

Three-Step Framework for Gaining Work Experience

The figure below depicts a three-step framework for working on impactful projects, extracted from the Education to Professional Employment (E2PE) framework. The E2PE framework is designed to facilitate your transition from a non-tech professional to an AI and Data specialist within 3-6 months, guaranteeing a 10-50% increase in salary. Explore the full framework by grabbing a FREE copy on Graffiland website. Link

Step 1: Choosing a Project

Employers are interested in experience they can relate to, so when selecting a project, focus on impactful initiatives that will attract potential employers. It’s better to focus on three high-quality projects than multiple low-quality ones. Here are some guidelines for choosing a suitable project topic:

Opt for projects related to your industry to demonstrate relevance.
Research prevalent problems in your industry.
Choose projects to showcase proficiency in trending AI and data skills demanded by employers.
Choose projects that will require a variety a skills to be tested.
Consider data availability when selecting projects.

Step 2: Executing Projects

Before diving into project execution, familiarize yourself with the professional standards and guidelines applicable to real-world projects. Working closely with an industry professional can provide valuable guidance. When executing projects, consider the following approaches:

Independent work: Undertake projects solo, leveraging your skills and resources.
Collaborative work: Collaborate with peers or industry professionals to tackle complex projects collectively.
Internship opportunities: Seek internships to gain practical experience under the mentorship of experienced professionals.

Irrespective of the approach used, follow these guidelines to execute an impactful project:

Leverage the insights and guidance of industry professionals to ensure your projects meet industry standards.
Choose methodologies, skills, and tools relevant to the current needs of the job market to attract potential employers.
Keep your solutions simple and relatable to employers, focusing on problem-solving rather than showcasing complex techniques.
As an ongoing practice, utilise technologies such as Git and GitHub to build a technical CV as you progress.

Step 3: Project Publication

Many valuable projects remain inaccessible on personal computers, limiting their potential impact. It is very important you publish your findings on online platforms. As such making it accessible to potential employers. To maximise exposure and attract potential employers, follow these guidelines when publishing your projects:

Select platforms frequented by your target audience, such as LinkedIn, Indeed, Glassdoor, and GitHub.
Tailor your project reports to cater to both technical and non-technical audiences by varying formats (e.g., short vs. long, PDF vs. video).
Consider sending project results directly to targeted employers if your solution is relevant to their needs.

We live in a digitally connected world where information sharing knows no boundaries. By publishing your project findings, you can unlock high-paying AI and data opportunities with across the globe.

Building a Technical Curriculum Vitae

Opportunities in the field of AI and data demand proficiency in both technical (hard) and soft skills. While soft skills are challenging to assess on paper, employers can evaluate hard skills more easily. Simply stating your hard skills on your CV is no longer sufficient; you must provide evidence of your mastery.

There are two ways to provide evidence: wait to be assessed during an interview or showcase your proficiency with a technical CV. You should choose the later. Unlike a conventional CV, a technical CV demonstrates your AI and data skills through a portfolio of projects. By following the recommendations above, you’ll inherently build a technical CV using platforms like Git and GitHub.

Hosting a technical CV on GitHub signals to employers your ability to use these platforms effectively. As Git and GitHub are widely used in companies, having a strong profile there sets you apart. Leveraging these platforms demonstrates not only your technical skills but also your familiarity with industry-standard tools.

To enhance your profile, invest time in mastering Git and GitHub using the plethora of free resources available online. Once mastered, include a reference on your conventional CV directing employers to your technical CV (portfolio of projects). This streamlined approach ensures that potential employers can easily access and assess your AI and data skills.

Conclusion

In today’s competitive job market, landing opportunities in AI and data requires more than just possessing the necessary skills. It demands a strong portfolio of projects (technical CV) that showcases your capabilities to potential employers. With the strategies outlined above, you have the tools to build a robust online profile that works for you around the clock, positioning you as a qualified candidate even while you sleep.

Bonus: AI and Data Project Template

While embarking on an AI and data project is a commendable step, how you document and present your work to potential employers is equally crucial. To assist you in this endeavour, I’ve created a comprehensive template that ensures your projects are effectively communicated and stand out to potential employers. This template is included in the education to professional employment (E2PE) framework. Interested in receiving a FREE copy of this template? Click here to make a request of the E2PE framework and unlock all resources.

Bonus: Project Execution with Git and GitHub

As earlier mention, you should get comfortable using Git and GitHub to host a portfolio of your projects. If you are new to these technologies, I have made a video to help you start your learning journey. Click here to watch the full tutorial on YouTube on how to build a technical portfolio with Git and GitHub.

Author: Ivo Kubam
Date: 09-05-2024
Graffiland