5 Ways to Better Personal Projects
We’ve all heard the same thing: build personal projects. You’ve probably made a bunch. And you’ve coded, organized, and presented them. But there’s the question:
Just how do you build them better?
Here are 5 tips to really improve your personal projects:
- Rebuild and improve on old ones
- Focus on Presentation (Add videos or PowerPoint)
- Talk about Business Value and the Use Cases
- Talk about Limitations of Data
- Talk about next Steps
In this article, we’ll dig into these tips, that will help you stand out in your job search.
1. Rebuild and Improve on Old Projects
When working on personal projects for machine learning, it’s common to come across older projects that may no longer be effective or relevant.
Rather than building and planning a new one, consider rebuilding and improving older projects. Since the last time you’ve touched them, you’ve likely acquired new tech, project, and domain knowledge. This can help you save time and resources, and apply your knowledge to a project you are familiar with.
When rebuilding and improving on older projects, it’s important to identify the specific areas that need improvement. Consider reevaluating the project’s objectives and goals to ensure that the updated project meets your current needs and aligns with your interests and passion.
By rebuilding and improving on old projects, you can demonstrate your ability to update and refine existing models, which can be a valuable skill in the workplace.
2. Focus on Presentation
While building effective machine learning models is crucial, it’s also important to focus on the present. This shows you are not only able to build models. But demonstrate the value of them, along with a personal project.
This is critical, since not every hiring manager looks at your code. Adding a presentations, charts, and summaries helps them immediately understand the value of the project.
Consider adding visualizations, videos, or dashboard to your project readme. Link them in the readme too. You want to make it easier them — they need to get different dimensions of what you as a candidate can offer. Make it engaging. Take them into your world. Let them explore your experience.
By focusing on the presentation of the results, you demonstrate your ability to communicate complex data insights to a non-technical audience. Which is a rare and a valuable skill in the workplace. It shows you can give exponential value as a employee.
Thanks for reading DataLife360! Subscribe for free to receive new posts and support my work.
3. Talk about Business Value and Use Cases
When working on personal projects for machine learning, it’s important to consider the business value and use cases of your project.
This can help you tailor your project to the specific industry you are targeting. While demonstrating that the project provides meaningful insights. This is essential — it shows you are thinking of BOTH the technical and business dimensions.
Consider identifying a specific problem or question that you want to answer with your project. This goes for dashboards, predictive ML models, data engineering pipelines, etc. This can help you stay focused and ensure that your project has an business application. This is very valuable, since you can quantify the value of your projects.
Write down use cases that your project was meant to solve. Write down the business questions you wanted to answer. Start from there. Show your thinking process succinctly.
This can demonstrate your ability to identify and address real-world problems using machine learning, which is a valuable skill in the workplace.
4. Talk about Limitations of Data
Personal projects for machine learning are not immune to the limitations of data.
Data can be biased, incomplete, or even irrelevant, which can lead to inaccurate models, wonky dashboards, and queries that pull a lot of dirty data.
It’s crucial to identify and address these limitations to ensure that the insights gained from your personal projects are accurate and effective. Note them in your personal project readme or in your presentations. Be ready to explain how you addressed those limitations.
By addressing these limitations, you can ensure that the insights gained from your personal projects are accurate and effective, leading to better decision-making. This can demonstrate your ability to work with real-world data and address its limitations, which is a valuable skill in the workplace.
5. Next Steps Section
As you work on your personal projects for machine learning, consider ways to improve it.
A next steps section in a personal portfolio project showcases your vision and thought process. You demonstrate that you can reflect on your work. And you can plan for the future.
It helps to demonstrate to potential employers, clients, or collaborators that you are not only capable of completing a project, but that you are also able to identify areas for improvement. And realized the potential course of action you can take.
It can also show your strong commitment to continuous learning and growth. You can use this section to discuss any challenges you faced during the project and how you plan to overcome them in future iterations.
This shows that you are aware of the limitations of your current project and are thinking about how to expand and improve upon it in the future.
Its the most important. It helps your portfolio stand out and make you a more appealing candidate.
Conclusion
This isn’t limited to data scientists. Its also important to do it for data engineers and data analysts too.
Personal projects are important for machine learning engineers. Here are five tips to make your projects better:
- Improve old projects to save time and resources.
- Focus on presentation to show value and engage your audience.
- Consider the business value and use cases of your project.
- Be aware of data limitations to ensure accurate insights.
- Include a Next Steps section to demonstrate your commitment to growth.
By following these tips, you can differentiate yourself from other candidates and demonstrate your skills to potential employers, clients, or collaborators. Remember, personal projects are an opportunity to learn, grow, and showcase your abilities. So keep building and improving.
Don’t reinvent the wheel. Make it more efficient.
If you liked what I wrote, check out my LinkedIn and YouTube channel:
Thank you for Reading!
