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Gold BlogThe Best Data Science Project to Have in Your Portfolio


If you are trying to find your first path into a Data Science career, then demonstrating the quality of your skills can be the greatest hurdle. While many standard projects exist for anyone to complete, creating an original data-driven project that attempts to solve some challenge is worth so much more. A good Data Scientist is one that can solve data-related questions, and a great Data Scientist poses original data-related questions and then solves.



Photo by Jon Tyson on Unsplash.

Data science has experienced tremendous growth in recent years. The potential to create value out of data has attracted businesses, which, as a result, has driven new investments in this field.

The popularity and potential of data science, along with the increasing demand for data scientists, cause lots of people to make a career change to work in this field.

The biggest challenge for aspiring data scientists is to take the first step into the field. I think what makes it hard to take the first step are the following reasons:

  1. Data science is an interdisciplinary field, so it is difficult to obtain and evaluate the required skills.
  2. Data science is still evolving, so it is not well established in the traditional education system yet.
  3. There is no straightforward way to demonstrate your skills if you do not have prior job experience.

In this article, I will elaborate on the third reason and provide my suggestion.

If you follow Medium publications on data science, you must have seen articles that list data science projects to put in your resume or portfolio.

They all are good for practicing hard skills such as coding, data wrangling, data science libraries and frameworks, machine learning algorithms, and so on. However, they lack an important skill.

Photo by Azazello BQ on Unsplash.

Can we even compare the skill of creating the Rubik’s Cube and the skill of solving it?

A data scientist identifies a problem and comes up with a suggested solution. The projects listed in those articles lay out the problem and solution for you. Moreover, it is relatively easy to obtain the required data in a neat and clean format.

Since most of those projects are so common, the solution or implementation can easily be found online. Thus, think of them as projects to practice your skills. Unfortunately, they are not enough to convince hiring managers that you are a good candidate for a data scientist position.

What we should do instead is to find a problem that can be solved with data and design our solution. The problem does not have to be complex, and we do not have to provide the best and most efficient solution. We might even fail to properly solve the problem.

However, being able to frame a problem that can be solved with data is more valuable than completing such commonplace projects. It proves your analytical thinking skills and clearly demonstrates that you have a comprehensive understanding of data science.

It helps a lot to convince your future employer to have one or two projects that you build from scratch. You might even end up having a new business idea. Another advantage of having unique projects is that they attract recruiters and hiring managers. They are likely to reach out to you instead of you applying to numerous positions.

You may argue that it is a highly challenging task to come up with a new project idea. I completely agree with you. This is the reason why I call it the best project to have in your portfolio.

I’m aware that it takes a great amount of time, effort, and thinking to come up with a unique project idea. Moreover, you will be spending long hours trying to implement your idea. It is important to point out that you might end up having a failed project. However, what you learn throughout the process are likely to be some skills that you cannot learn from a MOOC course or any tutorial.

You will also be improving your skills on how to approach an issue. You will learn how to evaluate a task from different perspectives. In some cases, the solution you have in mind does not fit the libraries or frameworks you are comfortable working with. Thus, it will also motivate you to learn new tools.

 

Conclusion

 

The 10 projects to have in your portfolio sounds appealing. I did some of those projects too. However, keep in mind that most people you compete with to get a data scientist job are doing those 10 projects as well. You will not be left behind, but doing the same thing will not take you further ahead either.

Hiring managers or recruiters will know how much you put into a project. Some of the popular commonplace projects can be completed in a day or two. Thus, you will have a hard time demonstrating your skills based on those projects.

I’m definitely not against doing these common projects. They are valuable in terms of practicing and improving your hard skills, but that’s all they can offer.

On the other hand, it makes you an exceptional candidate for the job if you present a project designed and implemented by you.

Original. Reposted with permission.

 

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