When you are looking for a start in the world of data science, you might want to consider starting a data science portfolio. This shows that you have the skill to build a functional program. A portfolio is a good way of impressing an employer and landing a job. The good news is that building a strong portfolio doesn’t need to be difficult. Here are some tips you can apply.
Pick Something That Excites You
There are dozens of projects that you can do. Lots of databases that you can analyze. The key is picking something that you are interested in. After all, it will be a few weeks to months of unpaid work. If you aren’t passionate about the project, it will be easy to give up.
Types of Projects
There are a few ways that a data scientist can demonstrate their value to an organization. Your project will need to focus on at least one of these elements.
First, you can try picking a complicated data set. You can then analyze it. Try to extract useful, practical insights. For example, you might want to look at loan data and determine which groups are most likely to default.
You can also try to build some new systems, which will create a better user experience. For example, you might want to create a system for a library. It will be able to analyze what books someone is reading and determine what other novels they might enjoy.
Cleaning up a database. Often, you will come across data that hasn’t been well-maintained. This results in a messy database, which is nearly impossible to understand. As a result, it will be difficult for businesses to get any practical benefit from it. Sometimes, there will be several databases, which should be combined into one, for easier analysis.
Focus on the Business Case
One of the most important areas is making sure that a potential employer understands how the program can be put to practical use. This can be done by creating an additional document. All you need to do is write a few paragraphs explaining why you wanted to complete the project.
You’ll need to make sure that code speaks for itself. Remember, you won’t be there to explain each line and why it is important. The best way to make sure that they understand the importance of the code is by putting the markup to use.
Have at Least Two Projects
You should aim to have a minimum of two projects in your data science portfolio. If there is only one, you risk running out of things to say during the interview or repeating yourself. Two projects will give you lots of time to go in-depth on each of them. But the more the merrier. The more projects you have, the more you can showcase your skills.
Show off Different Skills
One of the biggest problems that you will face is that you aren’t showing off your skills adequately. When you are thinking about your project selection, consider what elements they will be testing. You want to make sure that you can use a range of tools and skills. This shows that you are a good well-rounded employee.
Explain the Project to an Employer
As you are working on your data science portfolio, you want to think about the way you are presenting your projects. You want to make sure that your code is easy to read and understand. Because of this, it’s common to use GitHub.
You also want to make sure that they understand the business case and the process you went through. To do this, you might want to create a PowerPoint presentation. This will allow you to go through each step and why it was important. You might also want to include a YouTube video. This will give a prospective employer a chance to get to know your personality.
Display Your Portfolio
By now you should have come up with some innovative projects and have created an impressive portfolio. Now it’s time to start advertising it to the world. There are a few ways that you can do this.
First, you might want to create a website. This is the preferred option. It will allow you to house your CV and portfolio in the same place. You can also put links to your social media and email, so recruiters can get in touch. It’s essential to make sure that your website will be easy to navigate. Often, you’ll want to put your CV and a few paragraphs about yourself at the top. As they scroll down, they will come to your projects.
Another way to promote your portfolio is to make sure that you have a strong social media presence. The best place to focus your attention is LinkedIn. This is often used by professional recruiters to find potential employees. You can also add links to your website to your Twitter, Facebook, and Instagram.
Third, you might want to start a blog. This can be a good way to discuss the insights you got from anyalzing data. You might also want to talk about the process you used to gather and interpret the information. It’s also a great way to signal that you are a good communicator and have a good understanding of the data science processes.
Start With the Basics
When you are presenting your projects, it can be tempting to jump into technical conversations and talk about the steps you took to complete them. But it’s best to start with the basics. Spend a minute or two introducing the project and how it could have a real-world impact. From there, let the employer lead the conversation. Ask them what questions they have and where they would want more information. Let them dictate how in-depth they want you to go. Remember, not everyone knows about computer science.
Conclusion
Building a strong data science portfolio doesn’t need to be a hard job. When you boil it down, there are only two elements. You need to have projects that show off your skills. Then, you need to present them in a way that will be attractive to a potential recruiter. As long as you can do this, you should be getting plenty of job offers.
If you’re only starting out in the field of data science, check out the top 16 data science courses we have compiled.