A step-by-step guide to building a data science portfolio
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In the field of data science, it is integral that you have an impressive portfolio if you want to get a good job according to your skill level. Employers are constantly sceptical about people lying on their resumes and that is why they require the candidates to have proof of the work they have done previously before they hire them. You might have a lot of experience in the field as an intern or as an employee. But to have a good career in data science, you will have to convey that well to your future employers, and that is where a good portfolio comes in.
Why is a Good Portfolio Important for a Data Science Professional?
You might be wondering why is a good portfolio so important in the field of data science. The answer is pretty easy: because that is what employers are looking for. A portfolio is used to demonstrate your skills and all the certification course in data science, or work you have done previously. It impresses employers when they see that you not only have the knowledge, skills and certifications but also hands-on experience in the field. This boosts their confidence in your capabilities and also makes the hiring process much easier. They will interview you, but having a good portfolio will impress them and also make it clear to them that you take this career path seriously and that you have the necessary skills to back you up. That is why, if you are looking to get a job in the data science industry, you should invest in a good portfolio that will highlight your skills and experiences.
Some Tips and Tricks to Make Your Data Science Portfolio Outstanding
Now that we have explained why you need to have a stellar portfolio to build a great career in data science, let us talk about how you should go about doing that. In simpler words, what are some of the ways to make your portfolio stand out from the rest and make sure you get the job you are intending to.
Exhibit Your Technical Skills
As you can understand, this is probably one of the most important parts of building a great portfolio. You have to include the important technical skills that you have in your portfolio, but that does not mean you have to add all of them. You can add the more important ones and the ones that you feel are more important for the job roles while leaving the simpler, normal and cookie-cutter skills out of it. This shows off your better skills and makes your portfolio more attractive.
Being passionate is important as it can persuade your employers. In the data science sector, it is important for employers to see that you are passionate about what you do. It tells them that you will take the job more seriously.
Your portfolio is only a part of your identity. Your employers can find out a lot about you through your social media, or LinkedIn and GitHub profiles. Make sure that you link all of them in your Resume and portfolio to make your personal brand appealing to them as well. Your profiles should also convey your professionalism and passion for your chosen career path.
Now that we have covered all the bases about how to build a good portfolio, let us talk about how you can improve your skills and learn data science. You can add some amazing data science and machine learning certifications to your portfolio to make it more attractive to your employers. The IHUB IIT Data Science certification course that also teaches machine learning, will help you improve your portfolio for your next job.