Let me tell you why and how. If you’re new in data science, “doing data science” likely sounds like a big deal to you. You might think that you need meticulously collected data.
If you’re new in data science, “doing data science” likely sounds like a big deal to you. You might think that you need meticulously collected data, all the tools for data science and a flawless knowledge before you can claim that you “do data science”. But this is not true. You can, and in fact, you should start working and playing with data as soon as you can. You don’t have to call it “doing data science” if you don’t want to but working with data will only do good for you.
In this article, I will explain why working with data as part of your current job is a great idea. I will then give you examples of projects to get your imagination going. Lastly, we will look into some things to keep in mind while working on data science projects at your current company.
First of all, you will learn a ton of skills you didn’t even know you needed by doing hands-on work. Secondly, it is a great way to hint to your future employer that you mean business, you are interested in this job and you take every opportunity to improve yourself.
To learn new skills, any type of project counts. Work with data that you can get your hands on. Simple datasets from the internet, your own WhatsApp chat history, data you found on Reddit, anything goes. Take opportunities to stretch your working-with-data muscles. You can take on bigger challenges as you up your game.
Impressing a potential employer is a different deal though. It’s hard to get ahead of the competition by showing simple projects. At that point, your proof will be the portfolio of projects with a good deal of thought gone into them. Creating a portfolio of projects is a very vast topic. In this article, I want to focus on professional portfolio projects.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
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