Building a Career Vision

Super passionate up and coming data scientist documenting my journey! I dedicate my time to learning and creating ML content (data science projects and blog posts).
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Super passionate up and coming data scientist documenting my journey! I dedicate my time to learning and creating ML content (data science projects and blog posts).
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My Tech Portfolio

Cool unique data makes for intriguing projects, so let's go find some on the web! Today we'll get what we need to tell a story about the magic-making GitHub projects popular ⭐🌟⭐. Readmes, descriptions, languages... we'll collect it all. So, let the ...

Let's see how our machine Learning, project planning and essential coding tools can be brought to life in a real-world project! Today we're going through how we can predict how much energy we use daily using temperature data. We start here with impor...

Having a visual product, website or dashboard to show what your arduous efforts on a coding/machine learning project amounted to is something truly spectacular! Yet, it's often extremely difficult as numerous tools and technologies are usually requir...

Let's see how our machine learning, project planning and essential coding tools can be brought to life in a real-world project! Today we're going through how we can predict how much energy we use daily using temperature data. We previously imported a...

I've recently been questioning how to build relevant experience to acquire a job. After taking countless experts advice I've decided to tie it all together in one place. Note that here I'm including my own insights and derived steps, to provide a personal path to future success.
You may ask why is this a vision, and the answer to that is that you're not just deciding what to work on next, you're really questioning where and who you want to be?
Okay, okay, I know this sounds a little wishy-washy, but I think it comes down to forming a simple procedure:
Data science or machine learning are broad, overarching terms, which are quite ambiguous and so need to be further researched. Now I don't mean deciding whether to decide on being a machine learning engineer or data scientist since these terms are often mixed up. Instead find domains (like finance, health or security) to work in. The hope is that narrowing down your options allows you to define focal points or qualities of what to be. These will form your vision.
The way I narrowed my options down was to create a Trello board where I listed, sorted and described companies around me. I took this one step at a time, first searching startups near me, reading about them (on their websites), ordering them, adding extra details (like positions mentioned) and finally entering their domains. Please note that you can't categorize all companies, as some are just too new/unique, however, consider leaving these aside for now (you should have plenty of others).
To understand how to make your vision real you'll need to take the domains from before and discover what resources already exist about them. From here you should be able to decide which data sets you can access, and the common tasks completed on the job (like data cleaning). The reason we prefer this overlooking through the Iris data set is that everyone follows tutorials. You want to stand out with projects which are both achievable and somewhat unique.
Technical knowledge and an ability to problem-solve don't directly make you employable. This is because there are a plethora of other skills which can undermine one's ability to solve difficult problems. The important ones here are:
You can demonstrate your ability to communicate through writing blogs, writing documentation for your projects/making them publicly available and more. For business/domain-specific skills I think you just need to research and apply your skills (with projects). Finally, personal development is building yourself, something I've already discussed in my previous blog on soft skills.
Now that you've heard me ramble, I'd like to thank you for taking the time to read through my blog (or skipping to the end). I hope this helps to figure out what's needed to get a data science job.
Cover image sourced from here