# Building a Career Vision

# Why?
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?

# Can you skip to the chase?
Okay, okay, I know this sounds a little wishy-washy, but I think it comes down to forming a simple procedure:

* Discover specific areas you want to work in
    * Tip: Start generic (i.e. data science) and then narrow it down as you go (i.e. specifically NLP within data science)
* Find where you want to be/what you want to do
* Set a **vision** of your future work (a long-term goal)
* Find out **how** you can work your way to becoming this person
* **Work** and get paid

# Finding Purpose
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](https://trello.com/b/hmpyIMi6/job-seeking) 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).

# Gaining Skills
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**.

# Becoming Employable
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:
* Technical (everything above)
* Communication
* Business/Domain Specific
* Personal development

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](incremental_improvement.html).

# THANKS FOR READING!
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](https://www.pxfuel.com/en/free-photo-jrcsc)*