Introduction: Welcome to JungleML!
Artificial intelligence is a mature yet exciting field recently introduced to a technology market that is currently saturated with IT and software. Now is the best time to make that switch no matter what your occupation is right now.
The world is cluttered with data and most companies have no idea what the hell to do with it. However, harnessing the potential trends and patterns hidden in that data can shift company strategies and projections. With ever increasing computational capabilities, predictive modeling is more powerful than ever. That is where you come in.
There are no clear-cut defined tasks for a data scientist. In fact, variants of the job role exist on the market today that share some similarities but often tackle different problems, including:
- Data Engineer (Big Data)
- Machine Learning Engineer
- Data Scientist
- Machine Learning Research Scientist
- Artificial Intelligence Engineer
Some of these positions are easier to qualify for than others. Some may require an advanced degree (PhD/Masters). Some may require a specific technical background. However, all these technical roles play a major role in building the next ‘smart’/AI product on the market.
This blog is in line more with the machine learning side of data science, than say, big data. If I had to give myself a label, it would be in line with an AI/ML engineer. However, all of these roles require a general understanding of statistical processing and programming.
I wrote a Python script that scraped a bunch of job postings off of Indeed.com in order to obtain a general sense of the qualifications needed for a specific job. You can check out my post on that here.
Anyways, you’re probably wondering, “But what do I specifically have to do to get started?” I’ll let you guys in on what I did. I did not complete a PhD. My M.S degree wasn’t data science focused either. I went from a failed pre-medical student to a full-time machine learning engineer in about 2 years. I want to make one thing clear though. If you don’t know basic calculus, probability, or programming, that is fine. But you WILL have to learn it eventually.
The resources on the internet are endless, so learning your first programming language and calculus concept are not what this page is about. I will assume you have the dedication and the grit to get through the prerequisites so you can hop on board as soon as possible. You don’t need to master math or even be good at it; just know the basics and you’ll get better the more you integrate yourself into your data science role.
The following post will highlight the specific steps I took and I highly encourage you to do the same. Click Here to Read!
See you there!