4 Day Week - Your 4 Day Week Jobs (05 Dec) 🚀

Before we get to the jobs...

Some AI engineers are currently earning $400k+ (!!!) - but how do you start a career in AI?

Well, before I worked on 4 Day Week, I worked as a Data Scientist for ~10 years (the job role that builds AI models, such as ChatGPT).

So here's my tips on how to start your career in Data Science:

1. Learn Python (free course): you need some coding skills as a data scientist - more than a data analyst, but less than a software engineer. If you've never coded before, it can be intimidating... but here's the nice thing:

You only need to learn Python as (almost) all data science = Python. And what's even better? Python is one of the easiest languages to learn as a newbie.

2. Learn SQL (course): SQL is the technology used to work with databases. As a data scientist, you're going to work with a lot of data - so SQL will be very useful (but not 100% necessary).
3. Learn statistics (YouTube): Data Science is the intersection of programming and statistics. My background was Computer Science, so I learned Statistics using...

Youtube! 🥳 Statistics Crash Course was the course I did - highly recommended!

4. Build your first regression model (YouTube series): I hear ya... "what is this regression model you speak of?" Well... you'll find out soon, but the tl;dr is:

A model is just something that simulates a real world scenario in code.

A regression model is a specific type of model that takes input data (i.e.lots of numbers) and predicts a number.

For example, a student exam score regression model could take a University student's attendance rate and study hours and attempt to predict their exam score.

5. Build your first classifier: where regression predicts a number... classifiers predict a class (often just "yes" or "no"). A common 1st classifier to build is where you attempt to predict if someone survived or died on the Titanic - gruesome I know... You'll find this dataset on Kaggle (Oh, you're gonna spend a lot of time on Kaggle).
6. Learn Deep Learning (course): it's all about "deep learning" these days (the technology behind ChatGPT etc) and it's important to learn it... but here's my advice to you:

Only use the simple models first (e.g. linear regression). Don't even Google "pytorch" until you can recite linear regression in your sleep. In every machine learning problem you face: start simple and build your way up.

7. Build more models & pet projects: the best way to learn (anything) is to do. So go to Kaggle, download some data and build some models! p.s. it's pretty fun.
8. Get some certifications: applying to data scientist jobs won't be easy without a degree in CompSci, Stats, Maths or Economics - I'm just being honest. So the next best thing would be to get some online certifications e.g. Amazon Machine Learning Certification ($300) or others (Google, Microsoft, IBM etc)
9. Resume optimisation: make sure your resume is ATS compliant and job role specific e.g. using a tool like Resume.io
10. Start applying: imo start by applying to data analyst roles instead of data scientist. A data analyst does all the the stuff a data scientist does, except the machine learning. Data Analyst roles are easier to get - either that or "junior data scientist".

Hope this helps - all the best! 🤝

To become an AI engineer, you pretty much need to know Python.

So do you want to learn one of the mostly highly paid coding skills?

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