Here are 12 skills that you wanna add to your Data Science / Machine Learning resume.
The first 6 are foundational and important. The other 6 are in crazy high demand, harder to build, and will set you apart.
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The industry is relatively young so we are still figuring out titles and requirements, but certain skills are already surfacing as fundamental.
Here I compiled twelve of them.
They aren't all required. They just represent a good blueprint for you to focus on.
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1⃣ Notions of Probabilities and Statistics — You need at least enough to understand how some algorithms work and how to interpret their results.
2⃣ Data Management — Capturing, querying, storing, and transferring data. SQL is a very important skill here.
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3⃣ Data Wrangling — Preparing, cleaning, transforming the data for further analysis. This is one of the most important skills to build.
4⃣ Data Visualization — Usually an underrated skill. Your data is telling a story, and it's your job to present it to the world.
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5⃣ Programming — It's imperative that you know enough to draw insights from data using your language of choice.
6⃣ Machine Learning Algorithms — Understanding existing algorithms, and having the capability to apply them and interpret their results is key.
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Most people check these six skills.
But you aren't most, so here you have a list with the other six.
These are sexier but harder to build. These will set your resume apart.
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1⃣ Deep Learning — A subset of Machine Learning methods based on Neural Networks.
2⃣ Computer Vision and Natural Language Processing — These are probably the two hottest areas in the industry right now. They are about extracting meaning from images, videos, and text.
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3⃣ TensorFlow, Keras, PyTorch — These are the most popular libraries to build Deep Learning applications.
4⃣ Cloud Computing — Today, there's no Machine Learning without having access to the resources and services provided by the Cloud.
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5⃣ Big Data — The ability to deal with large and complex data sets. Tools like Hadoop and BigQuery are examples here.
6⃣ DevOps / MLOps — These skills are centered around the ability to build and manage machine learning pipelines and workflows.
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It's really difficult to acquire all of these skills and be good at every single one of them.
But you don't need that.
Instead, focus on the basics and expand your capabilities into areas that will increase your value.
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1. Mojo 🔥 went open-source 2. Claude 3 beats GPT-4 3. $100B supercomputer from MSFT and OpenAI 4. Andrew Ng and Harrison Chase discussed AI Agents 5. Karpathy talked about the future of AI
...
And more.
Here is everything that will keep you up at night:
Mojo 🔥, the programming language that turns Python into a beast, went open-source.
This is a huge step and great news for the Python and AI communities!
With Mojo 🔥 you can write Python code or scale all the way down to metal code. It's fast!
The best real-life Machine Learning program out there:
"I have seen hundreds of courses; this is the best material and depth of knowledge I've seen."
That's what a professional Software Engineer finishing my program said during class. This is the real deal.
I teach a hard-core live class. It's the best program to learn about building production Machine Learning systems.
But it's not a $9.99 online course. It's not about videos or a bunch of tutorials you can read.
This program is different.
It's 14 hours of live sessions where you interact with me, like in any other classroom. It's tough, with 30 quizzes and 30 coding assignments.
Online courses can't compete with that.
I'll teach you pragmatic Machine Learning for Engineers. This is the type of knowledge every company wants to have.
The program's next iteration (Cohort #8) starts on November 6th. The following (Cohort #9) on December 4th.
It will be different from any other class you've ever taken. It will be tough. It will be fun. It's the closest thing to sitting in a classroom.
And for the first time, the next iteration includes an additional 9 hours of pre-recorded materials to help you as much as possible!
You'll learn about Machine Learning in the real world. You'll learn to train, tune, evaluate, register, deploy, and monitor models. You'll learn how to build a system that continually learns and how to test it in production.
You'll get unlimited access to me and the entire community. I'll help you through the course, answer your questions, and help with your code.
You get lifetime access to all past and future sessions. You get access to every course I've created for free. You get access to recordings, job offers, and many people doing the job you want to do.
No monthly payments. Ever.
The link to join is in the attached image and in the following tweet.
The link to join the program:
The cost to join is $385.
November and December are the last two iterations remaining at that price. The cost will go up starting in January 2024.
Today, there are around 800 professionals in the community.ml.school
Live sessions and recordings:
Sessions are live, and I recommend every student to attend if they can.
But we also record every session, and you get access to the recordings. You can watch them whenever you want.
We also have 2 office hours. They are optional but a lot of fun!