Thread by @svpino

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.