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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If you have a list of things you've always wanted to solve, let an agent do them:
• Refactor code and ensure tests still run
• Find and fix bugs
• Close open tickets from your backlog
• Update documentation
• Write tests for untested code
• You can use it with any of the major models (GPT-X, Gemini, Claude)
• It has an option to Chat and Edit with the model
• It has an Agent mode to make changes to the notebook autonomously
Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them.
How this works:
1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents.
2. Graphiti connects to Neo4j running locally.
Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project.
Huge!
Here is the video I recorded.
To get this working on your computer, follow the instructions on this link:
Something super cool about using Graphiti's MCP server:
You can use one model to develop the requirements and a completely different model to implement the code. This is a huge plus because you could use the stronger model at each stage.
Also, Graphiti supports custom entities, which you can use when running the MCP server.
You can use these custom entities to structure and recall domain-specific information, which will tenfold the accuracy of your results.
First, MCP. Then, A2A. Now, we have a new AI protocol.
AG-UI is the Agent-User Interaction Protocol. This is a protocol for building user-facing AI agents. It's a bridge between a backend AI agent and a full-stack application.
Up to this point, most agents are backend automators: form-fillers, summarizers, and schedulers. They are useful as backend tools.
But, interactive agents like Cursor can bring agents to a whole new set of domains, and have been extremely hard to build.
But not anymore!
If you want to build an agent that co-works with users, you need:
It’s a lightweight, event-streaming protocol (over HTTP/SSE/webhooks) that creates a unified pipe between your agent backend (OpenAI, Ollama, LangGraph, custom code) and your frontend.
Here is how it works:
• Client sends a POST request to the agent endpoint
• Then listens to a unified event stream over HTTP
• Each event includes a type and a minimal payload
• Agents emit events in real-time
• The frontend can react immediately to these events
• The frontend emits events and context back to the agent