4 Skills You Need to Be a Full-Stack Data Scientist

Summary

This video introduces the concept of a full stack data scientist, defined as an individual capable of managing and implementing machine learning solutions from start to finish. It discusses the four key roles or 'hats' a full stack data scientist assumes: project manager, data engineer, data scientist, and machine learning engineer. The video emphasizes the importance of understanding the entire ML workflow and the value of learning the full tech stack, especially for freelancers or those in early-stage companies. It outlines the skills required for each role and offers a personal approach to becoming a full stack data scientist, advocating for learning on a need-to-know basis and prioritizing simplicity in solution building.

Takeaways

• 🧑‍💻 A full stack data scientist is defined as someone who can manage and implement machine learning (ML) solutions from end to end, having a comprehensive understanding of the entire ML workflow.
• 🔧 The ML workflow typically includes diagnosing business problems, designing ML solutions, sourcing and preparing data, developing the solution (training the ML model), and deploying the solution (integrating the model into workflows or products).
• 🚀 The value of learning the entire tech stack has become more evident, especially for freelancers dealing with small to medium-sized businesses that may lack a data science function or infrastructure.
• 🔑 The 'four hats' of a full stack data scientist include the project manager, data engineer, data scientist, and ML engineer, each corresponding to key parts of the ML workflow.
• 🗣️ The project manager's role is to answer the questions 'what', 'why', and 'how' regarding the project, emphasizing the importance of clear communication and relationship management.
• 🔍 The data engineer's role focuses on making data readily available for model development, which includes building data pipelines, ETL processes, and data monitoring.
• 📈 The data scientist's role involves leveraging data to drive impact, which includes model training and evaluation, with a focus on meaningful performance metrics tied to business impact.
• 🛠️ The ML engineer's role is to turn the ML model into a solution by containerizing it, adding an API for external communication, and potentially orchestrating more complex solutions with tools like Airflow.
• 💡 Becoming a full stack data scientist involves learning just enough to solve the problem at hand and keeping things as simple as possible, rather than mastering every detail of the tech stack.
• 📚 The speaker suggests a bottom-up approach to learning, where one learns new skills as problems arise, and emphasizes the importance of having a reason to learn new skills, such as through personal projects or freelancing.
• 🔬 The video is part of a series where the speaker will implement an ML project end-to-end, demonstrating each of the 'four hats' in action, starting with project management and culminating in ML engineering.

Q & A

What is a full stack data scientist according to the video?

A full stack data scientist is an individual who can manage and implement machine learning solutions from end to end, possessing a comprehensive understanding of the entire machine learning workflow.

What are the four hats of a full stack data scientist mentioned in the video?

The four hats are the project manager, the data engineer, the data scientist, and the machine learning (ML) engineer, each corresponding to key parts of the machine learning workflow.

Why is the role of a project manager important in the context of data science?

The project manager's role is crucial for diagnosing problems, designing solutions, and ensuring that the project addresses the right problem in an efficient and cost-effective manner, which can save time and effort.

What are some key skills for the data engineer hat in full stack data science?

Key skills include Python for tasks like ETL processes, knowledge of SQL for database interactions, understanding command line tools for automation, and familiarity with cloud platforms like AWS, GCP, or Azure for data storage and processing.

What does the data scientist hat involve in terms of machine learning model development?

The data scientist hat involves leveraging data regularities to drive impact, which typically includes training machine learning models, evaluating their performance, and iterating on the model development process based on feedback and results.

How does the ML engineer hat contribute to turning a machine learning model into a solution?

The ML engineer hat involves deploying the machine learning model by containerizing it, adding an API for external communication, and potentially setting up automated model retraining and monitoring systems.

What is the importance of simplicity in building machine learning solutions according to the video?

Simplicity is important because it helps avoid overcomplicating the project with too many tools, technologies, and best practices, allowing for a more straightforward and effective implementation of machine learning solutions.

What are the three principles the video suggests for becoming a full stack data scientist?

The three principles are having a reason to learn new skills, learning just enough to be dangerous (i.e., enough to solve the problem at hand), and keeping things as simple as possible.

How does the video propose learning the full tech stack in the context of full stack data science?

The video suggests a bottom-up approach where one learns just enough to implement their particular solution, focusing on addressing problems as they arise rather than trying to master every aspect of the tech stack upfront.

What is the significance of the iterative and experimental nature of data science mentioned in the video?

The iterative and experimental nature of data science is significant because it allows for continuous improvement of models based on feedback, making the process more of an art than a science and requiring adaptability and creativity.

Can you provide an example of a project that the video creator plans to implement as part of their learning process?

The video creator plans to implement a semantic search system that allows people to search across all of their YouTube videos, demonstrating the full stack data science process from project management to deployment.