Скачать или смотреть HOW IS TEAMWORK organized in Data Science PROJECTS?

Hi there, IT professionals!

In this educational video, I’ll walk you through how teamwork is structured in Data Science projects. If you're just starting your journey in the data field and want to understand how to work effectively in a team, this video is for you!

In this video, I’ll cover:

Project Structure :
👉🏻Input : Raw data, requirements, and tasks.
👉🏻Process : Data analysis, modeling, testing, and optimization.
👉🏻Output : Final models, reports, and insights.

Illustrated with clear diagrams and schematics to help you easily remember the key stages.

Key Tools :
👉🏻Git : Version control system for managing code and facilitating collaboration.
👉🏻Jira : Project management tool for tracking tasks and progress.
👉🏻Data Storage (DataBricks) : Repository for raw and processed data.
👉🏻Model Storage (MLFlow) : System for managing and tracking machine learning models.
👉🏻Deployment Automation Tools (CICD) : Jenkins and GitHub Actions for automating the deployment of your product.

Frequency Indicators :
I’ll also show how often various aspects of teamwork come up using a star rating system. This will help you focus on what’s most important.

This video is perfect for anyone looking to better understand how a Data Science team operates and which tools are essential for successful project execution. Don’t forget to subscribe to the channel and hit that like button if you found the content helpful!

Let’s take the first step together toward a successful career in Data Science!

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