Welcome to our Data Science Workflow series! In this short video we explain the data science workflow in the real world. From data collection to implementation, learn how each step is critical to turning raw data into actionable insights. This video is perfect for anyone who wants to understand the practical applications of data science.

Main points covered:

Data collection: collecting raw data from various sources.
Data cleaning: preparing the data by dealing with missing values, outliers and inconsistencies.
Data Exploration: Performing exploratory data analysis (EDA) to understand the characteristics of the data.
Data modeling: Building and training machine learning models.
Model evaluation: assessing model performance using appropriate metrics.
Model implementation: Implementation of the model in a production environment.
Monitoring and maintenance: Continuously monitor model performance and make necessary updates.
Stay tuned for more videos in this series, and don't forget to like, comment, and subscribe for more data science insights!

Please take the opportunity to connect and share this video with your friends and family if you find it helpful.