Hands-On Data Science Project Using CRISP-DM, A practical guide to each phase of the data science lifecycle, from Business Understanding to Deployment in Python.
Course Description
In this hands-on course, you’ll learn how to execute a full data science project using the CRISP-DM framework, an industry-standard approach that guides you from understanding business needs to deploying your final model. Whether you’re new to data science or seeking to expand your skill set, this course provides a practical, end-to-end experience that mirrors real-world project workflows.
Throughout this mini-course, we’ll cover each stage of CRISP-DM in detail, using Python to demonstrate essential techniques in data exploration, feature engineering, model training, and deployment. Starting with Business Understanding, you’ll learn to translate business challenges into actionable data science objectives. Then, we’ll dive into data preparation, exploring methods to clean and analyze data effectively, preparing it for modeling. You’ll work with real datasets and apply feature engineering techniques to make your model more accurate and insightful.
In the Modeling phase, we’ll select, train, and evaluate machine learning algorithms, optimizing them to create a robust solution. You’ll learn validation techniques to ensure your model’s performance and reliability, even in production environments. Finally, in the Deployment phase, we’ll cover how to prepare and deploy your model, so it’s ready for real-world use.
By the end of this course, you’ll have a solid foundation in CRISP-DM and the hands-on experience to confidently approach data science projects in a structured, methodical way. Join us to build real-world data science skills and make an impact with your analyses!
