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

What you’ll learn
Understand CRISP-DM: Grasp each step of the Cross-Industry Standard Process for Data Mining (CRISP-DM) and its relevance in real-world data science projects.
Translate Business Problems into Data Science Objectives: Learn to transform business questions into data science goals and define measurable success criteria.
Data Collection, Exploration, and Cleaning: Acquire skills in data gathering, exploratory data analysis (EDA), and data cleaning techniques in Python.
Feature Engineering and Selection: Selecting features that improve model accuracy and efficiency.
Choosing and Training Machine Learning Models: Understand how to select, train, and evaluate various machine learning models using Python libraries.
Model Evaluation and Validation: Learn methods for model evaluation, validation, and optimization to ensure robust and reliable performance.
Working with Python and Key Libraries: Python libraries for data science, including Pandas, NumPy, Scikit-Learn, and more.
Problem Solving with a Real-World Case Study: Work through a full project, applying each CRISP-DM phase to solve a real-world data science problem.

Requirements
The student must be familiar with the fundamentals of Python programming language.
Familiarrity with Machine Learning, knowing the concepts of training set, test set, predictor variables, target, model.

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!

Who this course is for
Aspiring Data Scientists and Analysts: Individuals new to data science who want a structured, end-to-end approach to real-world projects, covering essential skills from problem definition to deployment.
Working Professionals in Data-Driven Fields: Analysts, business intelligence professionals, and other data-focused roles looking to deepen their project management skills and learn CRISP-DM for structured data science workflows.
Students and Recent Graduates in STEM: STEM students and recent grads aiming to build practical data science experience by following a hands-on, guided project lifecycle using Python and industry-standard practices.

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