CRISP-ML(Q)-Data Pre-processing Using Python(2025)

👩‍💻 Course Title: CRISP-ML(Q) Data Pre-processing Using Python (2023) TDMG's Data Science - Data Pre-processing Using Python course is a comprehensive guide for anyone looking to master the art of data pre-processing within the framework of CRISP-DM (Cross-Industry Standard Process for Data Mining). This course will equip you with the skills to approach data science projects methodically, ensuring that you can deliver high-quality insights that align with business objectives and constraints.

🎓 Course Description:

Understanding Business Problems: Start your journey by learning how to understand the complexities of a business problem. This course will guide you through defining project objectives, establishing constraints, and setting success criteria that encompass both business, machine learning, and economic perspectives.

• Project Charter Creation: Begin every project on a strong foundation with the creation of a Project Charter, the first document essential to any project management approach.
• Data Types & Measures: Gain insight into various data types and the four key measures of data that are crucial for effective data collection and analysis.
• Data Collection Mechanisms: Learn about both primary and secondary data collection methods, including surveys and experiments, and how they contribute to the quality and relevance of your dataset.

Exploratory Data Analysis (EDA): Dive into the world of Exploratory Data Analysis (EDA) to understand the '4' moments of business and how to visualize these through graphical representations.

• Business Moments Visualization: Master the art of representing business moments using univariate, bivariate, and multivariate plots.
• Graphical Representations: Get hands-on experience with creating box plots, histograms, scatter plots, and Q-Q plots to visualize your data effectively.

Data Pre-processing Techniques in Python: The core of this course lies in teaching you the essential pre-processing techniques using Python, ensuring that your datasets are clean, structured, and ready for model building.

• Outlier Analysis & Imputation: Learn to detect outliers and handle missing data with robust imputation methods.
• Scaling Techniques: Understand various scaling techniques like normalization and standardization to prepare your data for ML models.
• Practical Datasets: Work with real-world datasets to apply the pre-processing techniques you've learned, gaining practical experience that will translate directly to your data science projects.

By the end of this course, you'll be well-versed in the CRISP-ML(Q) methodology for data pre-processing, ready to tackle real-world business problems using Python with confidence and precision. 🚀

📅 Why Enroll Now?

• Industry-Relevant Skills: Stay ahead of the curve by mastering a skill set in high demand across various industries.
• Hands-On Learning: Apply theoretical concepts to real datasets, ensuring you can hit the ground running on your own projects.
• Expert Instructors: Learn from industry experts who bring real-world experience and insights into the classroom.
• Community Engagement: Join a community of like-minded professionals and engage in discussions, share knowledge, and build connections.

Enroll now and transform the way you approach data pre-processing in your data science projects! 💻✨