Data Science Methodology
Master the Step-by-Step Process for Developing and Deploying AI Models
 
👉 Start learning today and gain a competitive edge in AI!
 
Course Overview
With the explosive growth in unstructured data, the opportunity to design, develop, and deploy AI models has never been greater. While many courses teach data science concepts, very few provide a structured, step-by-step methodology for selecting a problem, exploring data, developing models, deploying them, and continuously improving them through user feedback and learning.
The "Data Science Methodology" course offers a comprehensive framework tailored for AI model development and deployment. The course adapts and enhances the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to address the complexities of AI and big data. These modifications have been tested and refined on real-world, large-scale AI projects, giving you the practical skills and confidence to apply them in your own work.
Through hands-on case studies and practical insights, you will gain a deep understanding of the data science lifecycle—from data preparation and model training to monitoring and governance. By the end of the course, you’ll be prepared to build, deploy, and manage AI models effectively in a business environment.
 
Key Learning Objectives
By the end of this course, you will be able to:
✅ Articulate the data science process and methodology – Understand the modified CRISP-DM framework for AI and big data.
✅ Conduct feature engineering and data analysis – Learn how to evaluate data sources and prepare data for model training.
✅ Develop and integrate AI models – Understand how to build, test, and refine AI-driven models using structured and unstructured data.
✅ Engage stakeholders for model monitoring – Explore how to measure model performance and improve it using user feedback and expert insights.
✅ Implement AI governance and controls – Establish best practices for ensuring the security, fairness, and compliance of AI models.
 
Who Should Attend
This course is ideal for:
✔️ Data scientists and AI developers seeking to improve model deployment strategies.
✔️ Business analysts and consultants working with AI and machine learning projects.
✔️ IT and data professionals looking to develop practical AI solutions.
✔️ AI project managers overseeing AI model development and deployment.
✔️ Business leaders and strategists seeking to implement AI-driven decision-making.
 
Prerequisites
To enroll in this course, you should have:
Basic understanding of AI and machine learning concepts.
Familiarity with data analysis and basic statistics.
An analytical mindset and a willingness to learn.
 
Expected Outcome
Upon successful completion of the course, you will:
✅ Gain a deep understanding of the data science lifecycle and AI development process.
✅ Be able to analyze data sources, conduct feature engineering, and prepare data for AI modeling.
✅ Develop and deploy AI models using a structured, repeatable process.
✅ Implement model monitoring and improvement techniques using user feedback.
✅ Establish AI governance practices to ensure ethical, secure, and compliant model performance.
✅ Earn a recognized certification in Data Science Methodology from the Applied AI Institute (AAII).