Overview

Transform theoretical knowledge into practical expertise in this comprehensive project-based course designed for aspiring data professionals. Through an end-to-end project using synthetic customer support data (designed to mirror real-world scenarios) , you'll integrate advanced analytics, cloud computing, and AI-assisted development to solve authentic business challenges. Leveraging AWS services throughout the project, you'll work with S3 for data storage and management, utilize SageMaker for model development and deployment, and create automated data pipelines—gaining hands-on experience with industry-standard cloud tools. Upon completion, you'll be able to: • Design and implement end-to-end data science solutions • Build automated data pipelines with AWS integration • Create production-ready machine learning models • Develop interactive dashboards and reports • Generate comprehensive project documentation

Syllabus

Planning and Data Collection

Welcome to the foundation of building real-world data science solutions, where business understanding meets technical implementation. In this essential first module, you'll learn to bridge the gap between business challenges and data science solutions while mastering the fundamental AWS services needed for scalable implementations. Working with TicketWise's support ticket routing challenge, you'll learn to analyze business requirements, configure cloud environments, and establish the data management infrastructure that will support your end-to-end solution. Through hands-on experience with AWS S3 and Python integration, you'll develop the crucial skills needed to transform business problems into well-structured data science projects.

Exploratory Data Analysis (EDA) and Preprocessing

Discover how to transform raw support ticket data into actionable insights. In this module, you'll analyze TicketWise's ticket patterns and prepare data for modeling success. Through exploratory data analysis and systematic preprocessing, you'll uncover key insights about resolution times, customer segments, and routing patterns while ensuring data quality. Using Python libraries and AWS integration, you'll create a clean, well-structured dataset that will form the foundation of your routing solution.

Model Development and Evaluation

Ready to turn your prepared data into predictive power? In this module, you'll build and evaluate machine learning models that automatically route TicketWise's support tickets. Through feature engineering, model development, and systematic evaluation, you'll create a solution that makes intelligent routing decisions. Using both traditional techniques and AI assistance, you'll learn to select the right models, measure their effectiveness, and document your approach for production deployment.

Pipeline Construction and Deployment

From automated pipelines to clear documentation, this module transforms individual ML components into a production-ready system. Using TicketWise's support ticket routing solution as a practical example, you'll learn to build automated data pipelines, deploy models in AWS SageMaker, create insightful visualizations, and generate comprehensive documentation. Through hands-on labs and real-world scenarios, you'll master the skills needed to turn promising models into valuable business solutions, using both traditional techniques and AI assistance to ensure your work is scalable, maintainable, and well-documented.

Solution Integration and Delivery

In this culminating module, you'll demonstrate your mastery of end-to-end data science solutions. Through component integration scenarios and a comprehensive final assessment, you'll show how different tools and techniques work together effectively. Using TicketWise's support ticket routing system as context, you'll showcase your ability to design integrated solutions while considering business impact. Through guided reflection, you'll also identify growth opportunities and prepare for your next steps as a data science professional.