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Develop job-relevant skills with hands-on projects
Earn a shareable career certificate
There are 5 modules in this course
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
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.
What's included
3 videos7 readings1 assignment1 ungraded lab
Show info about module content
3 videos•Total 7 minutes
Welcome to Building a Real World Data Science Solution •2 minutes
Defining a Data Science Problem•2 minutes
Setting Up S3, Glue and Athena for Data Analysis•3 minutes
7 readings•Total 80 minutes
Course Syllabus & Roadmap•10 minutes
Companies and Datasets Overview•10 minutes
Video Transcript Access•10 minutes
Understanding TicketWise's Ticket Routing Business Problem•10 minutes
Best Practices in Problem Definition and Success Metrics•15 minutes
Introduction to Amazon SageMaker AI: Overview and Getting Started•10 minutes
AWS S3 Integration With Python•15 minutes
1 assignment•Total 60 minutes
Assessment 1: Solution Setup and AWS Configuration•60 minutes
1 ungraded lab•Total 60 minutes
Ungraded Lab: AWS S3 Configuration Lab•60 minutes
Exploratory Data Analysis (EDA) and Preprocessing
Module 2•5 hours to complete
Module details
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.
What's included
2 videos1 reading3 assignments2 ungraded labs
Show info about module content
2 videos•Total 11 minutes
First Look: Smart EDA for HR Data •7 minutes
Data Cleaning & Transformation Techniques•3 minutes
1 reading•Total 30 minutes
Exploratory Data Analysis Techniques•30 minutes
3 assignments•Total 120 minutes
Knowledge Check: EDA Concepts •30 minutes
Knowledge Check: Data Preprocessing•30 minutes
Assessment 2: Data Analysis and Preprocessing•60 minutes
2 ungraded labs•Total 120 minutes
Ungraded Lab: Guided EDA Exercises Lab•60 minutes
Ungraded Lab: Data Preprocessing Lab•60 minutes
Model Development and Evaluation
Module 3•5 hours to complete
Module details
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.
What's included
6 videos2 readings2 assignments2 ungraded labs
Show info about module content
6 videos•Total 36 minutes
Enhancing Your Features With AI•8 minutes
Feature Selection•5 minutes
Building Classification Models•6 minutes
Regression Techniques in Python•7 minutes
Time Series Analysis in Python•7 minutes
Clustering and PCA Techniques•4 minutes
2 readings•Total 60 minutes
AI‑Generated Feature Suggestions•30 minutes
Model Recap and Technique Overview•30 minutes
2 assignments•Total 90 minutes
Knowledge Check: Model Evaluation•30 minutes
Assessment 3: Model Building and Evaluation•60 minutes
2 ungraded labs•Total 120 minutes
Ungraded Lab: Feature Engineering Lab•60 minutes
Ungraded Lab: Model building Lab•60 minutes
Pipeline Construction and Deployment
Module 4•8 hours to complete
Module details
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.
What's included
6 videos5 readings2 assignments4 ungraded labs
Show info about module content
6 videos•Total 28 minutes
Automating Your Data Pipeline•5 minutes
Real-Time Model Deployment in SageMaker AI•6 minutes
Designing Interactive Dashboards•6 minutes
Crafting Data Stories With BI Tools•4 minutes
Day in the Life - An Interview With an Expert•5 minutes
Documenting Your Data Science Solution•3 minutes
5 readings•Total 135 minutes
Step‑by‑Step Pipeline Guide•30 minutes
SageMaker Case Study•30 minutes
Model Deployment Basics With SageMaker AI•30 minutes
Narrative Visualization Techniques•30 minutes
Best Practices in Solution Documentation•15 minutes
2 assignments•Total 90 minutes
Knowledge Check: Pipeline Components•30 minutes
Assessment 4: Pipeline and Deployment•60 minutes
4 ungraded labs•Total 240 minutes
Ungraded Lab: Pipeline Lab•60 minutes
Ungraded Lab: End-to-End ML Workflow with SageMaker•60 minutes
Ungraded Lab: Tableau Public Visualization Lab•60 minutes
Ungraded Lab: Documentation Lab•60 minutes
Solution Integration and Delivery
Module 5•2 hours to complete
Module details
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.
What's included
2 videos2 readings2 assignments
Show info about module content
2 videos•Total 4 minutes
Your Data Science Toolkit in Action•3 minutes
Congratulations on Completing Your Certificate!•1 minute
2 readings•Total 20 minutes
Mapping Your Data Science Toolkit to Business Solutions•10 minutes
Course Wrap-Up: Building Real-World Data Science Solutions•10 minutes
2 assignments•Total 90 minutes
Knowledge Check: Building End-to-End Solutions•30 minutes
Final Assessment: Complete Solution Delivery•60 minutes
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