Learning Outcomes

• Data integration basics - What is data integration and why it's important in the modern business environment
• How data integration skills can create more success in your non-technical career
• Why data integration is a great way to transition into a technology career
• Learn common approaches to Business-to-Business (B2B) integration including EDI, XML, JSON, Text files, FTP and AS2
• Gain a detailed understanding of Application Integration technologies including HTTP, SOAP, REST APIs, and Webhooks
• Learn about Database Integration techniques like data warehousing, data lakes Extract-Transform-Load (ETL), and replication

Learning Content

• Welcome to the Course!2 lectures • 7min
• What is Data Integration?
• Data Integration and Your Career
• Busines to Business Integration (B2B)
• Application Integration
• Database Integration
• Additional Content

Learning Outcomes

• Workflow of Machine Learning projects
• AI terminology
• Workflow of Data Science projects
• AI strategy

Learning Content

• Module 1: What is AI?
• Module 2: Building AI Projects
• Module 3: Building AI In Your Company
• Module 4: AI and Society

Learning Outcomes

• Identify stakeholders and key components imperative to an analytics project plan
• Visually identify, monitor, and remove process variation
• Explain how to create a compelling data story
• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Learning Content

• Data Analysis Software Tools
  • Module 1: This module provides an overview of the tools needed for data analysis.
• Statistical Process Control (SPC)
  • Module 2: This module covers SPC, a way to analyze variation over time in your process using data. It is helpful in identifying current problems and can also be used to monitor the process for any deviations once the process is 'in control'.
• Data Visualization and Translation
  • Module 3: This module provides tools for leveraging data through visualization and translation.
• Project: Data Analysis and Visualization
  • Module 4: This module provides an opportunity to bridge theory and practice. Learners apply knowledge from this course to solve a business problem.

Learning Outcomes

• Automatically extract information from images
• Detect and track objects in images and videos
• Apply the full deep-learning workflow to computer vision challenges
• Export models to common formats like Tensorflow or PyTorch

Learning Content

• Introduction to Image Processing
  • Perform analysis on a variety of common image datatypes
  • Detect objects and regions of interest using segmentation
  • Complete a project analyzing Antarctic ice melt in satellite images
• Image Segmentation, Filtering, and Region Analysis
  • Use segmentation to detect and analyze regions of interest
  • Segment and analyze 3D images, such as MRI images
  • Use interactive tools to test segmentation approaches
• Automating Image Processing
  • Apply image processing algorithms to large datasets
  • Analyze video files and calculate statistics
  • Complete a specialization-level project detecting cars in noisy videos
• Introduction to Computer Vision
  • Use algorithms for feature detection, extraction, and matching
  • Perform image registration and stitching
  • Complete a project stitching images from NASA’s Mars Rover
• Machine Learning for Computer Vision
  • Train and evaluate models for image classification
  • Train object detection machine learning models
  • Customize model training using cost matrices
• Object Tracking and Motion Detection
  • Use pre-trained neural networks like YOLO for object detection
  • Detect motion and perform multi-object tracking
• Introduction to Deep Learning for Computer Vision
  • Retrain common models like GoogLeNet and ResNet
  • Investigate model behavior and improve performance
  • Complete a real-world project practicing the deep learning workflow
• Deep Learning for Object Detection
  • Retrain YOLO models for specific applications
  • Evaluate models by examining class and location accuracy
• Advanced Deep Learning Techniques
  • Train specialized anomaly detection models
  • Generate synthetic training images
  • Use AI-assisted auto-labeling to save time and effort

Learning Outcomes

• Understand the basics of digital twins, digital twins platform and ecosystem
• Learn the implementation of digital twins in manufacturing, the corresponding business values, and risks
• Get to know the future trends of digital twins and digital threads
• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool

Learning Content

• What is Digital Twins?
  • Learn the basics behind this technology
  • Describe the applications and uses for digital twins within a manufacturing setting
• Digital Twins Platform, Ecosystem, and Business Context
  • Address the digital twin platform ecosystem
  • Understand the business context and advantages of digital twins
  • Review risks and challenges surrounding this technology
• Future Trends and Summary
  • Learn about the forecast of future trends for digital twins
  • Explore the related concept of digital threads
  • Work through a case project for your final assessment