Interactive learning to help you advance your skills and knowledge for smart and advanced manufacturing.
IMPEL Stands For:
Integrative Manufacturing and Production Engineering Curricula That Leverage Data Science. These courses were designed with you in mind: A completely online, modular data science curricula to help you advance your skills and knowledge for Industry 4.0
Program Course Offerings
Data Analytics
Sensor Analytics
Algorithms and Optimization
Computation and Visualization
Cyber-manufacturing Systems
Data Management for Analytics
Robotics and Automation
Ethics, Privacy and Cybersecurity
Personalize Your Path:
Tailor the curricula to meet your individual needs by accessing the course-module recommendation system. You’ll be provided with the right set of courses/modules taking into consideration your aptitude, competency, and workplace needs.
Learn More About Our Course Offerings!
Data Analytics
The course introduces data mining concepts, data pre-processing techniques, data visualization tools, dimension reduction methods, and machine learning models for discovering knowledge from large data sets that occur in real-world applications including manufacturing and production. The concepts and methods are illustrated with examples and learning is reinforced by interactive check-your-knowledge activities.
Sensor Analytics
This course introduces the fundamentals of measurement systems including different types of sensors and physical principles. The course also provides a solid theoretical foundation for analyzing and processing sensor measurements in time domain, frequency domain, and time-frequency and various data analytics methods for sensor data in various purposes and applications.
Algorithms and Optimization
This course introduces algorithms and their uses in industry. A general overview of algorithms is followed by an introduction to operations research, linear programming, graphs and graph algorithms, greedy algorithms, dynamic programming algorithms, network flow algorithms, and computational intractability, and ends with an exploration of metaheuristic algorithms.
Course format: Concepts are illustrated with interactive examples. Videos demonstrate methods of programming Python code to work with algorithms. Learning is reinforced by knowledge-check questions and reflections.
Data Management for Analytics
This course introduces the fundamental concepts and emerging technologies in database design and modeling, database systems, data storage and data governance. It presents a balanced theory-practice focus and covers entity relationship model and UML model, relational model, relational databases, Structured Query Language, and two flavors of NoSQL databases in MongoDB and Neo4j graph database.
Ethics, Privacy and Cybersecurity
This course consists of two modules that introduce ethics, both in the context of online learning and more specifically in application to data science and artificial intelligence. The first module reviews facets of ethical behavior in an online learning environment and various measures to enhance privacy and cybersecurity when using online learning platforms. The second module examines best practices for data handling and interpretation in each stage of data science and artificial intelligence projects, including the basis for understanding and implementing algorithmic fairness for societal benefit.
Computation and Visualization
This course covers the basics of data wrangling and data visualization. It provides students the opportunity to read, clean, select, and manipulate structured data.
It also introduces students to visualization charts and techniques that reveal information, patterns, interactions, and comparisons, combining theory and hands-on activities that allow students to extract meaningful insights from the data.
Cyber-manufacturing Systems
This course introduces the foundational concepts of industrial automation and its layered structure from the physical plant floor to institutional planning platforms. In order to understand the backbone of the system, networking concepts are also discussed based on real-life case studies. Interactive animations, short reflection questions, and higher-level quiz questions are utilized to enforce the learning.
Robotics and Automation
(This is a Placeholder Description) The course introduces data mining concepts, data pre-processing techniques, data visualization tools, dimension reduction methods, and machine learning models for discovering knowledge from large data sets that occur in real-world applications including manufacturing and production. The concepts and methods are illustrated with examples and learning is reinforced by interactive check-your-knowledge activities.
