Machine Learning and Industry

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Data Science Discipline:

Machine Learning and Industry

Even if you have an idea for a machine learning solution that could transform your company, implementing it is a whole different challenge. Machine Learning and Industry trains you to transform your organization by embracing the data engineering and machine learning strategies that are driving innovation today. You will learn to develop life cycles for managing projects utilizing machine learning and identify technologies and design architectures for implementing machine learning and intelligence effectively in any industry or organization.

8 weeks
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Continuous Enrollment

Installment payments are now available on all course purchases!

Knowledge Areas

Agile project management
Imperative and declarative languages
Explicit and heuristic algorithms
Database systems
Batch processing
Interactive processing
API standards
Data governance
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8 Modules

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4-6 Work Hours Per Module

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Thomas Miller, Ph.D.

Your Subject Matter Expert:

Thomas Miller, Ph.D.

“The words 'artificial intelligence’ are used by many, and managers need to be intelligent about machine learning and informed about the information systems that make machine learning possible.”

Course Modules

This course provides an overview of software, frameworks and systems used for implementing data science solutions, as well as a technically sound review of key concepts in data engineering. Receive an introduction to key data science algorithms, data structures, databases and information systems, and learn how to select appropriate information technology for batch, interactive and stream processing environments. The course also provides a technically sound review of key concepts in data engineering.

Module 1: Technology Management

Describe the role of the data engineer. Describe information system requirements. Contrast traditional versus agile project management. Summarize the digital transformation of organizations.

Module 2: Language and Systems

Contrast imperative and declarative computer languages. Identify software for machine learning and intelligence. Contrast on-premises and cloud-based systems. Discuss the benefits and limitations of proprietary versus open-source systems.

Module 3: Algorithms and Data Structures

Explain why algorithms matter. Contrast explicit versus heuristic algorithms. Illustrate alternative data structures. Describe the role of algorithms and data structures in object-oriented programming.

Module 4: Database Systems

Recommend methods of data exchange. Design a relational data model. Design a document database model. Design a graph database model.

Module 5: Batch Processing

Define batch processing. Describe hardware and software components of batch processing. Identify machine learning software alternatives for batch processing. Evaluate resource requirements and performance of batch processing systems.

Module 6: Interactive Processing

Define interactive processing. Describe front-end and back-end components of interactive processing. Identify application programming interface (API) standards. Evaluate resource requirements and performance of interactive systems.

Module 7: Stream and Event Processing

Identify event streams. Define the internet of things (IoT). List software alternatives for interactive processing. Evaluate resource requirements and performance of streaming systems.

Module 8: Technology and Human Values

Explain the importance of data governance in designing information systems. Identify issues in cybersecurity and how to approach them. Discuss data encryption and blockchain technologies. Discuss issues in privacy protection.

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Thomas Miller, Ph.D.

Thomas W. Miller is faculty director of the data science program at Northwestern University. He designed distance learning training materials for the program, including courses in advanced modeling techniques, marketing analytics, data engineering and machine learning. During the 2019-20 academic year, he will be teaching artificial intelligence and deep learning, natural language processing, and knowledge engineering. Dr. Miller has published six books about data science with Pearson Education (the series "Modeling Techniques in Predictive Analytics"). He also owns a publishing and consulting firm, Research Publishers LLC, located in Manhattan Beach, California. He provides data science consulting services and is actively involved in developing chatbot and survey research applications. Earlier in his career, Dr. Miller worked as a network engineer for NCR Comten and as a field engineer and account representative for Hewlett-Packard Company. He also directed the A.C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison.

Dr. Miller holds a doctorate in psychology and a master's degree in statistics from the University of Minnesota, as well as an MBA and a master's degree in economics from the University of Oregon.