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Microsoft Professional Programs

Descriptions and credit recommendations for all evaluated learning experiences

Location:

Various; distance learning format.

Length:

Varies, self-paced.

Dates:

April 2018 - Present.

Objectives:

Upon successful completion of the certificate program, students will be able to: describe how machine learning provides the foundation for artificial intelligence; leverage cognitive services in developing applications; build and derive insights using machine learning models in problem spaces such as classification, regression, and clustering; solve image classification problem using deep learning models such as convolution neural network (CNN); forecast time series data and process sequential data using deep learning models such as recurrent neural network (RNN) and long short term memory (LSTM); frame reinforcement learning problems and solve using algorithms such as dynamic programming, temporal difference learning, deep ‘Q’ learning, policy gradient and actor-critic; apply classical image analysis techniques, such as  edge detection,  watershed and distance transformation as well as K-means clustering to segment a basic dataset; apply deep learning models to solve machine translation and conversation problems; and compete and solve a real-world artificial intelligence challenge. 

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: artificial intelligence, Python, data science, essential math, ethics and law in data and analytics, research methods, natural language processing, and machine learning. Skills are measured through formative and summative assessments including a comprehensive capstone project. 

Credit recommendation:

In the lower division baccalaureate/associate degree category, 3 semester hours as Artificial Intelligence, Information Systems or as a technical elective (10/18).

Location:

Various; distance learning format.

Length:

Varies, self-paced.

Dates:

April 2018 – Present.

Objectives:

Upon successful completion of the course, students will be able to: analyze and visualize data; work with non-relational data; query relational data, create a data warehouse; process big data at rest; process big data in motion; orchestrate big data solutions; and build big data analysis solutions. 

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: introduction to big data, analyzing and visualizing data with Power BI, analyzing and visualizing data with Excel, introduction to No SQL data solutions, querying relational data with transaction-SQL, delivering a data warehouse in the Cloud, processing big data with Azure Data Lake analytics, processing big data with Azure HDInsight, processing real-time data streams in Azure, processing real-time data with Azure HDInsight, orchestrating big data with Azure Data Factory, developing big data solutions with Azure Machine Learning Studio, analyzing big data with Microsoft R Server, and implementing predictive analytics with Spark in Azure HDInsight. Skills are measured through formative and summative assessments including a comprehensive capstone project. 

Credit recommendation:

In the upper division baccalaureate degree category, 4 semester hours in Information Technology, Data Science, Data Analytics or as a technical elective (10/18).

Location:

Various; distance learning format.

Length:

Varies, self-paced.

Dates:

April 2018 - Present. 

Objectives:

Upon successful completion of the course, students will be able to: describe the current threat landscape; Identify and prioritize the key enterprise systems that require protection; demonstrate hardening tactics to make a breach more difficult and costly for a hacker to execute; identify breaches early;  classify the extent of a system breach and respond to a security incident effectively, while minimizing business impact; report technical details and business impact of incident to all relevant stakeholders.

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: enterprise security fundamentals; threat detection planning for a secure enterprise; planning a security incident response; power shell security best practices; managing identity; securing data in Azure and SQL server; Microsoft SharePoint 2016 authentication and security; Windows 10 security features; Windows server 2016 security features; Microsoft Azure security services; and Microsoft professional cybersecurity capstone.

Credit recommendation:

In the upper division baccalaureate degree category, 4 semester hours in Cybersecurity (10/18).

Location:

Various; distance learning format.

Length:

Varies, self-paced.

Dates:

April 2018 - Present.

Objectives:

Upon successful completion of the course, students will be able to: get started with data analysis; analyze data using statistics; create basic data visualizations; pivot and manipulate data; query relational data; build descriptive analytics models and visualizations; communicate data insights; apply data analysis methods in context of organizational and industry-specific scenarios.

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  major topics include: data analysis: a practical approach for absolute beginners; essential math for data analysis using Microsoft Excel online; data visualization; a practical approach for absolute beginners; introduction to data analysis using Microsoft Excel; querying data with T-SQL; analyzing and visualizing data with power bi; analyzing and visualizing data with Microsoft Excel; analytics storytelling for impact; applied data analysis: working in organizations and industries; and data analysis capstone.

Credit recommendation:

In the lower division baccalaureate/associate degree category, 3 semester hours in Data Science, Information Science, Business, Applied Mathematics, Organizational Management or Leadership (10/18).

Location:

Various; distance learning format.

Length:

Varies, self-paced.

Dates:

April 2018 - Present. 

Objectives:

Upon successful completion of the course, students will be able to: understand the fundamentals of data science; analyze and visualize data; communicate data insights; apply ethics and law in analytics; query relational data; explore data using code; apply math and statistics to data analysis, plan and conduct data studies; build machine learning models; and build predictive solutions at scale.

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: introduction to data science, analyzing and visualizing data with power Bi, analyzing and visualizing data with Excel, analytics storytelling for impact, ethics and law for data and analytics, querying with transact SQL, introduction to R for data science, introduction to Python for data science, essential math for machine learning: R edition, essential math for machine learning: Python edition, data science research methods: R edition, data science research methods: Python edition, principles of machine learning: R edition, principles of machine learning: Python edition, developing big data solutions with Azure machine learning, analyzing big data with Microsoft R, and implementing predictive analytics with Spark in Azure HD Insight. Skills are measured through formative and summative assessments including a comprehensive capstone project.

Credit recommendation:

In the upper division baccalaureate degree category, 3 semester hours in Introduction to Data Science (10/18).

Location:

Various; distance learning format.

Length:

Varies, self-paced.

Dates:

April 2018 - Present. 

Objectives:

Upon successful completion of the course, students will be able to: describe DevOps practices and principles; implement infrastructure as code; implement continuous integration and continuous deployment; implement configuration management; implement testing in continuous delivery pipelines; integrate databases in continuous delivery environments; monitor applications; and architect apps in DevOps environments.

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: introduction to DevOps practices, infrastructure DevOps as code, continuous integration and continuous deployment, configuration management for containerized delivery, DevOps testing, DevOps databases, application monitoring and feedback loops, DevOps for mobile apps, and architecting distributed cloud applications. Skills are measured through formative and summative assessments including a comprehensive capstone project. 

Credit recommendation:

In the upper division baccalaureate degree category, 3 semester hours in Computer Science, Information Systems, Information Technology, Software Engineering, or Web Design and Development (10/18).

Location:

Various; distance learning.

Length:

Varies, self-paced.

Dates:

April 2018 - Present. 

Objectives:

Upon successful completion of the course, students will be able to: think logically and systematically; create basic programs and functions; process simple data structures and files; develop a simple technical solution; develop interactive webpages; develop and deploy dynamic websites; create advanced programs and functions; create basic object-oriented programs; analyze algorithms and data structures for efficiency; apply software development to real-world scenarios; design user-centric software; and design software for global audience.

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: logic and computational thinking, introduction to Python: absolute beginner, introduction to Python: fundamentals, designing a technical solution, building interactive prototypes using JavaScript, building functional prototypes using Node.JS, learning to program in Java, object oriented programming in Java, algorithms and data structures, writing professional code, introduction to design thinking, and introduction to developing international software. Skills are measured through formative and summative assessments including a comprehensive capstone project. 

Credit recommendation:

In the lower division baccalaureate/associate degree category, 4 semester hours in Introduction to Programming, Introduction to Software Engineering or Information Systems (10/18).

Location:

Various; distance learning format.

Length:

Varies, self-paced.

Dates:

April 2018 - Present.

Objectives:

Upon successful completion of the Internet of Things (IoT) training curriculum, students will be able to:  recognize and define the opportunity for IoT solutions that achieve business goals for top/trending industries; program resource constrained device hardware, breadboard simple electrical circuits, and capture sensor readings; configure and implement secure two-way communications between devices and a cloud gateway; Implement data analytics (live streams and stored data) to inform device management and other actions; construct IoT data visualizations that enable businesses to gain insights related to their operations; apply machine learning to IoT data to facilitate predictive maintenance and improve business services; evaluate business scenarios, design IoT solution architectures, and develop associated business plans; design and implement an IoT solution for a given industry scenario.

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: getting started with IoT; introduction to device programming for IoT; IoT device configuration and communication; IoT data analytics and storage; business intelligence for IoT solutions; predictive analytics for IoT solutions; IoT solution architecture design and business planning; and Microsoft Professional Internet of Things Capstone.

Credit recommendation:

In the lower division baccalaureate/associate degree category, 3 semester hours in Information Technology, Computer Technology, Information Science, or Information Systems (10/18).

Location:

Various; distance learning.

Length:

Varies, self-paced.

Dates:
Objectives:

Upon successful completion of the course, students will be able to: properly support and troubleshoot the most common IT issues in Microsoft Windows and Microsoft Office environments over the telephone, email, chat and social media. In addition to technical skills, candidates will be able to document and escalate a support call and follow common help desk methodology.

Instruction:

Instruction is offered online through edX and through other Microsoft partners.  Major topics include: IT support: fundamentals, communication, troubleshooting, documentation, hardware essentials, networking essentials, Microsoft Windows support essentials: installation, Windows support configuration, Microsoft Windows support maintenance, IT support: troubleshooting Microsoft Windows, IT support: troubleshooting Microsoft Office, and IT support: cloud fundamentals. Skills are measured through formative and summative assessments including a comprehensive capstone project. 

Credit recommendation:

In the lower division baccalaureate/associate degree category, 3 semester hours in Information Technology, Information Systems, Networking, Web Design or a general technical elective (10/18).

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