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Product Analytics and AI
Alex Cowan
Alex Cowan
Few capabilities focus agile like a strong analytics program. Such a program determines where a team should focus from one agile iteration (sprint) to the next. Successful analytics are rarely hard to understand and are often startling in their clarity. In this course, developed at the Darden School of Business at the University of Virginia, you'll learn how to build a strong analytics infrastructure for your team, integrating it with the core of your drive to value.
Register for this course and see more details by visiting:
OpenCourser.com/course/ihamac/agile
What's inside
Syllabus
Introduction and Customer Analytics
Without an actionable view of who your customer is and what problems/jobs/habits they have, you’re operating on a shaky foundation. This week, we’ll look at how to pair your qualitative analytics on customer hypotheses with testable analytics.
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Syllabus
Introduction and Customer Analytics
Without an actionable view of who your customer is and what problems/jobs/habits they have, you’re operating on a shaky foundation. This week, we’ll look at how to pair your qualitative analytics on customer hypotheses with testable analytics.
Read more
Demand Analytics
Why build something no one wants? It seems like an obvious question, yet a lot (probably >50%) of software ends up lightly used or not used at all. This week, we’ll look at how to run fast but definitive experiments to test demand.
UX Analytics
Strong usability most often comes from ongoing diligence as opposed to big redesigns. Teams that do the hard work of consistently testing usability are rewarded with a consistent stream of customer wins and a culture of experimentation that makes work more enjoyable and rewarding.
Analytics and Data Science
The availability of big data and the ascendance of machine learning can supercharge the way you approach analytics. This week, we're going to learn how data science is changing analytics and how you can create a focused, productive interfaces to a data science capability.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a strong foundation for beginners who want to build a data analytics platform for an agile team
Taught by Alex Cowan, who are recognized for their work in analytics and machine learning
Develops skills, knowledge, and tools that are highly relevant to industry in analytics and data science
Requires learners to have a strong understanding of statistics and programming
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Reviews summary
In-depth product analytics for agile development
Learners largely agree that this is a well-received course that provides engaging assignments and thoughtful content. They especially value the insights gained from interviews with industry experts and the emphasis on practical applications of analytics in agile development. While some note that the material can be difficult, most learners believe that the course is well-paced and offers a valuable learning experience.
The course is well-paced and allows learners to absorb the material.
"At times I found it a little bit too quick, but overall very good in my opinion. "
"The course met my expectations"
The course is beginner-friendly and provides a solid foundation in the subject matter.
"For those like me who are new to Agile, the course is quite comprehensive and covers the basic principles in a clear way. I recommend."
"At the beginning I though it was going to be really difficult, but Alex makes the course fun, dynamic and easy to understand! Thank you!"
"It was a chore that I had to get through as quickly as possible. Why? The practice quizzes. They are everything that gives this type of course a bad name: confusingly written, ambiguous (often with multiple possible correct answers) and far too frequent."
"Excellent introduction to data science and its basic concepts, and fits perfectly to the rest of specialization. "
The course focuses on practical applications of analytics in agile development.
"This was a really good course for demonstrating how you can apply analytics, not just a run through of the theory."
"It was so interesting to learn how to devolop questions, analizes, hypothesis and define metrics to find the best ways to solve problems and develop products."
"Gives thoughtfull ideas on how to manage and make use of data in agile development."
"Of all modules in the Specialization course, this will rate as my second favourite, as a product manager/ Sales individual and even customer-facing PM these skills will provide you with a greater understanding of your client, and how to test your product to ensure you build good products and solutions."
In-depth interviews with industry experts provide real-world insights.
"The interviews with industry professionals were good"
"I really enjoyed the interviews which gave a nice insight into the 'real' world."
"The course provided relevant knowledge, useful opinions of experts (practitioners) and frameworks for managing a Digital product using Agile."
Some learners found the course to be difficult at times.
"Quite difficult course about agile analytics. But very useful."
"I didn't get as much out of this as the other courses"
The course includes frequent quizzes that some learners found to be confusing or ambiguous
"The practice quizzes. They are everything that gives this type of course a bad name: confusingly written, ambiguous (often with multiple possible correct answers) and far too frequent"
Activities
Coming soon
We're preparing activities for
Product Analytics and AI.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Product Analytics and AI will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is responsible for analyzing data to find patterns and insights that can be used to improve business decisions. They use a variety of statistical and machine learning techniques to extract meaning from data. This course can help Data Scientists learn how to use data and analytics to solve business problems. It can also help them build a strong foundation in the use of AI and machine learning to improve data analysis.
Product Manager
Product Manager
A Product Manager is responsible for the overall success of a product, from its inception to its launch and beyond. They work with engineers, designers, and marketers to ensure that the product meets the needs of users and is successful in the marketplace. This course can help Product Managers learn how to use data and analytics to make better decisions about product development and marketing. It can also help them build a strong foundation in the use of AI and machine learning to improve product performance.
Marketing Analyst
Marketing Analyst
A Marketing Analyst is responsible for analyzing marketing data to find patterns and insights that can be used to improve marketing campaigns. They use a variety of statistical and machine learning techniques to extract meaning from data. This course can help Marketing Analysts learn how to use data and analytics to improve marketing campaigns. It can also help them build a strong foundation in the use of AI and machine learning to improve marketing analysis.
Operations Analyst
Operations Analyst
An Operations Analyst is responsible for analyzing operations data to find patterns and insights that can be used to improve efficiency and effectiveness. They use a variety of statistical and machine learning techniques to extract meaning from data. This course can help Operations Analysts learn how to use data and analytics to make better decisions about operations. It can also help them build a strong foundation in the use of AI and machine learning to improve operations analysis.
Finance Analyst
Finance Analyst
A Finance Analyst is responsible for analyzing financial data to find patterns and insights that can be used to make investment decisions. They use a variety of statistical and machine learning techniques to extract meaning from data. This course can help Finance Analysts learn how to use data and analytics to make better investment decisions. It can also help them build a strong foundation in the use of AI and machine learning to improve financial analysis.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and systems to find ways to improve efficiency and effectiveness. They use data and analytics to identify problems and develop solutions. This course can help Business Analysts learn how to use data and analytics to make better decisions about business processes. It can also help them build a strong foundation in the use of AI and machine learning to improve business analysis.
Risk Analyst
Risk Analyst
A Risk Analyst is responsible for analyzing risk data to find patterns and insights that can be used to make risk management decisions. They use a variety of statistical and machine learning techniques to extract meaning from data. This course can help Risk Analysts learn how to use data and analytics to make better risk management decisions. It can also help them build a strong foundation in the use of AI and machine learning to improve risk analysis.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines. They use a variety of tools and technologies to collect, store, and process data. This course can help Data Engineers learn how to use data and analytics to improve data engineering. It can also help them build a strong foundation in the use of AI and machine learning to improve data engineering.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and testing software applications. They use a variety of programming languages and tools to create software that meets the needs of users. This course can help Software Engineers learn how to use data and analytics to improve software development. It can also help them build a strong foundation in the use of AI and machine learning to improve software engineering.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for ensuring that customers are successful with a product or service. They work with customers to identify their needs and develop solutions. This course can help Customer Success Managers learn how to use data and analytics to improve customer success. It can also help them build a strong foundation in the use of AI and machine learning to improve customer success management.
Sales Engineer
Sales Engineer
A Sales Engineer is responsible for helping customers to understand and purchase a product or service. They work with customers to identify their needs and develop solutions. This course can help Sales Engineers learn how to use data and analytics to improve sales. It can also help them build a strong foundation in the use of AI and machine learning to improve sales engineering.
Product Owner
Product Owner
A Product Owner is responsible for defining the vision and roadmap for a product. They work with stakeholders to gather requirements and prioritize features. This course can help Product Owners learn how to use data and analytics to make better decisions about product development and marketing. It can also help them build a strong foundation in the use of AI and machine learning to improve product ownership.
UX Designer
UX Designer
A UX Designer is responsible for designing the user experience for a product or service. They work with users to understand their needs and develop solutions. This course can help UX Designers learn how to use data and analytics to improve UX design. It can also help them build a strong foundation in the use of AI and machine learning to improve UX design.
UI Designer
UI Designer
A UI Designer is responsible for designing the visual interface for a product or service. They work with users to understand their needs and develop solutions. This course can help UI Designers learn how to use data and analytics to improve UI design. It can also help them build a strong foundation in the use of AI and machine learning to improve UI design.
Technical Writer
Technical Writer
A Technical Writer is responsible for creating documentation for software products and services. They work with engineers and other stakeholders to gather information and write clear and concise documentation. This course can help Technical Writers learn how to use data and analytics to improve technical writing. It can also help them build a strong foundation in the use of AI and machine learning to improve technical writing.
For more career information including salaries, visit:
OpenCourser.com/course/ihamac/agile
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Product Analytics and AI.
Comprehensive guide to probabilistic graphical models, covering a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for those looking to gain a deep understanding of the theoretical foundations of machine learning.
Comprehensive guide to deep learning, covering the theoretical foundations, algorithms, and applications of deep learning. It is an invaluable resource for those interested in learning about the latest advancements in deep learning.
Comprehensive guide to pattern recognition and machine learning, covering a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for those looking to gain a deep understanding of the theoretical foundations of machine learning.
Comprehensive guide to machine learning from a probabilistic perspective. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for those looking to gain a deep understanding of the theoretical foundations of machine learning.
Comprehensive guide to Bayesian data analysis, covering a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for those looking to gain a deep understanding of the theoretical foundations of machine learning.
Classic in the field of machine learning and provides a comprehensive overview of statistical learning methods. It valuable resource for those looking to gain a deep understanding of the theoretical foundations of machine learning.
Practical guide to machine learning using the R programming language. It covers a wide range of machine learning topics, including supervised learning, unsupervised learning, and deep learning. This book valuable resource for those looking to gain hands-on experience with machine learning.
Practical guide to machine learning using the Python programming language. It covers a wide range of machine learning topics, including supervised learning, unsupervised learning, and deep learning. This book valuable resource for those looking to gain hands-on experience with machine learning.
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Provides a practical guide to data science, covering the entire data science workflow, from data collection to model deployment. It valuable resource for those looking to gain a comprehensive understanding of data science.
Practical guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning topics, including data preparation, feature engineering, model selection, and evaluation. This book is an excellent resource for those looking to gain hands-on experience with machine learning.
Gentle introduction to statistical learning and provides a practical guide to applying statistical learning methods to real-world problems. It valuable resource for those looking to get started with machine learning.
Focuses on the application of data science in a business context. It covers topics such as data mining, predictive modeling, and business intelligence. This book valuable resource for those looking to use data science to solve business problems.
Provides a comprehensive introduction to data analytics, covering the fundamental concepts, techniques, and tools essential for understanding and working with data in a meaningful way. It valuable resource for those looking to build a strong foundation in data analytics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ihamac/agile
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Effort
4 weeks of study, 2-5 hours/week
Level
Introductory
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Institution
University of Virginia
Instructor
Alex Cowan
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Digital Product Management. It's comprised of five courses:
- Managing an Agile Team
- Hypothesis-Driven Development
- Agile Meets Design Thinking
- Digital Product Management: Modern Fundamentals
- Product Analytics and AI (viewing)
Good to know
Provides a strong foundation for beginners who want to build a data analytics platform for an agile team
Taught by Alex Cowan, who are recognized for their work in analytics and machine learning
Develops skills, knowledge, and tools that are highly relevant to industry in analytics and data science
Requires learners to have a strong understanding of statistics and programming
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