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Josh Bernhard ,
Mike Yi,
Judit Lantos,
David Drummond,
Andrew Paster,
Juno Lee,
and
Luis Serrano
Take Udacity's Experimental Design course and learn how to run statistically valid tests, interpret results and generate personalized recommendations based on user data.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
Read more
Take Udacity's Experimental Design course and learn how to run statistically valid tests, interpret results and generate personalized recommendations based on user data.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
Read more
Take Udacity's Experimental Design course and learn how to run statistically valid tests, interpret results and generate personalized recommendations based on user data.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic statistical modeling
- Data wrangling
- Python for data science
- Basic descriptive statistics
- Relational database basics
- Data visualization
- Linear algebra
- Basic calculus
- Inferential statistics
- JSON
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/47o6r8/experimental
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What's inside
Syllabus
Why do we care about experiment design and recommendation engines? In this lesson, you'll get an overview of the topics you'll learn in this course.
Read more
Syllabus
Why do we care about experiment design and recommendation engines? In this lesson, you'll get an overview of the topics you'll learn in this course.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Covers experimental design and data analysis skills that are highly relevant to data science, statistics, and engineering fields
Offers hands-on labs and interactive materials to reinforce learning and build practical skills
Taught by industry experts with extensive experience in experimental design and recommendation systems, ensuring high-quality instruction
Requires foundational knowledge in statistics, modeling, and programming, suitable for intermediate learners or those seeking to advance their skills
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Practical experimental design and recommendations
According to learners, this course offers a solid foundation in experimental design and recommendation systems, particularly valuable for data professionals. Many found the practical projects, especially the A/B testing case study and the IBM Watson Studio application, to be highly effective in solidifying understanding. While the course provides a strong theoretical base, students emphasize the importance of meeting the stated prerequisites, as the content can be challenging. Some suggest that the recommendation engine section could be more in-depth. Overall, it's considered a rewarding experience for those with the right background.
Lectures are generally clear and well-structured.
"The instructors explain complex statistical concepts in an understandable way."
"I found the lectures to be very concise and to the point, which I appreciated."
"Content delivery was largely clear, though some advanced topics required rewatching."
Provides skills directly applicable to data roles.
"I can immediately apply the A/B testing principles learned here in my job."
"The course provides practical tools and strategies that I could apply immediately to my work."
"It bridges the gap between theory and practical application in data science roles."
Hands-on projects apply concepts effectively.
"The A/B Testing case study was incredibly helpful; it brought all the conceptual and statistical parts together."
"I really enjoyed the final project on IBM Watson Studio; it helped me put everything I learned into practice."
"The hands-on exercises cemented my understanding of experimental design."
Experimental design is strong, recommendations less so.
"The experimental design section was thorough and well-explained, covering crucial concepts."
"While the experimental design was excellent, I felt the recommendation engines part was a bit rushed and could benefit from more detail."
"I wish the recommendation section had more practical examples and deeper dives into algorithms."
Requires strong foundational knowledge to succeed.
"Ensure you have a solid grasp of statistics and Python before starting, otherwise you'll struggle."
"The course is very dense, and the prerequisites listed are genuinely important. Don't skip them."
"I found myself constantly reviewing external resources for the statistical concepts, as the course assumes prior knowledge."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Experimental Design and Recommendations with these
activities:
Review Basic Statistical Modeling
Show steps
Refresh your understanding of basic statistical modeling concepts to strengthen your foundation for this course.
Show steps
-
Revisit key statistical modeling concepts such as probability distributions, hypothesis testing, and regression analysis.
-
Work through practice problems and exercises to solidify your knowledge.
Join a Study Group
Show steps
Engage with fellow learners to discuss course concepts and share insights.
Browse courses on
Collaboration
Show steps
-
Find a study group or create your own with classmates or online.
-
Meet regularly to review material, solve problems, and exchange perspectives.
Python for Data Science Practice
Show steps
Reinforce your Python skills for data science through targeted practice drills.
Browse courses on
Python
Show steps
-
Solve coding challenges related to data manipulation, visualization, and analysis.
-
Participate in online coding competitions to test your skills against others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend an Experiment Design Workshop
Show steps
Gain hands-on experience with experiment design through a structured workshop.
Browse courses on
Experiment Design
Show steps
-
Identify and register for a workshop that aligns with your learning goals.
-
Actively participate in the workshop, engage with experts, and apply techniques.
Learn about A/B Testing
Show steps
Expand your knowledge of A/B testing techniques to enhance your understanding of experiment design.
Browse courses on
A/B Testing
Show steps
-
Follow online tutorials and webinars on A/B testing principles and best practices.
-
Apply A/B testing concepts to real-world scenarios through case studies and simulations.
Develop a Recommendation Engine Prototype
Show steps
Deepen your understanding of recommendation engines by building a prototype.
Browse courses on
Recommendation Engines
Show steps
-
Choose a domain and collect relevant data.
-
Implement a recommendation algorithm using Python or R.
-
Evaluate the performance of your prototype and refine it based on results.
Mentor a Junior Data Scientist
Show steps
Solidify your understanding of experiment design and recommendation engines by sharing your knowledge with others.
Show steps
-
Identify a junior data scientist or student seeking guidance in experiment design.
-
Provide mentorship through regular meetings, code reviews, and project support.
Review Basic Statistical Modeling
Show steps
Refresh your understanding of basic statistical modeling concepts to strengthen your foundation for this course.
Show steps
Show steps
Show steps
- Revisit key statistical modeling concepts such as probability distributions, hypothesis testing, and regression analysis.
- Work through practice problems and exercises to solidify your knowledge.
Join a Study Group
Show steps
Engage with fellow learners to discuss course concepts and share insights.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find a study group or create your own with classmates or online.
- Meet regularly to review material, solve problems, and exchange perspectives.
Python for Data Science Practice
Show steps
Reinforce your Python skills for data science through targeted practice drills.
Browse courses on
Python
Show steps
Show steps
Show steps
- Solve coding challenges related to data manipulation, visualization, and analysis.
- Participate in online coding competitions to test your skills against others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend an Experiment Design Workshop
Show steps
Gain hands-on experience with experiment design through a structured workshop.
Browse courses on
Experiment Design
Show steps
Show steps
Show steps
- Identify and register for a workshop that aligns with your learning goals.
- Actively participate in the workshop, engage with experts, and apply techniques.
Learn about A/B Testing
Show steps
Expand your knowledge of A/B testing techniques to enhance your understanding of experiment design.
Browse courses on
A/B Testing
Show steps
Show steps
Show steps
- Follow online tutorials and webinars on A/B testing principles and best practices.
- Apply A/B testing concepts to real-world scenarios through case studies and simulations.
Develop a Recommendation Engine Prototype
Show steps
Deepen your understanding of recommendation engines by building a prototype.
Browse courses on
Recommendation Engines
Show steps
Show steps
Show steps
- Choose a domain and collect relevant data.
- Implement a recommendation algorithm using Python or R.
- Evaluate the performance of your prototype and refine it based on results.
Mentor a Junior Data Scientist
Show steps
Solidify your understanding of experiment design and recommendation engines by sharing your knowledge with others.
Show steps
Show steps
Show steps
- Identify a junior data scientist or student seeking guidance in experiment design.
- Provide mentorship through regular meetings, code reviews, and project support.
Career center
Learners who complete Experimental Design and Recommendations will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
The skills you gain in Experimental Design and Recommendations will be useful in your work as a Data Scientist by providing you with the knowledge you need to properly design and conduct experiments, which is essential for gathering data. You will also learn how to generate recommendations based on user data, which is a valuable skill for Data Scientists who work on recommendation systems. Additionally, this course provides you with a strong foundation in statistical modeling and analysis, which are essential skills for Data Scientists.
Statistician
Statistician
The Experimental Design and Recommendations course provides you with the skills you need to be successful as a Statistician. You will learn how to design and conduct experiments, as well as how to analyze the results of those experiments. You will also learn how to generate recommendations based on user data, which is a valuable skill for Statisticians who work on recommendation systems. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Data Analyst
Data Analyst
The Experimental Design and Recommendations course provides you with the skills you need to succeed as a Data Analyst. You will learn how to design and conduct experiments to collect data, as well as how to analyze the results of those experiments. You will also learn how to generate recommendations based on user data, which is a valuable skill for Data Analysts who work on recommendation systems. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Customer Success Manager
Customer Success Manager
The Experimental Design and Recommendations course can be useful for Customer Success Managers who want to learn more about how to design and conduct experiments to collect data on customer satisfaction. You will also learn how to analyze the results of those experiments and generate recommendations for improving customer satisfaction. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Business Analyst
Business Analyst
The Experimental Design and Recommendations course can be useful for Business Analysts who want to learn more about how to design and conduct experiments to collect data on business processes. You will also learn how to analyze the results of those experiments and generate recommendations for process improvements. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Marketing Analyst
Marketing Analyst
The Experimental Design and Recommendations course can be useful for Marketing Analysts who want to learn more about how to design and conduct experiments to collect data on marketing campaigns. You will also learn how to analyze the results of those experiments and generate recommendations for campaign improvements. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Product Manager
Product Manager
The Experimental Design and Recommendations course can be useful for Product Managers who want to learn more about how to design and conduct experiments to collect data on their products. You will also learn how to analyze the results of those experiments and generate recommendations for product improvements. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Quality Assurance Engineer
Quality Assurance Engineer
The Experimental Design and Recommendations course may be useful for Quality Assurance Engineers who want to learn more about how to design and conduct experiments to collect data on software quality. You will also learn how to analyze the results of those experiments and generate recommendations for improving software quality.
Project Manager
Project Manager
The Experimental Design and Recommendations course may be useful for Project Managers who want to learn more about how to design and conduct experiments to collect data on project performance. You will also learn how to analyze the results of those experiments and generate recommendations for improving project performance.
Technical Support Engineer
Technical Support Engineer
The Experimental Design and Recommendations course may be useful for Technical Support Engineers who want to learn more about how to design and conduct experiments to troubleshoot technical issues. You will also learn how to analyze the results of those experiments and generate recommendations for resolving technical issues. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Sales Engineer
Sales Engineer
The Experimental Design and Recommendations course may be useful for Sales Engineers who want to learn more about how to design and conduct experiments to collect data on sales performance. You will also learn how to analyze the results of those experiments and generate recommendations for improving sales performance. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Operations Analyst
Operations Analyst
The Experimental Design and Recommendations course may be useful for Operations Analysts who want to learn more about how to design and conduct experiments to collect data on operational efficiency. You will also learn how to analyze the results of those experiments and generate recommendations for improving operational efficiency. Additionally, this course provides you with a strong foundation in statistical modeling and analysis.
Financial Analyst
Financial Analyst
The Experimental Design and Recommendations course may be useful for Financial Analysts who want to learn more about how to design and conduct experiments to collect data on financial performance. You will also learn how to analyze the results of those experiments and generate recommendations for improving financial performance.
Product Marketing Manager
Product Marketing Manager
The Experimental Design and Recommendations course may be useful for Product Marketing Managers who want to learn more about how to design and conduct experiments to collect data on product marketing campaigns. You will also learn how to analyze the results of those experiments and generate recommendations for improving product marketing campaigns.
Software Engineer
Software Engineer
The Experimental Design and Recommendations course may be useful for Software Engineers who want to learn more about how to design and conduct experiments to collect data on software performance. You will also learn how to analyze the results of those experiments and generate recommendations for improving software performance.
For more career information including salaries, visit:
OpenCourser.com/course/47o6r8/experimental
Reading list
We've selected 11 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
Experimental Design and Recommendations.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, regression, classification, and clustering.
Provides a probabilistic perspective on machine learning. It covers a wide range of topics, including supervised and unsupervised learning, regression, classification, and clustering.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including deep neural networks, convolutional neural networks, and recurrent neural networks.
Save
Provides a fascinating look at the history and future of artificial intelligence. It explores the different approaches to artificial intelligence, and the challenges and opportunities that lie ahead.
Save
Explores the potential risks and benefits of artificial intelligence. It argues that the development of artificial intelligence could pose a serious threat to humanity, and that we need to take steps to ensure that artificial intelligence is used for good.
Save
Explores the implications of artificial intelligence for the future of humanity. It argues that artificial intelligence could lead to a new era of human progress, but that we need to be careful to use artificial intelligence in a way that is beneficial to all.
Explores the challenges of aligning the goals of artificial intelligence with the values of humanity. It argues that we need to develop new ways to ensure that artificial intelligence is used for good.
Save
Explores the possible futures of humanity. It argues that the development of artificial intelligence could lead to a new era of human progress, but that we need to be careful to use artificial intelligence in a way that is beneficial to all.
Provides a gentle introduction to statistical learning methods, including supervised and unsupervised learning, regression, classification, and clustering.
Explores the potential of artificial intelligence to transform humanity. It argues that the development of artificial intelligence could lead to a new era of human progress, but that we need to be careful to use artificial intelligence in a way that is beneficial to all.
Save
Provides a comprehensive overview of data mining techniques, including data preprocessing, data mining algorithms, and data visualization. It valuable resource for anyone who wants to learn more about data mining.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/47o6r8/experimental
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Effort
1 month
Level
Advanced
Via
Udacity
Instructors
Josh Bernhard
Mike Yi
Judit Lantos
David Drummond
Andrew Paster
Juno Lee
Luis Serrano
Language
English
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