If you’re looking to enhance your skills in the field of Ab testing, you might be interested in enrolling in an online course. With a plethora of options available on the internet, it can be challenging to determine which courses are worth your time and money. This article aims to provide insights into the best online Ab testing courses available, outlining their features and benefits to help you make an informed decision.
Here’s a look at the Best Ab Testing Courses and Certifications Online and what they have to offer for you!
10 Best Ab Testing Courses and Certifications Online
- 10 Best Ab Testing Courses and Certifications Online
- 1. Bayesian Machine Learning in Python: A/B Testing by Lazy Programmer Inc. (Udemy) (Our Best Pick)
- 2. A/B Testing and Experimentation for Beginners by Anil Batra (Udemy)
- 3. A/B Testing Crash Course for Product Managers by Gökçe Tombul (Udemy)
- 4. Complete Course on Product A/B Testing with Interview Guide by Preeti Semwal (Udemy)
- 5. Coding for A/B testing: Run more AB tests, find more winners by Ruben de Boer (Udemy)
- 6. Optimization & A/B Testing Statistics by Jared Waxman (Udemy)
- 7. A/B Testing in Python by 365 Careers, Anastasia K (Udemy)
- 8. A/B Testing 101 by Mel Restori (Udemy)
- 9. Learn to A/B test like a professional by Andrew McKenna (Udemy)
- 10. A/B testing 101: Methodology for Digital Marketing A/B Test by Maria Bulatova (Udemy)
This course, titled “Bayesian Machine Learning in Python: A/B Testing”, is designed to provide data science, machine learning, and data analytics techniques for marketing, digital media, online advertising, and more, with a focus on A/B testing. The course covers traditional A/B testing and its approximations, but eventually moves on to the Bayesian machine learning approach, which offers an entirely different way of thinking about probability. The course aims to provide the fundamental tools of the Bayesian method through the concrete example of A/B testing, and then allow learners to carry those Bayesian techniques to more advanced machine learning models in the future. The suggested prerequisites for the course include probability, Python coding, as well as knowledge of Numpy, Scipy, and Matplotlib. The course also offers extra help with Python coding for beginners and effective learning strategies for machine learning.
The course instructors, Lazy Programmer Inc., emphasize a hands-on approach to learning, with the belief that if you can’t implement it, you don’t understand it. The course teaches learners how to implement machine learning algorithms from scratch, rather than just plugging in data into a library. The course content includes an introduction and outline, the high-level picture, Bayes rule and probability review, traditional A/B testing, Bayesian A/B testing, Bayesian A/B testing extension, practice makes perfect, setting up your environment, extra help with Python coding for beginners, and effective learning strategies for machine learning. The course concludes with an appendix and FAQ finale.
The course aims to help learners solve the explore-exploit dilemma through adaptive methods. It covers the epsilon-greedy algorithm, which is used in reinforcement learning, and its improvement with a similar algorithm called UCB1. Finally, the course covers a fully Bayesian approach, which is considered a paradigm shift and offers powerful new tools for machine learning.
The A/B Testing and Experimentation for Beginners course, taught by Anil Batra, covers the fundamentals of A/B testing and provides a process for making data-driven marketing, conversion rate, and customer experience decisions. The course is designed to help learners improve landing pages, conversion rates, and marketing ROI. It includes examples to inspire creative thinking and covers various applications of A/B testing including product changes, marketing messages, creative design, call to actions, funnel conversions, search marketing, conversion rate optimization, and growth hacking.
The course also teaches learners how to set up an A/B test in Google Optimize, a tool used by major organizations such as Facebook, Google, Amazon, Microsoft, and Twitter, who spend millions of dollars on A/B testing. Anil Batra has worked with major organizations, including Microsoft, Starbucks, Wall Street Journal, ESPN, and T-Mobile, to help them make data-driven marketing decisions.
Anil Batra has over 15 years of experience in digital marketing and analytics and has trained people from diverse backgrounds to become high-performing digital marketers and analysts. He has engineering degrees and an MBA and has developed various courses, teaching students from all over the world. He is an online instructor for the University of British Columbia, University of Washington, Bellevue College, and Digital Analytics Association.
The course is divided into several sections, including an introduction, A/B testing and related terms and definitions, ideas for testing, the A/B testing process, and Google Optimize. By the end of the course, learners will have a solid understanding of A/B testing and how to apply it to make data-driven decisions that can improve their marketing, conversion rates, and customer experience.
The A/B Testing Crash Course for Product Managers is a course taught by Gökçe Tombul, Senior Director of Product Management at Udemy. The course aims to teach a step-by-step process to determine what to A/B test in order to improve a product and grow a business. The course consists of five sections: Introduction, Build the Right Process, Build the Right Team, Create the Right Culture, and Conclusion.
The course begins by addressing the issue of failed experiments due to testing the wrong things. It emphasizes the importance of spending time and resources in the right direction, especially for those on a budget. The course promises to provide a method for figuring out what to experiment with, as well as practical advice on managing dependencies, identifying the necessary qualities of a strong experiment owner, and hiring the right people.
The course is designed to be short and to the point, providing advanced frameworks and ideas for improving a product through A/B testing. The instructor encourages students to take the course in order to take their products to the next level. However, it is important to note that the course does not cover A/B testing tools or statistical analysis.
Overall, the A/B Testing Crash Course for Product Managers is a concise course that offers practical advice for those looking to improve their products through A/B testing. The course is divided into five easy-to-follow sections, each focusing on a different aspect of the A/B testing process.
The Complete Course on Product A/B Testing with Interview Guide, instructed by Preeti Semwal, focuses on teaching the fundamental concepts of product experimentation and A/B testing. The course is designed to provide an in-depth understanding of statistical concepts through everyday examples. The course aims to enable the attendees to apply product experimentation in their job or interviews.
The course covers topics such as AB Testing, Multivariate Testing, Multi-armed Bandit, R Coding, Hypothesis Testing, and Statistics. The course is designed to ensure a comprehensive understanding of the process, from conceptualization and design to implementation and analysis.
The course is intended for individuals who are interested in using product experimentation in their start-up or current role, or those who are interviewing for a position in big Tech. The course is focused on equipping the attendees with the necessary skills and knowledge to succeed in their job or interviews.
The course is structured in a way that reinforces the concepts with real-world examples from companies such as Amazon, AirBnb, Square, and Uber. Attendees will also be provided with templates and cheat sheets to aid in their learning process.
The course is two hours long and includes sections on Product Experimentation Overview, Statistics & A/B Testing, Sample Size & Duration, Start to Finish Example in R, and Interview Preparation Guide. Attendees will learn how to conduct successful AB tests and excel in AB testing interviews.
This course, “Coding for A/B Testing: Run More AB Tests, Find More Winners,” is designed to help Conversion Optimization specialists or anyone working on website A/B testing. The course focuses on teaching learners HTML, CSS, JS, and data tracking for website AB testing. By taking this course, learners will be able to set up and run their own A/B tests without relying on a developer, making it easier to run more tests and increase their chances of success.
The course is unique in that it focuses on A/B testing rather than web development specifically. This means that learners will be gaining skills that are crucial to their success as Conversion specialists, growth hackers, and front-end developers.
Overall, this course is a bestselling A/B testing course on Udemy in the A/B testing category. With its practical approach and focus on building and running reliable and successful A/B tests, learners will be able to significantly improve their chances of success in their field.
The Optimization & A/B Testing Statistics Course, led by instructor Jared Waxman, focuses on the importance of optimization and a/b testing for businesses of all sizes. The course aims to help participants learn how to conduct a/b tests effectively and efficiently to increase their learning and growth rates.
The course begins with an introduction to the basics, followed by an in-depth discussion of the 8 steps involved in running a successful a/b test. Participants will also learn about the statistical concepts behind hypothesis testing, which will allow them to set up tests correctly and analyze them with the appropriate level of rigor.
The course covers various topics, including examples of a/b tests, hypothesis testing, measurement as risk reduction, selecting a KPI or success metric, selecting from among a/b test and MVT test designs, lift threshold, null hypothesis, statistical significance, sample size estimates, confidence interval, test statistic, t-tests, standard error of the mean, chi-square, Fischer Exact test, statistical power, Type I error, Type II error, and p-values.
The course is designed to provide participants with a comprehensive understanding of optimization and a/b testing. By the end of the course, participants will be equipped with the knowledge and skills to successfully conduct a/b tests, analyze the results, and make data-driven decisions to drive growth and success for their organizations.
The course offers a 30-day money-back guarantee and promises to improve participants’ skill levels. Companies that successfully implement optimization strategies can see double and triple digit ROI. By taking this course, participants can impress their colleagues with their newfound knowledge and skills on Monday morning.
The course is divided into five sections: Introduction, Goals & Test Designs, Testing in 8 Steps, Statistical Tests, and Wrap-Up. The course is structured in a way that ensures participants can learn at their own pace and apply the concepts they learn in practice.
This course, titled “A/B Testing in Python,” is taught by 365 Careers and Anastasia K. The course teaches students how to define, start, and analyze the results of an A/B test, with a focus on improving business performance. A/B testing is a tool that helps companies make reliable decisions based on data, making this course essential for anyone looking to become a data scientist or analyst.
Anastasia K is a senior data scientist working at a Stockholm-based music streaming startup. She has earned two Master’s degrees in Business Intelligence and Computer Science and has performed a significant number of A/B tests for large tech companies with hundreds of millions monthly users. Through this course, students will learn how to define an A/B test, start an A/B test, and analyze the results of an A/B test on their own.
The course includes a case study of a fictional company with a digital product, which unfolds throughout the course and covers everything from the beginning of the A/B testing process to advanced considerations. Anastasia also provides advice on how to prepare for A/B testing questions in a data scientist or analyst interview. One unique aspect of this course is that it teaches students how to design A/B tests for digital products with millions or hundreds of millions of users from a business, technical, and data analysis perspective.
This course is suitable for data science students, junior data scientists with no experience in A/B testing, software developers, and product managers. The course covers the fundamental skills needed for a career in data science and teaches an invaluable skill that can transform a company’s business and the student’s career. The course includes an introduction, defining KPIs and metrics, setting up and executing A/B tests, advanced considerations, advanced questions for interview preparation, and a conclusion.
Students can purchase the course by clicking the “Buy now” button and beginning the journey today.
The A/B Testing 101 course, led by instructor Mel Restori, is designed to teach participants how to successfully execute A/B testing from planning to decision-making. A/B testing, also known as split or hypothesis testing, is a method of optimizing business performance via data-informed decision-making.
This course covers a wide range of applications for A/B testing, such as marketers using it to maximize their return on investment, product managers testing new website and app features to improve user experience, and data scientists using it to enhance their algorithms.
Unlike other similar courses, A/B Testing 101 focuses on the entire lifecycle of experimentation. Participants will learn how to develop a learning plan for determining what to test, plan and execute A/B tests to gain the most insights and reduce testing time, interpret test results and other data to make informed decisions, and avoid common pitfalls in A/B testing.
While participants will not learn statistical formulas in this course, they will develop a strong grasp of the intuition and underlying principles behind these formulas to effectively run experiments and interpret results. The course also covers alternatives to A/B testing and how to determine when an idea should be tested.
In addition to course material, participants will receive tools such as an experiment planning form, A/B Testing Calculator Reference, and a sample experiment decision-making flow chart to help implement best practices. Optional reading material will also be provided for additional learning.
Course content includes an introduction, Stats Intuition 101 (from intuition to A/B test results), A/B Testing, A/B Testing and Beyond, and a wrap-up section.
The Learn to A/B Test like a Professional course, led by Andrew McKenna, aims to equip participants with the skills to conduct effective A/B tests that can boost their company’s profitability. This course provides a comprehensive understanding of A/B testing and how it fits into the conversion rate optimization (CRO) process to improve a website’s conversion rates. Unlike many other A/B testing courses, this program covers all aspects of the testing process, including setting and measuring goals, gathering insights, creating problem statements and hypotheses, and documenting experiments. The course is suitable for both beginners and experienced digital marketers.
The course covers several topics, organized into the following sections: Introduction, A/B Testing and the Optimization Process, Measuring Success, Insight Gathering, Testing and Iteration, Before Testing, During Testing, After Testing, and Documenting your A/B Tests. Each section provides a detailed overview of the respective topic to facilitate insight gathering, testing, and documentation.
The course is ideal for those who want to start running A/B tests but don’t know where to start. It is also appropriate for individuals who work in marketing and want to expand their knowledge or run a small or medium-sized business and want to increase their sales. By the end of the course, participants will have a clear understanding of the A/B testing process, enabling them to set up and run A/B tests that can help them improve their website’s conversion rates.
Course Title: A/B Testing 101: Methodology for Digital Marketing A/B Test
This course aims to provide practical knowledge to marketers of any level about A/B testing methodology and its business impact. The course covers topics such as choosing and characterizing metrics to evaluate marketing experiments, designing A/B tests with sufficient statistical power, analyzing test results, and ensuring that the results have a business impact and can be implemented in daily marketing activities.
The course is divided into several sections, with a welcome message as the course introduction. The first section covers A/B testing 101, which lays the foundation for understanding A/B testing methodology. The second section dives into A/B testing methodology in more detail, explaining the different types of A/B tests and their applications.
The third section teaches how to set up A/B tests, including how to determine sample size, set test duration, determine success metrics, and develop test variations. The fourth section focuses on analyzing test results, with an emphasis on how to draw valid conclusions from statistical analysis.
Finally, the fifth section is concerned with ensuring that the results of the test have a business impact and can be implemented in daily marketing activities. By the end of the course, learners will have a thorough understanding of A/B testing methodology and how to apply it to their marketing strategies.