Статистика для обработки экспериментов и А/B-тестирования from Coursera

Если Вы боитесь проводить А/В тестирование или не уверены, что сможете правильно посчитать сезонность товаров по представленным данным. Если Вам хочется вырасти из рядового маркетолога в руководителя службы или просто сменить вид деятельности и заняться маркетингом - этот курс для Вас! Курс ориентирован на широкую аудиторию, заинтересованную в изучении статистических экспериментов, A / B-тестирования и анализа данных. Студенты курса должны обладать базовыми компьютерными навыками и математическим образованием на уровне бакалавриата. A / B тестирование является популярным способом тестирования гипотез в маркетинге и набирает обороты в области науки о данных. В данном курсе мы покажем, что такое A / B-тестирование и как Вы можете использовать A / B-тестирование в науке о данных. Наш курс поможет вам - Строить гипотезы h0 и h1 для анализа данных - Определять соответствующий уровень статистической значимости для различных бизнес-целей - Видеть разницу между частотным и байесовским подходами - Проверять гипотезы и интерпретировать результаты. В заключение курса Вам предстоит проанализировать данные А/Б тестирования интернет-магазина. После прохождения курса Вы сможете: - провести регрессионный анализ и создать прогноз продаж на сайте, - использовать ансамблевые методы для улучшения качества прогнозной модели, - проверить статистику на наличие сезонности и обосновать маркетинговые решения, опираясь на анализ данных.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches the basics of statistical experiments and A/B testing, making it suitable for beginners

Covers advanced topics like seasonality and regression analysis, appealing to experienced learners

Taught by experts in marketing and data science, ensuring high-quality content

Focuses on practical applications, preparing learners for real-world scenarios

Provides a comprehensive understanding of statistical analysis, valuable for various industries

Requires prior knowledge in computer skills and mathematics, potentially limiting accessibility for some learners

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 Статистика для обработки экспериментов и А/B-тестирования with these activities:

Attend a study group to discuss A/B testing case studies

Show steps

Collaborate with peers to deepen understanding and gain diverse perspectives.

Browse courses on A/B Testing

Show steps

Find a study group or organize one with classmates.
Select A/B testing case studies for discussion.
Discuss the case studies, sharing insights and perspectives.

Read 'Statistical Inference'

Show steps

Review the key concepts of statistical inference to deepen your understanding of the course materials.

View Statistical Inference (Chapman & Hall/CRC Texts... on Amazon

Show steps

Obtain a copy of the book and read the first three chapters.
Identify the key concepts of statistical inference, such as hypothesis testing, confidence intervals, and power analysis.
Work through the practice problems at the end of each chapter.

Follow a tutorial on A/B testing using a specific software platform

Show steps

Learn about the practical aspects of A/B testing by following a tutorial.

Show steps

Choose a software platform for A/B testing, such as Google Optimize or Optimizely.
Find a tutorial on how to use the software platform you chose.
Follow the steps in the tutorial to create an A/B test.

Ten other activities

Expand to see all activities and additional details

Show all 13 activities

Follow tutorials on Bayesian A/B testing

Show steps

Extend understanding of concepts covered in the course by exploring related materials.

Browse courses on Bayesian Statistics

Show steps

Search for tutorials on Bayesian A/B testing.
Follow the tutorials and take notes on the key concepts.
Apply the concepts to practice problems and check your understanding.

Practice interpreting A/B test results

Show steps

Reinforce understanding by coding solutions to practice problems and receiving immediate feedback.

Browse courses on A/B Testing

Show steps

Read the problem statement and identify the hypothesis being tested.
Write code to calculate the test statistic and p-value.
Interpret the p-value and draw a conclusion about the hypothesis.

Discuss A/B testing with classmates or colleagues

Show steps

Engage with peers to exchange ideas and deepen your understanding.

Show steps

Find a study partner or group who is also taking the course.
Schedule a time to meet with your study partner or group.
Discuss the concepts of A/B testing and share your experiences with using it.

Attend a workshop on A/B testing

Show steps

Learn from experts and network with professionals in the field.

Show steps

Find a workshop on A/B testing that is relevant to your interests.
Register for the workshop.
Attend the workshop.

Create a blog post on A/B testing best practices

Show steps

Reinforce understanding and solidify your ability to synthesize knowledge on the topic.

Browse courses on A/B Testing

Show steps

Research A/B testing best practices.
Outline the blog post with a clear introduction, body, and conclusion.
Write the blog post in a clear and concise manner, providing examples and case studies.
Edit and proofread the blog post for clarity and grammar.

Conduct an A/B test on a website or mobile app

Show steps

Conduct real-world A/B testing to reinforce what you learn in the course.

Show steps

Choose a metric to measure the success of your A/B test.
Create two versions of the website or app, one with the change you want to test and one without.
Run the A/B test for a period of time.
Analyze the results of the A/B test to determine whether the change you tested had a significant impact.

Write a blog post about A/B testing

Show steps

Explain the concepts of A/B testing to others and solidify your understanding of the topic.

Show steps

Choose a topic for your blog post, such as "The Basics of A/B Testing" or "How to Use A/B Testing to Improve Your Website".
Write a draft of your blog post, explaining the key concepts of A/B testing.
Edit and proofread your blog post.
Publish your blog post on your website or blog.

Develop an A/B testing plan for a specific business case

Show steps

Apply the principles of A/B testing to a real-world business problem.

Show steps

Identify a business problem that could be solved using A/B testing.
Develop a hypothesis for your A/B test.
Design the two versions of your website or app for the A/B test.
Develop a plan for how you will run the A/B test.
Write a report summarizing the results of your A/B test.

Create a personal website or blog and use A/B testing to improve it

Show steps

Apply the concepts of A/B testing to your own website or blog.

Show steps

Create a personal website or blog.
Choose a metric to measure the success of your website or blog.
Develop a hypothesis for your A/B test.
Design the two versions of your website or blog for the A/B test.
Run the A/B test for a period of time.
Analyze the results of the A/B test to determine whether the change you tested had a significant impact.

Attend a workshop on A/B testing in e-commerce

Show steps

Enhance practical skills and gain exposure to real-world applications.

Browse courses on A/B Testing

Show steps

Search for workshops on A/B testing in e-commerce.
Attend the workshop and participate actively.
Apply the knowledge gained to your own projects or work.

Career center

Learners who complete Статистика для обработки экспериментов и А/B-тестирования will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians apply statistical methods to solve problems and draw conclusions from data. This course provides a comprehensive foundation in statistical methods, including hypothesis testing, A/B testing, and data analysis techniques, which are essential skills for Statisticians. The course also covers regression analysis and forecasting, enabling Statisticians to build predictive models, quantify uncertainties, and provide insights based on data analysis. These skills are highly sought after in various industries, including healthcare, finance, and research.

See salaries and explore the career path for Statistician

Marketing Analyst

Marketing Analysts use data to understand customer behavior and optimize marketing campaigns. This course provides a comprehensive foundation in statistical methods specifically tailored for marketing professionals. It covers hypothesis testing, A/B testing, and data analysis techniques, enabling Marketing Analysts to draw meaningful conclusions from data and make informed decisions. Additionally, the course's focus on building statistical models and forecasting helps Marketing Analysts predict future trends and forecast demand, making them invaluable assets in driving marketing strategies.

See salaries and explore the career path for Marketing Analyst

Data Scientist

Data Scientists employ statistical methods to extract meaningful information from data. This course introduces essential statistical concepts, including hypothesis testing and interpretation, which are foundational skills for Data Scientists. Learning to conduct A/B testing and analyze data from experiments empowers Data Scientists to evaluate the effectiveness of marketing campaigns and optimize strategies. This course also covers regression analysis and forecasting, enabling Data Scientists to make predictions about future events. These skills are essential for building predictive models, which are used to guide decision-making and drive strategic initiatives.

See salaries and explore the career path for Data Scientist

Quantitative Researcher

Quantitative Researchers use statistical methods to investigate social and economic issues. This course provides Quantitative Researchers with the statistical knowledge necessary to design and conduct research studies, analyze data, and draw meaningful conclusions. By understanding hypothesis testing, A/B testing, and data analysis techniques, Quantitative Researchers can contribute to evidence-based policy-making and decision-making. The course's focus on regression analysis and forecasting enables Quantitative Researchers to model complex relationships, predict future trends, and quantify uncertainties, making their research more impactful and reliable.

See salaries and explore the career path for Quantitative Researcher

Data Analyst

Data Analysts clean, process, and analyze data to extract meaningful insights for organizations. This course provides a solid foundation in statistical methods, including hypothesis testing, A/B testing, and data analysis techniques, which are essential skills for Data Analysts. The course's emphasis on regression analysis and forecasting enables Data Analysts to build predictive models and forecast future trends. These skills are critical in identifying patterns, predicting outcomes, and supporting decision-making in data-driven organizations.

See salaries and explore the career path for Data Analyst

Risk Analyst

Risk Analysts use statistical methods to assess and manage risk. This course equips Risk Analysts with the statistical knowledge necessary to understand the principles of risk management, quantify risks, and make informed decisions. By learning hypothesis testing, A/B testing, and data analysis techniques, Risk Analysts can develop risk models, evaluate risk scenarios, and propose mitigation strategies to minimize financial losses and protect organizations from potential threats.

See salaries and explore the career path for Risk Analyst

Quantitative Trader

Quantitative Traders use statistical methods to develop and execute trading strategies. This course provides Quantitative Traders with the statistical knowledge necessary to understand financial markets, analyze trading data, and build predictive models for investment decisions. By understanding hypothesis testing, A/B testing, and data analysis techniques, Quantitative Traders can develop trading algorithms, optimize trading strategies, and make informed decisions about risk management and portfolio performance.

See salaries and explore the career path for Quantitative Trader

Business Analyst

Business Analysts use data to identify business problems and develop solutions. This course equips Business Analysts with the statistical skills necessary to analyze business data effectively. By understanding hypothesis testing, A/B testing, and data analysis techniques, Business Analysts can make data-driven recommendations and drive business decisions that improve performance. The course also covers regression analysis and forecasting, enabling Business Analysts to predict future trends and forecast business outcomes. This knowledge empowers them to quantify the impact of business decisions and justify recommendations with supporting evidence.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts use statistical methods to analyze financial data and make investment recommendations. This course provides Financial Analysts with the statistical knowledge necessary to understand financial markets, analyze investment opportunities, and make informed decisions about portfolio management. By understanding hypothesis testing, A/B testing, and data analysis techniques, Financial Analysts can develop financial models, assess investment risks and returns, and make recommendations for optimizing investment strategies and achieving financial goals.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to optimize business operations. This course provides Operations Research Analysts with the statistical knowledge necessary to analyze business data, build models, and make recommendations for improving efficiency and effectiveness. By understanding hypothesis testing, A/B testing, and data analysis techniques, Operations Research Analysts can identify inefficiencies, optimize resource allocation, and develop data-driven solutions to improve business outcomes.

See salaries and explore the career path for Operations Research Analyst

Marketing Manager

Marketing Managers oversee the development and execution of marketing campaigns. This course provides Marketing Managers with the statistical knowledge necessary to evaluate the effectiveness of marketing strategies and optimize campaigns. By understanding hypothesis testing, A/B testing, and data analysis techniques, Marketing Managers can make informed decisions based on data and improve the impact of their marketing efforts. The course's focus on regression analysis and forecasting enables Marketing Managers to predict future trends and forecast demand, helping them plan and allocate resources effectively.

See salaries and explore the career path for Marketing Manager

Actuary

Actuaries use statistical methods to assess and manage financial risk. This course provides Actuaries with the statistical knowledge necessary to understand actuarial science principles, analyze data, and make informed decisions about risk and financial planning. By understanding hypothesis testing, A/B testing, and data analysis techniques, Actuaries can develop actuarial models, assess financial risks, and provide recommendations for mitigating financial risks and ensuring the financial security of individuals and organizations.

See salaries and explore the career path for Actuary

Product Manager

Product Managers are responsible for developing and managing products that meet customer needs. This course equips Product Managers with the statistical knowledge necessary to understand user behavior and make data-driven product decisions. By learning hypothesis testing, A/B testing, and data analysis techniques, Product Managers can gather insights, test product features, and optimize the user experience. The course also covers regression analysis and forecasting, enabling Product Managers to predict future demand and plan product roadmaps based on data-driven evidence.

See salaries and explore the career path for Product Manager

Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models. This course may be helpful for Machine Learning Engineers who want to gain a deeper understanding of the statistical underpinnings of machine learning. By learning hypothesis testing, A/B testing, and data analysis techniques, Machine Learning Engineers can build more robust and effective machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers design and manage data pipelines. This course may be helpful for Data Engineers who want to gain a deeper understanding of the statistical principles behind data analysis. By learning hypothesis testing, A/B testing, and data analysis techniques, Data Engineers can build more efficient and reliable data pipelines that support data-driven decision-making.

See salaries and explore the career path for Data Engineer

Статистика для обработки экспериментов и А/B-тестирования

Good to know

Save this course

Activities

Career center

Reading list

Share

Similar courses