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Applied Statistics
Mean Comparison using Python
Svyatoslav Solodushkin
Svyatoslav Solodushkin
This guided project explores methods for comparing the means of two or more samples. You will learn how to make plots which prove the difference of average sales volumes and prove the signiffacnce of these differences.
Register for this course and see more details by visiting:
OpenCourser.com/course/l958s6/applied
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces methods which are standard in the analytics and machine learning industry
Builds foundational knowledge and skills for learners who want to work with statistical data and tests
Requires learners to have a working knowledge of statistics
Ideal for learners who want to apply statistical methods to marketing and business problems
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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 Applied Statistics: Mean Comparison using Python with these
activities:
Seek guidance from a statistics expert
Show steps
Gain valuable insights and personalized guidance from an experienced professional in the field of statistics
Show steps
-
Identify a statistics expert in your field of interest.
-
Reach out to the expert and request guidance or mentorship.
-
Attend meetings or sessions to learn from the expert's knowledge and experience.
Read 'Statistical Methods for Psychology'
Show steps
Gain a solid foundation in statistical methods used in psychology, which will aid in comprehension of the course material
View
Fundamental Statistics for the Behavioral...
on Amazon
Show steps
-
Read the first six chapters of the book.
-
Complete the practice exercises at the end of each chapter.
Practice hypothesis testing
Show steps
Reinforce understanding of hypothesis testing concepts and improve problem-solving skills
Browse courses on
Hypothesis Testing
Show steps
-
Solve 10 practice problems on hypothesis testing.
-
Review the solutions and identify areas for improvement.
-
Complete a mock hypothesis test using a real-world dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on ANOVA
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Enhance understanding of ANOVA techniques and their application in data analysis
Browse courses on
ANOVA
Show steps
-
Watch a tutorial on the basics of ANOVA.
-
Complete a hands-on tutorial on performing ANOVA using statistical software.
-
Analyze a dataset using ANOVA and interpret the results.
Discuss research papers on statistical methods
Show steps
Foster critical thinking, improve communication skills, and stay updated on the latest research in statistical methods
Browse courses on
Research Methods
Show steps
-
Select a research paper on a statistical method relevant to the course.
-
Read the paper and prepare a summary of the main findings.
-
Join a peer discussion group and present the summary.
-
Engage in discussions and exchange insights with fellow students.
Create a data visualization project
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Develop skills in data visualization, communication, and presenting statistical results effectively
Browse courses on
Data Visualization
Show steps
-
Collect and clean a dataset relevant to the course.
-
Choose appropriate data visualization techniques to represent the data.
-
Create a visually appealing and informative data visualization.
-
Write a brief report interpreting the results and discussing the implications.
Mentor junior students in statistics
Show steps
Enhance understanding of statistical concepts by teaching others, improve communication and interpersonal skills
Show steps
-
Volunteer as a mentor for a statistics club or organization.
-
Provide guidance and support to junior students on statistical concepts and assignments.
-
Create and share study materials or resources to assist students with their learning.
Seek guidance from a statistics expert
Show steps
Gain valuable insights and personalized guidance from an experienced professional in the field of statistics
Show steps
Show steps
Show steps
- Identify a statistics expert in your field of interest.
- Reach out to the expert and request guidance or mentorship.
- Attend meetings or sessions to learn from the expert's knowledge and experience.
Read 'Statistical Methods for Psychology'
Show steps
Gain a solid foundation in statistical methods used in psychology, which will aid in comprehension of the course material
View
Fundamental Statistics for the Behavioral...
on Amazon
Show steps
Show steps
Show steps
- Read the first six chapters of the book.
- Complete the practice exercises at the end of each chapter.
Practice hypothesis testing
Show steps
Reinforce understanding of hypothesis testing concepts and improve problem-solving skills
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Solve 10 practice problems on hypothesis testing.
- Review the solutions and identify areas for improvement.
- Complete a mock hypothesis test using a real-world dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on ANOVA
Show steps
Enhance understanding of ANOVA techniques and their application in data analysis
Browse courses on
ANOVA
Show steps
Show steps
Show steps
- Watch a tutorial on the basics of ANOVA.
- Complete a hands-on tutorial on performing ANOVA using statistical software.
- Analyze a dataset using ANOVA and interpret the results.
Discuss research papers on statistical methods
Show steps
Foster critical thinking, improve communication skills, and stay updated on the latest research in statistical methods
Browse courses on
Research Methods
Show steps
Show steps
Show steps
- Select a research paper on a statistical method relevant to the course.
- Read the paper and prepare a summary of the main findings.
- Join a peer discussion group and present the summary.
- Engage in discussions and exchange insights with fellow students.
Create a data visualization project
Show steps
Develop skills in data visualization, communication, and presenting statistical results effectively
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Collect and clean a dataset relevant to the course.
- Choose appropriate data visualization techniques to represent the data.
- Create a visually appealing and informative data visualization.
- Write a brief report interpreting the results and discussing the implications.
Mentor junior students in statistics
Show steps
Enhance understanding of statistical concepts by teaching others, improve communication and interpersonal skills
Show steps
Show steps
Show steps
- Volunteer as a mentor for a statistics club or organization.
- Provide guidance and support to junior students on statistical concepts and assignments.
- Create and share study materials or resources to assist students with their learning.
Career center
Learners who complete Applied Statistics: Mean Comparison using Python will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians work with data, often comparing different groups or samples to find meaningful conclusions. Therefore, a course on statistical methods for comparing means is highly relevant to the field of Statistics. The course will help learners enhance their understanding of these methods and, with practice, they will be able to apply them in their day-to-day work.
Data Scientist
Data Scientist
Data Scientists use statistical methods to analyze data, and a crucial part of data analysis often involves comparing the means of different groups using inferential statistics. Having a strong grasp of how to conduct mean comparisons, make visualizations, and draw conclusions from them would help a Data Scientist, especially one new to the field, excel at their job. This course will introduce learners to these concepts and equip them with tools to accurately come up with conclusions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical methods to analyze data and make predictions for financial institutions. Understanding how to compare means is a key skill for Quantitative Analysts, as they often need to compare the performance of different investments or portfolios. This course will provide learners with the necessary tools and techniques to perform mean comparisons in a financial context.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in the field of medicine and healthcare. They often need to compare different groups of patients to evaluate the effectiveness of a new treatment or procedure. This course will teach learners how to conduct mean comparisons in a medical context and draw meaningful conclusions from their findings.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data to understand consumer behavior and market trends. Mean comparison is a common technique used in market research to compare different groups of consumers or products. This course will provide learners with the skills and knowledge they need to conduct mean comparisons effectively and draw accurate conclusions from their research.
Data Analyst
Data Analyst
Data Analysts use statistical methods to analyze data and identify trends and patterns. Understanding how to compare means is a key skill for Data Analysts, as they often need to compare different groups of data or variables.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical methods to optimize business processes and operations. Understanding how to compare means is a key skill for Operations Research Analysts, as they often need to compare the performance of different systems or processes.
Epidemiologist
Epidemiologist
Epidemiologists study the causes and distribution of diseases in populations. They often need to compare different groups of people to identify risk factors for diseases or evaluate the effectiveness of public health interventions. This course will provide learners with the skills and knowledge they need to conduct mean comparisons in an epidemiological context.
Financial Analyst
Financial Analyst
Financial Analysts use statistical methods to analyze financial data and make recommendations for investments. Understanding how to compare means is a key skill for Financial Analysts, as they often need to compare the performance of different companies or investments. This course will provide learners with the necessary tools and techniques to perform mean comparisons in a financial context.
Economist
Economist
Economists use statistical methods to analyze economic data and make predictions about the economy. Understanding how to compare means is a key skill for Economists, as they often need to compare the economic performance of different countries or regions.
Actuary
Actuary
Actuaries use statistical methods to assess risk and uncertainty in the insurance and finance industries. Understanding how to compare means is a key skill for Actuaries, as they often need to compare the risk profiles of different groups of people or the performance of different insurance products. This course will provide learners with the necessary tools and techniques to perform mean comparisons in an actuarial context.
Business Analyst
Business Analyst
Business Analysts are often required to compare different business metrics, such as sales volumes, customer satisfaction levels, or marketing campaign results. A course on mean comparison will teach learners how to analyze and interpret data, draw conclusions, and make recommendations based on their findings, all of which are crucial skills for a Business Analyst.
Data Science Manager
Data Science Manager
Data Science Managers oversee teams of data scientists and analysts. They need to understand how to compare means in order to assess the performance of their team and make informed decisions.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers use statistical methods to analyze data related to medical devices and treatments. Understanding how to compare means is a key skill for Biomedical Engineers.
Software Engineer
Software Engineer
Software Engineers may use statistical methods to analyze and improve the performance of their software systems. Understanding how to compare means is a key skill for Software Engineers, as they often need to compare the performance of different algorithms or code optimizations.
For more career information including salaries, visit:
OpenCourser.com/course/l958s6/applied
Reading list
We've selected 14 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
Applied Statistics: Mean Comparison using Python.
Provides a comprehensive overview of statistical methods commonly used in psychology, including hypothesis testing, analysis of variance, and regression analysis. It valuable resource for students and researchers who need a strong foundation in statistical methods.
Provides a comprehensive overview of machine learning methods, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and researchers who want to learn about the latest advances in machine learning.
Provides a comprehensive overview of statistical methods, including descriptive statistics, inferential statistics, and regression analysis. It valuable resource for students and researchers who need a strong foundation in statistical methods.
Provides a comprehensive overview of mathematical statistics, including probability theory, statistical inference, and regression analysis. It valuable resource for students and researchers who need a strong foundation in mathematical statistics.
Provides a comprehensive overview of probability and statistics, including probability theory, statistical inference, and regression analysis. It valuable resource for students and researchers who need a strong foundation in probability and statistics.
Provides a comprehensive overview of statistical methods, including data collection, data analysis, and interpretation of results. It valuable resource for students and researchers who need to apply statistical methods to real-world problems.
Provides a comprehensive overview of statistical methods, including data collection, data analysis, and interpretation of results. It valuable resource for students and researchers who need a strong foundation in statistical methods.
Provides a comprehensive overview of statistical inference, including point estimation, interval estimation, and hypothesis testing. It valuable resource for students and researchers who need a strong foundation in statistical inference.
Provides a comprehensive overview of Bayesian data analysis, including Bayesian inference, Bayesian modeling, and Bayesian computation. It valuable resource for students and researchers who want to learn about the latest advances in Bayesian data analysis.
Provides a comprehensive overview of causal inference, including causal models, causal effects, and causal inference methods. It valuable resource for students and researchers who want to learn about the latest advances in causal inference.
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Provides a comprehensive overview of deep learning, including deep neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and researchers who want to learn about the latest advances in deep learning.
Provides a comprehensive overview of reinforcement learning, including reinforcement learning algorithms, reinforcement learning theory, and reinforcement learning applications. It valuable resource for students and researchers who want to learn about the latest advances in reinforcement learning.
Provides a comprehensive overview of natural language processing, including natural language processing techniques, natural language processing tools, and natural language processing applications. It valuable resource for students and researchers who want to learn about the latest advances in natural language processing.
Save
Provides a comprehensive overview of computer vision, including computer vision algorithms, computer vision techniques, and computer vision applications. It valuable resource for students and researchers who want to learn about the latest advances in computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l958s6/applied
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Svyatoslav Solodushkin
Good to know
Introduces methods which are standard in the analytics and machine learning industry
Builds foundational knowledge and skills for learners who want to work with statistical data and tests
Requires learners to have a working knowledge of statistics
Ideal for learners who want to apply statistical methods to marketing and business problems
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