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David Dalsveen
By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups.
Read more
By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups.
Read more
By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups.
The T-Test is well known in the field of statistics. It is used to test a hypothesis using a set of data sampled from the population. To perform the T-Test, the population sample size, the mean, or average, of each population, and the standard deviation are all required. These will all be calculated in this project.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/4x0zp8/statistical
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What's inside
Syllabus
Statistical Analysis using Python Numpy
By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes practical application of statistical principles within programming
Provides a solid foundation in Python Numpy for statistical analysis
Suitable for learners with a background in statistics and programming
May not be suitable for complete beginners in statistics or programming
Focuses on T-Test, which may limit its applicability to broader statistical analyses
Save this course
Save Statistical Analysis using Python Numpy to your list so you can find it easily later:
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Reviews summary
Engaging guided projects
Learners say that this course has well-organized and engaging guided projects. Students have reported that they enjoyed this course.
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 Statistical Analysis using Python Numpy with these
activities:
Review statistics
Show steps
Brushing up on your statistics skills will help you better understand the statistical concepts covered in this course.
Browse courses on
Statistics
Show steps
-
Review basic concepts of statistics, such as mean, median, and standard deviation.
-
Practice solving statistical problems.
Revisit Python Programming Basics
Show steps
Recall key concepts, syntax, and data structures of Python programming to prepare for learning about statistical analysis in Python.
Browse courses on
Python
Show steps
-
Review core Python concepts and data types.
-
Practice basic Python operations (e.g., arithmetic, string manipulation).
-
Revisit Python control structures (e.g., loops, conditionals).
Attend a Data Science Workshop
Show steps
Participate in a workshop led by experts, gaining insights into practical applications of statistical analysis, including hands-on exercises.
Browse courses on
Statistical Analysis
Show steps
-
Research and find a relevant data science workshop.
-
Attend the workshop and actively participate.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Data Manipulation with Numpy
Show steps
Strengthen skills in data manipulation and analysis using Numpy's functions for mathematical operations, array handling, and statistical calculations.
Browse courses on
NumPy
Show steps
-
Explore Numpy array creation and operations.
-
Practice data aggregation and statistical calculations with Numpy functions.
Discuss and Compare Statistical Methods
Show steps
Engage in discussions with peers to exchange different perspectives on statistical methods, solidifying understanding of the topic.
Browse courses on
t-Test
Show steps
-
Present a different statistical method (e.g., ANOVA, regression).
-
Compare and contrast the method with T-Test.
Explore Advanced Numpy Features
Show steps
Enhance proficiency in Numpy by learning advanced techniques, expanding analytical capabilities for statistical analysis.
Browse courses on
NumPy
Show steps
-
Explore vectorization and broadcasting.
-
Learn advanced array manipulation techniques.
Analyze Student Data using T-Test
Show steps
Demonstrate understanding of T-Test principles and their application in analyzing real-world data, providing insights into student performance.
Show steps
-
Prepare and clean the student data.
-
Conduct T-Test analysis on the data.
-
Interpret the results and draw conclusions.
-
Create a report presenting the findings.
Develop a Data Analysis Tool
Show steps
Apply knowledge and skills learned in the course to create a functional tool that automates data analysis tasks, promoting project-based learning.
Browse courses on
Python Programming
Show steps
-
Identify a specific data analysis problem.
-
Design and develop a tool using Python and Numpy.
-
Test and refine the tool.
Review statistics
Show steps
Brushing up on your statistics skills will help you better understand the statistical concepts covered in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review basic concepts of statistics, such as mean, median, and standard deviation.
- Practice solving statistical problems.
Revisit Python Programming Basics
Show steps
Recall key concepts, syntax, and data structures of Python programming to prepare for learning about statistical analysis in Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review core Python concepts and data types.
- Practice basic Python operations (e.g., arithmetic, string manipulation).
- Revisit Python control structures (e.g., loops, conditionals).
Attend a Data Science Workshop
Show steps
Participate in a workshop led by experts, gaining insights into practical applications of statistical analysis, including hands-on exercises.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Research and find a relevant data science workshop.
- Attend the workshop and actively participate.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Data Manipulation with Numpy
Show steps
Strengthen skills in data manipulation and analysis using Numpy's functions for mathematical operations, array handling, and statistical calculations.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Explore Numpy array creation and operations.
- Practice data aggregation and statistical calculations with Numpy functions.
Discuss and Compare Statistical Methods
Show steps
Engage in discussions with peers to exchange different perspectives on statistical methods, solidifying understanding of the topic.
Browse courses on
t-Test
Show steps
Show steps
Show steps
- Present a different statistical method (e.g., ANOVA, regression).
- Compare and contrast the method with T-Test.
Explore Advanced Numpy Features
Show steps
Enhance proficiency in Numpy by learning advanced techniques, expanding analytical capabilities for statistical analysis.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Explore vectorization and broadcasting.
- Learn advanced array manipulation techniques.
Analyze Student Data using T-Test
Show steps
Demonstrate understanding of T-Test principles and their application in analyzing real-world data, providing insights into student performance.
Show steps
Show steps
Show steps
- Prepare and clean the student data.
- Conduct T-Test analysis on the data.
- Interpret the results and draw conclusions.
- Create a report presenting the findings.
Develop a Data Analysis Tool
Show steps
Apply knowledge and skills learned in the course to create a functional tool that automates data analysis tasks, promoting project-based learning.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Identify a specific data analysis problem.
- Design and develop a tool using Python and Numpy.
- Test and refine the tool.
Career center
Learners who complete Statistical Analysis using Python Numpy will develop knowledge and skills
that may be useful to these careers:
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to biological and medical data. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to perform T-Tests, individuals can gain valuable experience in data analysis and interpretation.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to calculate population sample size, mean, and standard deviation, individuals can build a solid foundation for data analysis.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of diseases. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to calculate population sample size, mean, and standard deviation, individuals can build a strong foundation for epidemiological research.
Statistician
Statistician
Statisticians apply statistical methods to collect, analyze, interpret, and present data. This course can provide a strong foundation for a career in this field by teaching the statistical capabilities of the Python Numpy package. Individuals will learn how to perform T-Tests, which are commonly used in statistical analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to calculate population sample size, mean, and standard deviation, individuals can build a foundation for success in this field.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to calculate population sample size, mean, and standard deviation, individuals can build a strong foundation for financial analysis.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to perform T-Tests, individuals can gain valuable experience in data analysis and interpretation.
Actuary
Actuary
Actuaries use statistical methods to assess risk and uncertainty. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to perform T-Tests, individuals can gain valuable experience in data analysis and interpretation.
Market Researcher
Market Researcher
Market Researchers conduct surveys and analyze data to understand consumer behavior. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to perform T-Tests, individuals can gain valuable experience in data analysis and interpretation.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines and systems. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to calculate population sample size, mean, and standard deviation, individuals can build a solid foundation for data engineering.
Data Scientist
Data Scientist
A Data Scientist uses their knowledge of statistics and programming to interpret large datasets. This course's focus on statistical analysis using Python's Numpy package can help build a foundation for a career in this field. By learning how to calculate population sample size, mean, and standard deviation, individuals can develop the skills necessary for data analysis and interpretation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help individuals develop the skills necessary for this role by teaching them how to use Python's Numpy package for statistical analysis. By learning how to perform T-Tests, individuals can gain valuable experience in data analysis and interpretation.
For more career information including salaries, visit:
OpenCourser.com/course/4x0zp8/statistical
Reading list
We've selected ten 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
Statistical Analysis using Python Numpy.
Provides a comprehensive guide to using Python for data analysis. It covers topics such as data cleaning, exploration, and visualization. It valuable resource for learners who want to learn more about Python for data analysis.
Provides a comprehensive guide to data mining with Python. It covers topics such as data cleaning, feature engineering, and model building. It valuable resource for learners who want to learn more about data mining.
Provides a comprehensive guide to statistical learning. It covers topics such as linear regression, logistic regression, and decision trees. It valuable resource for learners who want to learn more about statistical learning.
Provides a hands-on guide to machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for learners who want to learn more about machine learning.
Provides a comprehensive guide to machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for learners who want to learn more about machine learning.
Provides a comprehensive introduction to statistical learning, the field of using data to learn about the world. It covers topics such as linear regression, logistic regression, and decision trees. It valuable resource for learners who want to learn more about statistical learning.
Provides a comprehensive introduction to machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for learners who want to learn more about machine learning.
Provides a comprehensive introduction to Bayesian data analysis. It covers topics such as probability theory, Bayesian inference, and statistical modeling. It valuable resource for learners who want to learn more about Bayesian data analysis.
Provides a comprehensive guide to deep learning with Python. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for learners who want to learn more about deep learning.
Provides a comprehensive introduction to Bayesian statistics. It covers topics such as probability theory, Bayesian inference, and statistical modeling. It valuable resource for learners who want to learn more about Bayesian statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4x0zp8/statistical
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
David Dalsveen
Language
English
Good to know
Emphasizes practical application of statistical principles within programming
Provides a solid foundation in Python Numpy for statistical analysis
Suitable for learners with a background in statistics and programming
May not be suitable for complete beginners in statistics or programming
Focuses on T-Test, which may limit its applicability to broader statistical analyses
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