Sampling in R

Mathematics and Statistics

Short Description

Master sampling to get more accurate statistics with less data.

Long Description

Sampling is a fundamental aspect of inference statistics and hypothesis testing, playing a crucial role in survey analysis and experimental design. This comprehensive course delves into the significance of sampling, providing insights into its purpose and relevance. It equips learners with the knowledge and skills to execute various sampling techniques, ranging from simple random sampling to more intricate methods such as stratified and cluster sampling. Additionally, the course delves into estimating population statistics and effectively measuring uncertainty in these estimates through the generation of sampling distributions and bootstrap distributions. To enhance understanding, real-world datasets on coffee ratings, Spotify songs, and employee attrition are explored throughout the course.

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Course Details

Duration: 4 hours
Difficulty: Intermediate
Format: Short Course
Price: USD39.00
Course Link

DataCamp

DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.

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