Introduction to Linear Modeling in Python

Mathematics and Statistics

Short Description

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

Long Description

Scientists strive to identify patterns in data and construct models that can describe, predict, and extract valuable insights from these patterns. Among the fundamental patterns, a linear relationship between two variables stands out. This course aims to introduce participants to the exploration, quantification, and modeling of linear relationships in data. It accomplishes this by showcasing techniques such as least-squares, linear regression, estimation, and bootstrap resampling. Throughout the course, participants will have the opportunity to utilize powerful modeling tools within the python data science ecosystem, including scipy, statsmodels, and scikit-learn. By delving into the concepts and applications of linear models using python, this course serves as a practical introduction to modeling while also establishing a solid foundation for further exploration of advanced modeling techniques and tools in statistics and machine learning.

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Course Details

Duration: 4 hours
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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