Unlocking Data Insights: Statistics with R and Python
Welcome to “Statistics with R and Python,” your gateway to mastering the art and science of data analysis with Ai Tools Engeneering- In today’s data-driven world, the ability to extract meaningful insights is crucial, and this course provides you with the skills to do so, leveraging two of the most powerful tools in a data professional’s arsenal: R and Python. This course is meticulously designed for hands-on learning. You’ll begin by building a solid foundation in descriptive statistics and data visualization, transforming raw data into compelling narratives using libraries like ggplot2, Matplotlib, and Seaborn. We then delve into inferential statistics, guiding you through the principles of probability, hypothesis testing, and confidence intervals, enabling you to draw valid conclusions from your data. A significant portion of the course is dedicated to regression analysis, where you’ll learn to build and interpret linear and logistic models for forecasting and understanding relationships. Through hands-on exercises and real-world case studies, you’ll gain expertise in data cleaning, manipulation, and analysis workflows. By the end of this journey, you’ll not only understand statistical concepts but also possess the practical coding skills in both R and Python to effectively apply them across various domains. Join us to transform data into actionable insights! Use data with AI apps to build reliable statistical predictions and get closer to the world of machine learning.“This course contains the use of artificial intelligence.”
What you’ll learn
- Introduction to Data and Programming Environments.
- Descriptive Statistics.
- Probability and Probability Distributions.
- Sampling and Estimation.
- Hypothesis Testing Fundamentals.
- Comparing Groups.
- Categorical Data Analysis.
- Correlation and Regression.
Course Content
- Introduction to Data and Programming Environments –> 4 lectures • 19min.
- Covariance – From Theory to Practise –> 2 lectures • 20min.
- Normal Distribution –> 2 lectures • 24min.
- Correlation and Regression Data Analysis –> 6 lectures • 24min.
- Probability and Probability Distributions –> 4 lectures • 12min.
- Hypothesis Testing Fundamentals –> 2 lectures • 5min.
- Descriptive Statistics –> 6 lectures • 25min.
- Comparing Groups –> 4 lectures • 20min.
- Categorical Data Analysis –> 4 lectures • 28min.
Requirements
Welcome to “Statistics with R and Python,” your gateway to mastering the art and science of data analysis with Ai Tools Engeneering- In today’s data-driven world, the ability to extract meaningful insights is crucial, and this course provides you with the skills to do so, leveraging two of the most powerful tools in a data professional’s arsenal: R and Python. This course is meticulously designed for hands-on learning. You’ll begin by building a solid foundation in descriptive statistics and data visualization, transforming raw data into compelling narratives using libraries like ggplot2, Matplotlib, and Seaborn. We then delve into inferential statistics, guiding you through the principles of probability, hypothesis testing, and confidence intervals, enabling you to draw valid conclusions from your data. A significant portion of the course is dedicated to regression analysis, where you’ll learn to build and interpret linear and logistic models for forecasting and understanding relationships. Through hands-on exercises and real-world case studies, you’ll gain expertise in data cleaning, manipulation, and analysis workflows. By the end of this journey, you’ll not only understand statistical concepts but also possess the practical coding skills in both R and Python to effectively apply them across various domains. Join us to transform data into actionable insights! Use data with AI apps to build reliable statistical predictions and get closer to the world of machine learning.“This course contains the use of artificial intelligence.”