Statistics Fundamentals with Python

After all the work that went into getting data and converting it into an accessible format, you want to be able to draw concise and clear conclusions. This is the final step in any data analysis process. This course will teach you how to understand data, build a foundation for statistical thinking and interpret the information it gives you. The foundations of statistical thinking are now easier than ever thanks to computers. By the end of this course, you will be proficient in Python-based tools.

Part 1 of Statistical Thinking In Python will give you the foundational skills to be a hacker statistician and a probabilistic mindset that can help you dig into data and extract useful information. This course will teach you how to do just that. This course will help you to improve your hacker statistics skills, including hypothesis testing, parameter estimation, statistical inference and hypothesis testing. You will learn by working with real data, culminating in the analysis and interpretation of finches' beaks. This course will give you the skills and knowledge to solve your own inference problems.

Scientists are driven by one goal: To identify patterns in data and build models that can predict and describe them. The most basic pattern is a linear relationship between two variables. This course will teach you how to explore, quantify and model linear relationships in data. This course will cover techniques like least squares, linear regression, and estimation. This course will show you how to make the most of the Python data science ecosystem's most powerful tools. This course will teach you how to use python to create and analyze linear models. This course also gives you an introduction to modeling and provides the foundation for advanced techniques in statistics, machine-learning, and machine-learning.

Simulations are a type if computational algorithm that use the simple idea random sampling to solve increasingly complex problems. Although simulations have been around since ancient times they have gained popularity with the rise in computational power. Simulations are used in many areas, including Artificial Intelligence, Physics, Computational Biology and Finance. To simulate data and generate them, NumPy will be used. Simulators with simple, real-world examples will be used to give students hands-on experience.

This course is designed for individuals who have completed Statistical Thinking I & II and are looking further to develop their statistical analysis skills in a real-world setting. The course covers various topics, such as exploratory data analysis, parameter estimation, and statistical analysis using Python.

One of the key areas of focus in this course is the examination of data from 2013 and 2015 to determine the relative speed of swimmers. This analysis will use statistical techniques to analyze the controversy surrounding the 2013 Worlds, where swimmers claimed a slight current.

Another topic covered in this course is the study of earthquake frequency and magnitude worldwide. Specifically, the course will explore the rise in seismicity in the US, particularly in Oklahoma, and its correlation with high-pressure wastewater injections at oil extraction sites.

Throughout this course, students can expand their understanding of statistics and data analysis by working with real-world datasets. Students will gain valuable insights from these datasets by applying statistical thinking skills and utilizing Python.

Overall, this course aims to equip students with the necessary skills and mindset needed to extract valuable insights from data through statistical analysis. By completing this course, students will be able to apply their statistical thinking skills and exploratory data analysis techniques to real-world scenarios.

Statistics knowledge is essential for interpreting and reporting on data. These courses will teach you how to evaluate statistical models, simulate data and draw conclusions from a variety of data sets.