Statistics and Probability
Introduction to Probability and Data
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes’ rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization — source.
Inferential Statistics
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public — source.
A list of articles summarizes my notes on the above two courses from Statistics with R Specialization from Duke University.
These courses helped me a lot in making sense of the data, understanding and visualizing different data types, being able to tell if the observed trend is significant or not, and a lot more.
Statistics and probability serve as the foundation and building blocks for Data Science. We can’t really harness the data and make informed decisions without basic understanding of statistics and probability.
The list of articles in the series are as the following:
- A gentle introduction to statistics
- Exploratory Data Analysis
- Probability
- Probability Distribution
- The central limit theorem and confidence interval
- Hypothesis Testing
- T-distribution & ANOVA
- Inference for proportions
- Inference based on simulation
It’s worth mentioning that these articles will be kept updated when there is a bug, misspelling, grammar mistake, or any other issue.
Moreover, I can’t take credit for most of what’s written in these articles. As I mentioned, these are my personal notes, and most of it are summarizing and paraphrasing the video notes.
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