Dive into a comprehensive series of statistics tutorials covering essential concepts for data science. Learn about measures of variability, population vs sample distinctions, levels of measurement, and central tendency. Explore skewness, hypothesis testing, and data visualization techniques with guidance on selecting appropriate chart types. Delve into clustering methods, including flat, hierarchical, and K-means, understanding their pros and cons. Gain insights into categorical and numerical data types, probability fundamentals, correlation and regression analysis, and the decomposition of variability. Master the linear regression model, normal distribution, and other key statistical concepts crucial for aspiring data scientists.
Overview
Syllabus
Variance, Standard Deviation, Coefficient of Variation.
Population vs Sample.
Data Science & Statistics: Levels of measurement.
Statistics Tutorials: Mean, median and mode.
Skewness.
Hypothesis testing. Null vs alternative.
Which is the best chart: Selecting among 14 types of charts Part I.
Which is the best chart: Selecting among 14 types of charts Part II.
Flat and Hierarchical Clustering | The Dendrogram Explained.
K Means Clustering: Pros and Cons of K Means Clustering.
Types of Data: Categorical vs Numerical Data.
Introduction to Probability | 365 Data Science Online Course.
The Differences Between Correlation and Regression | Statistics Tutorials.
Decomposition of Variability: Sum of Squares | Statistics Tutorial.
The linear regression model.
The Normal Distribution.
What is a distribution?.
Simple linear regression model. Geometrical representation.
Taught by
365 Data Science
