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Introduction to Statistics
Basics of statistics and key concepts

Descriptive statistics
Summarizing and interpreting data

Random variables
Understanding variables and probability

Distributions
Different types of statistical distributions

%

Probability
Fundamentals of probability theory

Point estimation and CI
Estimating parameters with confidence

α

Hypothesis testing
Testing statistical hypotheses

Sampling
Methods of selecting data samples

Bootstrap (resampling)
Resampling techniques in statistics

Regression and classification
ML techniques for prediction and clustering

Time series
Analyzing time-dependent data

Optimization
Techniques to optimize functions and models

Glossary Tables
Categories
Introduction to Statistics Descriptive statistics Random variables Distributions Probability Point estimation and CI Hypothesis testing Sampling Bootstrap (resampling) Regression and classification Time series Optimization
Glossary
Tables
R & Python

R

R CODER
From introductory to advanced R tutorials with examples

R

R CHARTS
Learn data visualization with base R and ggplot2

R

R PACKAGES
Explore all R packages available, functions and datasets

PY

PYTHON CHARTS
Learn data visualization with matplotlib, seaborn and plotly
R & Python
R CODER R CHARTS R PACKAGES PYTHON CHARTS
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INTRODUCTION TO STATISTICS

Learn what statistics is and what it is used for, along with basic concepts such as population, individuals, census, and sample, as well as the difference between quantitative and qualitative variables

What is Statistics?

What does Statistics mean? What are its objectives and applications? Learn the core definitions and how statistics is applied in the real world.

Population, individuals, census and sample

Every statistical study starts by defining who or what you are studying, and how to select a representative subset of it

Quantitative and qualitative variables

The type of variable you measure determines which charts, summaries, and tests are valid. Learn to tell them apart