Statistics is generally divided into **two branches:**

- Theoretical Statistics
- Applied Statistics
- Inferential Statistics
- Descriptive Statistics

We will cover Applied Statistics in this post i.e., Inferential Statistics and Descriptive Statistics.

Types of Statistics for Data Science

1. Descriptive Statistics

Descriptive statistics is used to presenting, organizing and summarize data.

2. Inferential Statistics

Inferential statistics is used to draw conclusion about a population based on data observed in a sample.

## Descriptive Statistics

Use descriptive statistics to summarize and graph the data for a group that you choose. This process allows you to understand that specific set of observations.

Descriptive statistics frequently use the following statistical measures to describe groups:

**Central tendency**: Use mean, mode or median.**Dispersion/ Variability/ Spread**: How far out data is spread from the center, i.e. range, variance, standard deviation, etc..**Curve Types:**Skewness (whether the distribution is symmetric or skewed) and Kurtosis.

## Inferential Statistics

Inferential statistics takes data from a sample and makes inferences about the larger population from which the sample was drawn. Because the goal of inferential statistics is to draw conclusions from a sample and generalize them to a population, we need to have confidence that our sample accurately reflects the population.

- Probability Basics
- Probability Distributions
- Central Limit Theorem
- Hypothesis testing
- Distributions

**Numerical**(or Quantitative) data- Discrete data
- Continuous data

**Categorical**(or Qualitative) data- Nominal data
- Ordinal data