Topics to be Read from Probability for Data Science

Probability is a way of measuring how likely something is to happen. It is a measure of the likelihood of an event occurring. More formally, it is a numerical measure that quantifies the likelihood of a particular outcome or set of outcomes occurring in an uncertain situation.

1. Probability Fundamentals:

i. Sample space and events

ii.Basic probability axioms

iii. Types of Probability

iii. Conditional Probability

iv. Types of Events

v. Complement rule

vi. Addition rule

vii.Multiplication rule

2. Bayesian Probability:

i. Bayes’ theorem

ii. Prior likelihood and posterior probabilities

iii. Bayesian inference

3. Probability Distributions:

i. Discrete probability distributions (e.g., Bernoulli, binomial, Poisson)

ii. Continuous probability distributions (e.g., normal, exponential)

iii. Probability mass function (PMF)

iv. Probability density function (PDF)

v. Cumulative distribution function (CDF)

4. Central limit theorem:

These topics form the backbone of probability theory in the context of data science, providing essential tools for analyzing data, building models, and making predictions.

https://medium.com/pythons-gurus/data-science-roadmap-2024-9473f5c7372e

Give it :👏👏👏👏:
If you found this guide helpful , why not show some love? Give it a Clap 👏, and if you have questions or topics you’d like to explore further, drop a comment 💬 below 👇. If you appreciate my hard work please follow me. That is the only way I can continue my passion.