# Foundations of Probability in Python
This is a DataCamp course: Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
## Course Details
- **Duration:** ~5h
- **Level:** Intermediate
- **Instructor:** Alexander A. Ramírez M.
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Probability & Statistics, Data Science and Analytics
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Introduction to Statistics in Python
## Learning Outcomes
- Python
- Probability & Statistics
- Data Science and Analytics
- Foundations of Probability in Python
## Traditional Course Outline
1. Let's start flipping coins - A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.
2. Calculate some probabilities - In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.
3. Important probability distributions - Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.
4. Probability meets statistics - No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions. We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.
## Resources and Related Learning
No public datasets, resources, or related tracks are listed for this course.
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Course
Foundations of Probability in Python
IntermediateSkill Level
Updated 08/2024PythonProbability & Statistics5 hr16 videos61 Exercises5,050 XP15,635Statement of Accomplishment
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Prerequisites
Introduction to Statistics in Python1
Let's start flipping coins
A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.
2
Calculate some probabilities
In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.
3
Important probability distributions
Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.
4
Probability meets statistics
No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions.We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.
Foundations of Probability in Python
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FAQs
What probability distributions does this course cover?
You learn about the binomial, normal, Poisson, and geometric distributions. The course starts with coin flip experiments and builds up to more complex distributions.
Which Python library is used for probability simulations?
The course uses the scipy library to simulate probability experiments, alongside pandas for data manipulation and standard Python for calculations.
Does this course connect probability to data science applications?
Yes. The final chapter explores how the law of large numbers and central limit theorem apply to real problems, including connections to linear and logistic regression.
What math background do I need for this course?
You should have completed Introduction to Statistics in Python. The course explains concepts like mean, variance, and conditional probability from the ground up using practical examples.
How large is this course in terms of content?
It has 4 chapters with 61 exercises and over 5,000 XP. Learners typically spend about 4 to 5 hours completing it.
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