Course
Statistical Simulation in Python
IntermediateSkill Level
Updated 12/2023PythonProbability & Statistics4 hr16 videos58 Exercises4,800 XP19,761Statement of Accomplishment
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Loved by learners at thousands of companies
Training 2 or more people?
Try DataCamp for BusinessCourse Description
Get to Grips with Random Variables
Simulations are a class of computational algorithms that use random sampling to solve increasingly complex problems. Although simulations have been around for a long time, interest in this area has recently grown due to the rise in computational power and the applications across Artificial Intelligence, Physics, Computational Biology and Finance just to name a few.This course provides hands-on experience with simulations using real-world applications, starting with an introduction to random variables and the tools you need to run a simulation.
Gain an Introduction to Probability Concepts
The second chapter in this course provides an overview of probability concepts, using practice exercises based on card games and well-known probability puzzles to provide a framework for your new knowledge. You’ll finish this chapter by modeling an eCommerce advertising simulation.Discover Resampling Methods and Applications
The third chapter looks at different resampling methods, including bootstrap resampling, jackknife resampling, and permutation testing. Once you’ve completed this course, you’ll be able to add these methods to your data analysis process.Learn to Use Simulation for Business and Build Your Portfolio
Simulation has many real-world applications, especially in the world of business. The final chapter in this course looks at these, and takes you through a business planning problem to get you used to using your new skills in a business setting. You’ll look at modeling profits, optimizing costs, and getting started with power analysis.Prerequisites
Sampling in Python1
Basics of Randomness & Simulation
This chapter gives you the tools required to run a simulation. We'll start with a review of random variables and probability distributions. We will then learn how to run a simulation by first looking at a simulation workflow and then recreating it in the context of a game of dice. Finally, we will learn how to use simulations for making decisions.
2
Probability & Data Generation Process
This chapter provides a basic introduction to probability concepts and a hands-on understanding of the data generating process. We'll look at a number of examples of modeling the data generating process and will conclude with modeling an eCommerce advertising simulation.
3
Resampling Methods
In this chapter, we will get a brief introduction to resampling methods and their applications. We will get a taste of bootstrap resampling, jackknife resampling, and permutation testing. After completing this chapter, students will be able to start applying simple resampling methods for data analysis.
4
Advanced Applications of Simulation
In this chapter, students will be introduced to some basic and advanced applications of simulation to solve real-world problems. We'll work through a business planning problem, learn about Monte Carlo Integration, Power Analysis with simulation and conclude with a financial portfolio simulation. After completing this chapter, students will be ready to apply simulation to solve everyday problems.
Statistical Simulation in Python
Course Complete
Earn Statement of Accomplishment
Add this credential to your LinkedIn profile, resume, or CVShare it on social media and in your performance reviewEnroll Now
FAQs
What is resampling in data science?
Resampling is the process whereby you may start with a dataset in your typical workflow, and then apply a resampling method to create a new dataset that you can analyze to estimate a particular quantity of interest. You can resample multiple times to get multiple values. There are several types of resampling, including bootstrap and jackknife, which have slightly different applications.
Is this course suitable for beginners?
This course requires existing knowledge of both Python and sampling. We recommend that you take our Sampling in Python course before starting to ensure that you have a strong understanding of the key concepts and processes.
Join over 19 million learners and start Statistical Simulation in Python today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Grow your data skills with DataCamp for Mobile
Make progress on the go with our mobile courses and daily 5-minute coding challenges.