# Statistical Simulation in Python

Learn to solve increasingly complex problems using simulations to generate and analyze data.

4 Hours16 Videos58 Exercises

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## 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.

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.
1. 1

### Basics of randomness & simulation

Free

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.

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Introduction to random variables
50 xp
np.random.choice()
50 xp
Poisson random variable
100 xp
Shuffling a deck of cards
100 xp
Simulation basics
50 xp
Throwing a fair die
100 xp
Throwing two fair dice
100 xp
Simulating the dice game
100 xp
Using simulation for decision-making
50 xp
Simulating one lottery drawing
100 xp
100 xp
Calculating a break-even lottery price
100 xp
2. 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. 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. 4

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.

Collaborators

Prerequisites

Sampling in Python
Tushar Shanker

Data Science Manager at Uber

Tushar currently leads the UberEats Marketing Data Science team at Uber, with a focus on improving global marketing efficiency across various channels like Facebook and Google. Before Uber, Tushar learned from and contributed to world class data science teams at Airbnb and LinkedIn. Tushar's career so far has involved combining his engineering background, MBA and PhD training in Platform Strategy & Economics to consistently help businesses solve complex problems. He has benefited from rigorous academic training with experts at Harvard, MIT and BU, in particular, with the platform guru Prof. Marshall Van Alstyne.
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