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Statistical Simulation in Python

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

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4 Hours16 Videos58 Exercises15,755 Learners4800 XP

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Course Description

Simulations are a class of computational algorithms that use the relatively simple idea of random sampling to solve increasingly complex problems. Although they have been around for ages, they have gained in popularity recently due to the rise in computational power and have seen applications in multiple domains including Artificial Intelligence, Physics, Computational Biology and Finance just to name a few. Students will use simulations to generate and analyze data over different probability distributions using the important NumPy package. This course will give students hands-on experience with simulations using simple, real-world applications.

  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
    Should we buy?
    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.

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  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.

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  4. 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.

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Collaborators

lore
Lore Dirick
beccarobins
Becca Robins
sarasnell
Sara Snell

Prerequisites

Sampling in Python
Tushar Shanker Headshot

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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