Statistical Simulation in Python
Learn to solve increasingly complex problems using simulations to generate and analyze data.
- 4 hours
- 16 Videos
- 58 Exercises
- 3,171 Participants
- 4,800 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.
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1
Basics of randomness & simulation
FreeThis 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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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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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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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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1
Basics of randomness & simulation
FreeThis 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.
-
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.
-
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.
-
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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Tushar Shanker
Data Scientist at Uber
Tushar currently works at Uber where he leads the data science effort to optimize spend on advertising channels. Tushar's career so far has involved combining his background in engineering, his business acumen and doctoral training in economics to consistently help businesses solve complex problems. He has benefitted from rigorous academic training with experts at Harvard, MIT and BU - in particular the platform guru Prof. Marshall Van Alstyne. In addition, he has learned from and contributed to the world class data science teams at Airbnb and LinkedIn.
See MoreCollaborators
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Lore Dirick
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Becca Robins
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Sara Snell