Statistical Simulation in Python

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(3)

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Simulations are a type if computational algorithm that use the simple idea random sampling to solve increasingly complex problems. Although simulations have been around since ancient times they have gained popularity with the rise in computational power. Simulations are used in many areas, including Artificial Intelligence, Physics, Computational Biology and Finance. To simulate data and generate them, NumPy will be used. Simulators with simple, real-world examples will be used to give students hands-on experience.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Statistical Thinking in Python (Part 2)

What You Will Learn

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

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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

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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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We will get a taste of bootstrap resampling, jackknife resampling, and permutation testing

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We'll work through a business planning problem, learn about Monte Carlo Integration, Power Analysis with simulation and conclude with a financial portfolio simulation

Course Instructors

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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 le...

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