Hands On Training

Statistical Simulation in Python

DataCamp

(3)

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

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.

Highlights

Rating & Reviews

Top 30 Percentile

Pedagogy

Top 30 Percentile

Parameters

Pedagogy

Acquire all major Python Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in Python Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

Rating & Reviews

This highly acclaimed course is among the top-rated in Python Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in Python Programming.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Statistical Thinking in Python (Part 2)

What You Will Learn

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

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

We'll look at a number of examples of modeling the data generating process and will conclude with modeling an eCommerce advertising simulation

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

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 Content

Expand all sections

Module 1: Basics of randomness simulation

Module 2: Probability data generation process

Module 3: Resampling methods

Module 4: Advanced Applications of Simulation

Course Instructors

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...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%