Understanding Statistical Models and Mathematical Models

This course covers important techniques from both mathematical and statistical modeling, including the use of ordinary differential equations to model deterministic systems, classic local search and simulated annealing to explore large search spaces.
Course info
Level
Beginner
Updated
Dec 17, 2019
Duration
2h 36m
Table of contents
Course Overview
Understanding Statistical and Mathematical Models
Case Studies on Statistical and Mathematical Models
Applying Mathematical Models in R
Applying Statistical Models in R
Description
Course info
Level
Beginner
Updated
Dec 17, 2019
Duration
2h 36m
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Description

Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist and it us important to choose the type of model most appropriate to your use-case. In this course, Understanding Statistical Models and Mathematical Models, you will gain the ability to differentiate between mathematical models and statistical models and pick the right type of model for your scenario.

First, you will learn the important characteristics of mathematical and statistical models and their applications. Next, you will discover how classic mathematical models find wide applicability in solving differential equations and modeling deterministic systems.

Then, you will also learn how statistical models are great for modeling systems with randomness, using business-based use-cases from risk management, and the use of Monte Carlo simulations. Finally, you will round out your knowledge performing hypothesis testing using T-tests and Z-tests on real-world data.

When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from both mathematical and statistical modeling, including solving simple ordinary differential equations, the use of simulated annealing and classic hill climbing, as well as hypothesis testing and statistical tests such as the T-test.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi, and welcome to this course on building statistical summaries with R. A little about myself. I have a master's in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real‑time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high‑quality video content. Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and modeling tools, so it's becoming very important to choose the type of model most appropriate to your use case. In this course, you will gain the ability to differentiate between mathematical models and statistical models and pick the right type of model for your scenario. First, you will learn important characteristics of mathematical and statistical models and their applications. Next, you will discover how classic mathematical models find wide applicability in solving differential equations and modeling deterministic systems, such as in solving the 8‑queens problem using both classic local search, as well simulated annealing. You will also learn how statistical models are great for modeling systems with randomness using business‑based use cases from risk management and the use of Monte Carlo simulations. Finally, you'll round out your knowledge performing hypothesis testing using t‑tests and z‑tests on real‑world data. When you're finished with this course, you will have the skills and knowledge to use powerful techniques from both mathematical and statistical modeling, including solving simple ordinary differential equations, the use of simulated annealing and classic hill climbing, as well as hypothesis testing and statistical tests such as the t‑test.