Understanding Statistical Models and Mathematical Models
By Janani Ravi
Course info
Course info
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
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.
More from the author
Section Introduction Transcripts
Course Overview
[Autogenerated] Hi, My name is Johnny Ravi, and welcome to this course on building statistical summaries with are a little about myself. I have a masters in electrical engineering from Stanford, and I've worked at companies such as Microsoft, Google and Flip Card at Google. I was one of the first engineers working on real time collaborative editing in Google Dogs and I hold four patterns for its underlying technologies. I currently work on my own startup, Loony Con, a studio for high quality video content. Data science and data modeling are fast emerging is crucial capabilities that every enterprise and every technologists must possess. These these 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 off 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 eight queens problem using both classic local search as well, assimilated and kneeling. You will also learn how statistically models are great for modeling systems, with randomness using business based use cases from risk management and the use of Monte Carlo simulations. Finally, URL 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 and kneeling and classic hill climbing, as well as hypothesis, testing and statistical tests such as the T test.