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- Statistical Modeling

- Data Science in Real Life: Johns Hopkins University
- Modern Regression Analysis in R: University of Colorado Boulder
- ANOVA and Experimental Design: University of Colorado Boulder
- Generalized Linear Models and Nonparametric Regression: University of Colorado Boulder
- Fitting Statistical Models to Data with Python: University of Michigan
- Integral Calculus and Numerical Analysis for Data Science: University of Colorado Boulder
- Building Statistical Models in R: Linear Regression: Coursera Project Network
- Regression and Classification: University of Colorado Boulder
- Decision Tree Classifier for Beginners in R: Coursera Project Network
- IBM Data Science: IBM Skills Network

Statistical modeling is the process of using statistics to analyze data and make it usable for decision-making and analysis. It involves using computer programs to pull out numbers from data sets, identify trends, make predictions, and understand different situations. Most statistical modeling results in charts, graphs, or other types of reports to explain the data.

It's important to learn statistical modeling for many reasons to comprehend related material as a student and fulfill work-related requirements. Students often need to learn it because it's used in many different academic fields to understand the material. Many occupations either involve doing statistical analysis or being able to use the output in order to make decisions. Because of the way that data is reported in the media, it's helpful for members of the general public to have some understanding of how statistical modeling works.

Typical careers that use statistical modeling are medical research, scientific research, and financial analysis, but it is a component of many different fields. People working in marketing and sales use statistical modeling to build forecasts and generate prospects. Business managers use it to make decisions and to share information. Academics use it to understand trends and patterns in their fields. Journalists use statistical modeling to present information in their stories. By turning data into usable information, including charts and graphs, statistical modeling can add value to many different fields. People who understand the fundamentals of quantitative modeling and statistical model can expect the skill to enhance their careers.

Taking online courses on Coursera can help you learn statistical modeling in different ways, depending on your interests and needs. Some courses cover the fundamentals of statistics, which may be helpful if you have little background in the area. Others look at how statistical modeling is done with different software packages, such as Python, Excel, or SAS. Many classes focus on the uses of statistical modeling in specific fields, such as artificial intelligence, finance, or history. All classes include projects that help you apply what you learn.

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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