Learn Statistics and Regression Modeling for Data Science
$19.99 Track price
A complete hands on practical exercises to learn statistics and build regression models which are highly used in business data analysis. This course is designed to start with the very basics and then add up information gradually till the professional level. Accordingly students who have fair background in statistics can choose to jump to the practical part of the course to learn building regression models in detail.
In this course you will learn descriptive and inference statistics, such as central and variability measures, visualize data, calculate confidence intervals and test hypotheses. Furthermore, you will learn to build different types of regression models and use them in data analysis. You will start first with Simple Linear Regression. After that Multiple Linear Regression when you use several independent variables to predict target values. After that you will learn Logistic Regression for classification. You will learn step by step how to understand a business problem from data observations and determine the variables you need to include in the regression analysis.
You will also learn how to interpret model coefficients from business point of view and assess regression model’s prediction power using several indicators, such as: R–squared and p–value. After that you will be able to prepare your business recommendations that can be used by decision makers. You will learn using important data analysis applications like Microsoft Excel, Gretl and R.
Courses : 3
Specification: Learn Statistics and Regression Modeling for Data Science
3 reviews for Learn Statistics and Regression Modeling for Data Science
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Easy yo understand without needing background in statistics.
Stuart Hendry –
This course is simple and to the point. It’s a really useful introduction to regression models.
Rob Whyte –
A good review and enjoyable course. I’d be happier with practice exercises that assigned a task and then provided answers and explanations. That’s the best way to learn. Most of the samples had low R–squared figures, which didn’t provide much variety.