Foundations of Data and Models: Regression Analytics
Course Info
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Course Details
Introduction
Objective
Who should attend
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Every forecast, prediction, and insight comes from a regression model that operates in the background.
Data science starts with understanding how data behaves and how to model it effectively. For those interested in learning more about data modeling, algorithmic thinking, and regression-driven problem solving, LPC Training's Foundations of Data and Models: Regression Analytics course is a great choice.
You will study both basic and advanced regression techniques over five full days. It talks about linear models and machine learning techniques like SVM, KNN, and XGBoost. You will get to work with Python in real life. You will also learn how to change parameters with genetic algorithms, grid search, and random search. You will also employ neural networks to solve hard regression problems.
The course is mostly on model reliability, error analysis, Bayesian inference, and how to design experiments. This method gives you both useful tools and a strong theoretical base, so you can be sure that your predictive models will be accurate.
Course Outline
Introduction to Data and Models
- Explaining data and modeling philosophy, and basic concepts
- Defining key statistical concepts and their uses:
- Distributions
- Mean
- Variance
- Understanding linear regression and the idea of fitting a line to data
- Introduction to Python and its uses for data modeling
- Exercise: Applying statistical concepts in Python or other statistical tools
