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Linear regression is the primary workhorse in statistics and data science. Its high degree of flexibility allows it to model very different problems. We will review the theory, and we will concentrate on the R applications using real world data (R is a free statistical software used heavily in the industry and academia). We will understand how to build a real model, how to interpret it, and the computational technical details behind it. The goal is to provide the student the computational knowledge necessary to work in the industry, and do applied research, using lineal modelling techniques. Some basic knowledge in statistics and R is recommended, but not necessary. The course complexity increases as it progresses: we review basic R and statistics concepts, we then transition into the linear model explaining the computational, mathematical and R methods available. We then move into much more advanced models: dealing with multilevel hierarchical models, and we finally concentrate on nonlinear regression. We also leverage several of the latest R packages, and latest research. We focus on typical business situations you will face as a data scientist/statistical analyst, and we provide many of the typical questions you will face interviewing for a job position. The course has lots of code examples, real datasets, quizzes, and video. The video duration is 4 hours, but the user is expected to take at least 5 extra hours working on the examples, data , and code provided. After completing this course, the user is expected to be fully proficient with these techniques in an industry/business context. All code and data available at Github.
What are the requirements?
- Ideally some basic statistics and R, though neither is strictly necessary
- Some previous experience manipulating Excel files
What am I going to get from this course?
- Model basic and complex real world problem using linear regression
- Understand when models are performing poorly and correct it
- Design complex models for hierarchical data
- How to properly prepare the data for linear regression
- When linear regression is not sufficient
- Understand how to interpret the results and translate them to actionable insights
Who is the target audience?
- People pursuing a career in Data Science
- Statisticians needing more practical/computational experience
- Data modellers
- People pursuing a career in practical Machine Learning