Linear regression in R for Data Scientists

Linear regression in R for Data Scientists, available at $44.99, has an average rating of 3.15, with 30 lectures, 8 quizzes, based on 14 reviews, and has 206 subscribers.

You will learn about 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 This course is ideal for individuals who are People pursuing a career in Data Science or Statisticians needing more practical/computational experience or Data modellers or People pursuing a career in practical Machine Learning It is particularly useful for People pursuing a career in Data Science or Statisticians needing more practical/computational experience or Data modellers or People pursuing a career in practical Machine Learning.

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Summary

Title: Linear regression in R for Data Scientists

Price: $44.99

Average Rating: 3.15

Number of Lectures: 30

Number of Quizzes: 8

Number of Published Lectures: 30

Number of Published Quizzes: 8

Number of Curriculum Items: 38

Number of Published Curriculum Objects: 38

Original Price: £19.99

Quality Status: approved

Status: Live

What You Will Learn

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 Should Attend

People pursuing a career in Data Science
Statisticians needing more practical/computational experience
Data modellers
People pursuing a career in practical Machine Learning

Target Audiences

People pursuing a career in Data Science
Statisticians needing more practical/computational experience
Data modellers
People pursuing a career in practical Machine Learning

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Getting the data/code for this course

Lecture 3: What is linear regression, and what is this course about?

Lecture 4: Why R?

Lecture 5: Setting up R. Understanding the basics

Lecture 6: Preparing the data in R

Chapter 2: Linear regression: Ordinary Least Squares

Lecture 1: Mathematical preliminaries (OPTIONAL)

Lecture 2: A first example in R

Lecture 3: The likelihood – Ordinary least squares equivalence

Lecture 4: PValues

Lecture 5: PValue hacking

Lecture 6: A more realistic example

Lecture 7: Model Selection

Lecture 8: Residuals and plots

Lecture 9: Influence plots and outlier detection

Lecture 10: Log transformations – Price Elasticities

Lecture 11: Overfitiing

Lecture 12: Prediction

Lecture 13: Multicollinearity

Lecture 14: Heteroscedasticity, and how to solve it

Lecture 15: Autocorrelation, and how to solve it

Lecture 16: Monte Carlo

Chapter 3: Linear regression: Mixed Effects Regression

Lecture 1: Hierarchical Models and linear regression

Lecture 2: Random effects – A philosophical discussion

Lecture 3: A better mixed model example

Lecture 4: Computational Method behind the lmer() function

Lecture 5: Model results and residuals

Lecture 6: Advanced random effects modelling and nested effects

Lecture 7: Multiple Comparisons

Chapter 4: Robust linear regression

Lecture 1: The rlm() and the lmRob() functions

Instructors

Francisco Juretig
Mr

Rating Distribution

1 stars: 2 votes
2 stars: 1 votes
3 stars: 5 votes
4 stars: 4 votes
5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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