REG-R.AJ1
Regression Analysis with R
With this Regression Analysis in R course, you’ll build predictive models, stand out in interviews, and secure high-paying roles in data science, analytics, and research.
- 10 Lecciones interactivas y 54 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso 4.5/5 (100 Revisar)
01 / Habilidades que obtendrás
What you will be able to do
Videos courses and tutorials teach you how to run regression models. This one teaches you how to think with them.
In this Regression Analysis with R course, you’ll learn how to uncover meaningful relationships in data, predict outcomes, and build models that can influence real-world decisions. Work with real datasets, tackle hands-on projects and build a job-ready portfolio. Go from simple linear regression to advanced techniques, all through practice, job-relevant scenarios.
No beating about the bush, just applied regression modeling in R, taught in a way that sticks.
- Build, optimize, and validate predictive models using R, the language trusted by statisticians and data pros.
- Uncover hidden patterns, measure relationships, and extract powerful insights that drive business impact.
- Develop a problem-solving mindset to predict sales, analyze trends, and optimize outcomes.
- Get hands-on with industry-grade libraries and tools likeggplot2, caret, lm(), and more.
- Interpret model outputs, diagnose errors, and communicate findings.
- Visualize and represent your data insights.
Course Highlights
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10 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
10 Lecciones interactivas · 54 topics01 Preface 3 topics +
- What this course covers
- To get the most out of this course
- Conventions used
02 Getting Started with Regression 10 topics +
- Going back to the origin of regression
- Regression in the real world
- Understanding regression concepts
- Regression versus correlation
- Discovering different types of regression
- The R environment
- Installing R
- RStudio
- R packages for regression
- Summary
03 Basic Concepts – Simple Linear Regression 6 topics +
- Association between variables – covariance and correlation
- Searching linear relationships
- Least squares regression
- Creating a linear regression model
- Modeling a perfect linear association
- Summary
04 More Than Just One Predictor – MLR 6 topics +
- Multiple linear regression concepts
- Building a multiple linear regression model
- Multiple linear regression with categorical predictor
- Gradient Descent and linear regression
- Polynomial regression
- Summary
05 When the Response Falls into Two Categories – Logistic Regression 5 topics +
- Understanding logistic regression
- Generalized Linear Model
- Multiple logistic regression
- Multinomial logistic regression
- Summary
06 Data Preparation Using R Tools 6 topics +
- Data wrangling
- Finding outliers in data
- Scale of features
- Discretization in R
- Dimensionality reduction
- Summary
07 Avoiding Overfitting Problems - Achieving Generalization 4 topics +
- Understanding overfitting
- Feature selection
- Regularization
- Summary
08 Going Further with Regression Models 4 topics +
- Robust linear regression
- Bayesian linear regression
- Count data model
- Summary
09 Beyond Linearity – When Curving Is Much Better 6 topics +
- Nonlinear least squares
- Multivariate Adaptive Regression Splines
- Generalized Additive Model
- Regression trees
- Support Vector Regression
- Summary
10 Regression Analysis in Practice 4 topics +
- Random forest regression with the Boston dataset
- Classifying breast cancer using logistic regression
- Regression with neural networks
- Summary
03 / Preguntas frecuentes
Preguntas antes de empezar
What exactly will I be able to do after finishing this course?+
How is this course different from free tutorials?+
Can I take this course if I’m switching to a data career?+
What tools or software do I need?+
Build a Portfolio Too Strong for Employers to Ignore
Start building the data career they said you needed experience for.
- 1 año de acceso completo
- Certificado de finalización
No se requiere tarjeta de crédito