Course Summary
Learn how to use the Tidyverse suite of tools to model and visualize data in R.Key Learning Points
- Discover the power of the Tidyverse suite of tools
- Learn to model and visualize data using R
- Apply best practices for data analysis
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $62,453 - $118,445
- India: ₹300,000 - ₹1,400,000
- Spain: €20,000 - €45,000
- Data Scientist
- USA: $85,000 - $150,000
- India: ₹450,000 - ₹2,000,000
- Spain: €25,000 - €55,000
- Business Intelligence Analyst
- USA: $57,000 - $115,000
- India: ₹300,000 - ₹1,400,000
- Spain: €20,000 - €45,000
Related Topics for further study
Learning Outcomes
- Master the Tidyverse suite of tools for data analysis
- Learn best practices for modeling and visualizing data
- Develop skills in statistical modeling using R
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of R programming
- Familiarity with data analysis techniques
Course Difficulty Level
IntermediateCourse Format
- Online Course
- Self-Paced
Similar Courses
- Data Science Essentials
- Data Analysis and Visualization
Related Education Paths
Notable People in This Field
- R-bloggers
- DataCamp
Related Books
Description
Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.
Knowledge
- D​escribe different types of data analytic questions
- Conduct hypothesis tests of your data
- A​pply linear modeling techniques to answer multivariable questions
- A​pply machine learning workflows to detect complex patterns in your data
Outline
- Modeling Data Basics
- The Purpose of Data Science
- Types of Data Science Questions
- Data Needs
- Number of observations is too small
- Dataset does not contain the exact variables you are looking for
- Variables in the dataset are not collected in the same year
- Dataset is not representative of the population that you are interested in
- Some variables in the dataset are measured with error
- Variables are confounded
- Descriptive and Exploratory Data Analysis
- Missing Values
- Shape
- Identifying Outliers
- Evaluating Variables
- Evaluating Relationships
- Modeling Data Basics Quiz
- Inference
- Inference
- Uncertainty
- Random Sampling
- Inference Quiz
- Linear Modeling
- Linear Regression
- Assumptions
- Association
- Association Testing in R
- Fitting the Model
- Model Diagnostics
- Tree Girth and Height Example
- Interpreting the Model
- Variance Explained
- Using broom
- Correlation Is Not Causation
- Confounding
- Linear Regression Quiz
- Multiple Linear Regression
- Multiple Linear Regression
- Multiple Linear Regression Quiz
- Beyond Linear Regression
- Beyond Linear Regression
- Mean Different From Expectation?
- Testing Mean Difference From Expectation in R
- Hypothesis Testing
- More Statistical Tests
- Hypothesis Testing
- The infer Package
- Hypothesis Testing Quiz
- Prediction Modeling
- Prediction Modeling
- What is Machine Learning?
- Machine Learning Steps
- Data Splitting
- Train, Test, Validate
- Train
- Test
- Validate
- Variable Selection
- Model Selection
- Regression vs. Classification
- Model Accuracy
- Prediction and Machine Learning Quiz
- The tidymodels Ecosystem
- The tidymodels Ecosystem
- Benefits of tidymodels
- Packages of tidymodels
- Example of Continuous Variable Prediction
- Example of Categorical Variable Prediction
- tidymodels Quiz
- Case Studies
- Case Study #1: Predicting Annual Air Pollution
- The Data
- Data Import
- Data Exploration and Wrangling
- Evaluate Correlation
- Splitting the Data
- Making a Recipe
- Running Preprocessing
- Specifying the Model
- Assessing the Model Fit
- Model Performance: Getting Predicted Values
- Visualizing Model Performance
- Quantifying Model Performance
- Assessing Model Performance on v -folds Using tune
- Random Forest
- Model Tuning
- Final model performance evaluation
- Summary of tidymodels
- Summary of tidymodels
- Project: Modeling Data in the Tidyverse
- Important information before you start the quiz
- C​ourse Project Prediction Quiz
Summary of User Reviews
The Tidyverse Modelling Data course on Coursera has received positive reviews from users. Many users found the course to be informative and engaging.Key Aspect Users Liked About This Course
The course is well-structured and easy to follow.Pros from User Reviews
- The course covers a range of topics in data modelling.
- The instructors are knowledgeable and provide clear explanations.
- The course provides practical examples and exercises to reinforce learning.
- The course is suitable for beginners and intermediate learners.
- The course content is relevant and up-to-date.
Cons from User Reviews
- Some users found the course to be too basic.
- Some users experienced technical difficulties with the platform.
- Some users found the pace of the course to be too slow.
- Some users found the course to be too theoretical and lacking in real-world applications.
- Some users found the course to be too focused on R programming language.
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