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Basic Introduction to Data Science with R

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5 courses

124 practices

8 hours

Begin a wonderful journey designed to transform you from a data science enthusiast into a skilled practitioner. This learning path is crafted with a selection of courses aimed at providing you with the essential knowledge, tools, and methodologies for extracting valuable insights from data.

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Verified skills you'll gain

DEVELOPING

Exploratory Data Analysis and Visualization

DEVELOPING

Machine Learning and Predictive Modeling

Tools you'll use

R

Course 1

Introduction to Data Science with R

5 lessons

25 practices

Start your journey with data science in R, leveraging its powerful capabilities for statistical analysis and data visualization. This course covers essential data science concepts and prepares you for practical applications, ensuring a strong foundation in R for your data science endeavors.

Course 2

Data Manipulation in R with dplyr

4 lessons

20 practices

Master data manipulation using the dplyr package. This course will help you perform data wrangling tasks such as filtering, selecting, summarizing, and mutating data effectively.

Course 3

Data Visualization with ggplot2

5 lessons

25 practices

Learn to create powerful visualizations using ggplot2. This course takes you through the fundamentals of plotting, exploring various geom layers, and enhancing your visualizations with themes and annotations.

Course 4

Introduction to Machine Learning with caret

5 lessons

25 practices

Embark on your data science journey with the introduction to machine learning using the caret package in R. This course guides you through essential steps like data preprocessing, splitting datasets, training simple models, and evaluating their basic performance.

Course 5

Practical Machine Learning with the mtcars Dataset in R

6 lessons

29 practices

Put your data science skills into practice by working on machine learning projects using the classic `mtcars` dataset in R. This course provides hands-on experience with end-to-end solutions, from data preprocessing to model evaluation, ensuring you are prepared for real-world tasks.

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