R Programming for Data Science- Practise 250 Exercises-Part1

Learn by Doing: Practical R Programming with Data Frames, ggplot2, and dplyr for Data Science using RStudio

Created by: Sheik Jamil Ahmed
Instructor at Udemy, Expert in ML, DL, DS, NLP, CV & GenAI

What Will I Learn?

• Develop a strong foundation in R programming by solving diverse exercises, reinforcing key concepts like data types, control structures, and functions.
• Gain hands-on experience with popular R libraries such as dplyr, ggplot2, tidyverse, and caret to manipulate and visualize datasets effectively.
• Apply data wrangling techniques to clean, transform, and organize real-world datasets using R.
• Master data visualization by creating insightful and professional-quality plots with ggplot2 and other visualization libraries.
• Enhance your statistical analysis skills by performing descriptive statistics, hypothesis testing, and regression analysis in R.
• Explore different datasets available in R and use them to practice machine learning algorithms such as linear regression, classification, and clustering.
• Debug and optimize R code by identifying common errors and applying best practices for efficient coding.
• Prepare for real-world data science challenges by solving exercises that reflect common tasks in data analysis and machine learning projects.

Requirements

• Basic understanding of programming concepts
• Introductory knowledge of R programming
• Familiarity with basic statistics and data analysis

Target audience

• Aspiring data scientists looking to strengthen their R programming skills through hands-on practice.
• R programmers seeking to improve their problem-solving abilities and apply advanced R libraries in real-world data analysis.
• Students and professionals in data science who want to enhance their understanding of data manipulation, visualization, and machine learning in R.
• Self-learners and enthusiasts interested in applying R to solve diverse data challenges using real-world datasets.
• Anyone preparing for data science job interviews or certifications that require proficiency in R programming and data analysis techniques.