R Programming for Data Science- Practise 250 Exercises-Part2

Level Up Your Skills: Advanced Challenges & Expert Insights in R Programming!

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

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 R programming: Familiarity with R syntax, variables, data types, and basic functions.
• Introduction to data structures in R: Knowledge of common data structures like vectors, data frames, and lists.
• Passion to become Data Scientist
• Internet connection and Laptop

Target audience

• Aspiring Data Scientists: Those looking to build a strong foundation in R programming while solving real-world data science problems.
• Students and Academics: Learners studying data science or related fields who want hands-on practice with R and its various libraries and datasets.
• Professionals in Data-Driven Roles: Individuals working in fields like business analytics, finance, healthcare, or marketing who want to enhance their data analysis skills using R.
• Self-Learners and Coding Enthusiasts: Those passionate about learning R programming through practical exercises and improving their coding proficiency in data science projects.