Course description

This course will guide you through the entire data science workflow, starting with data manipulation, cleaning, visualization to machine learning whether you’re new to R or looking to deepen your knowledge.

You will start with the basics of R programming, gaining familiarity with essential data structures and functions, then move on to using key R libraries like dplyr, ggplot2, and caret. You will learn to clean and wrangle data, create insightful visualizations, perform statistical analyses, and build predictive models by simply working on real-world projects. Throughout the course, you’ll practice your skills with hands-on exercises that reinforce key concepts to ensure you gain the necessary skills required to get you ready for industry-level operations.

By the end of this course, you will have a strong foundation in using R in data science, and be able to confidently apply your knowledge to solve data-driven problems, create compelling visualizations, and build machine learning models. This course is ideal for anyone looking to start a career in data science or those who want to expand their existing skills. Join us today, and let us learn and share knowledge together.

What will i learn?

• Gain a solid foundation in R syntax, variables, data types, operators, and control structures.
• Work confidently with vectors, lists, matrices, data frames, and factors to organize and transform data efficiently.
• Use R to clean, process, and explore datasets using functions from base R and packages like dplyr, tidyr, and readr.
• Build informative and visually appealing charts using ggplot2 and other powerful visualization libraries.
• Develop custom functions to automate repetitive tasks and enhance code modularity and reusability.
• Analyze datasets and generate insights from practical case studies related to business, health, and public data.
• Learn how to write clean, readable, and efficient R code following professional coding standards.
• Use R Markdown to create dynamic reports and reproducible research documents for academic or professional use.
• Be well-prepared to pursue further learning in statistical modeling, machine learning, and data science using R.

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