Benefits of the course
- R programming fundamentals: Learn the basics of the R programming language, such as variables, data types, functions, and loops.
- Data manipulation: Learn how to use R to import, clean, and manipulate data from a variety of sources, such as CSV files, SQL databases, and APIs.
- Exploratory data analysis (EDA): Learn how to use R to perform EDA, which is the process of exploring and understanding data. This includes tasks such as calculating descriptive statistics, creating data visualizations, and identifying patterns and trends.
- Statistical analysis: Learn how to use R to perform statistical analysis on data. This includes tasks such as hypothesis testing, regression analysis, and classification.
- Data visualization: Learn how to use R to create informative and engaging data visualizations. This includes using libraries such as ggplot2 to generate charts, graphs, and maps.
Topics for this course
Module 1: Introduction
What is Data Science
Application of Statistics and Programming in Data Science
Importance of Data in Al, Data Science and Data Analytics
Module 2 : Statistics
Introduction to Data Types
Module 3 : R Programming
Introduction to R Programming
Getting Data from different Sources
Module 4 : DA with R
- No prior coding knowledge is required or assumed
- Career changers: People who are interested in transitioning to a career in data science or data analytics.
- Students: Undergraduate and graduate students in computer science, data science, statistics, and other related fields.
- Professionals: People who work in a variety of fields and want to learn how to use data analytics to improve their work. This includes people who work in business intelligence, marketing, finance, healthcare, and more.