About this course

This course provides an introductory yet thorough grounding in the R programming language, which is integral to data analysis and data science. Participants will learn the fundamentals of R, including data types, manipulation techniques, and essential programming tasks. With a focus on practical skills, the course promotes hands-on learning through various projects including programming in RStudio, data manipulation in data structures, and producing data-driven insights using tools like Watson Studio and Jupyter notebooks.

The course requires no prior knowledge of R or programming, making it accessible to beginners.

At a glance

• Institution: IBM
• Subject: Data Analysis & Statistics
• Level: Introductory
• Prerequisites: None
• Language: English
• Video Transcript: English
• Associated programs:
  • Professional Certificate in Data Analytics and Visualization with Excel and R
  • Professional Certificate in Applied Data Science with R
• Associated skills: Data Structures, R Programming, Data Analysis, Data Science, RStudio, Jupyter, Watson Studio

What you'll learn

• Manipulate both numeric and textual data types using RStudio or Jupyter Notebooks.
• Define and effectively manage various R data structures such as vectors, lists, and data frames.
• Control program flow, define and incorporate functions, and handle character string and date operations within R.
Perform data read/write operations, and apply web scraping techniques using R.

Who this course is for:

This course is designed for individuals aspiring to begin or elevate their career in data science. It is well-suited for analytic-minded individuals, including business analysts, research professionals, or anyone interested in harnessing the power of data for decision-making.

Real-World Application

Skill sets acquired from this course can be applied in real-world settings to analyze data effectively, build data-driven models, make informed decisions based on statistical evidence, and solve practical problems in various business and research contexts.