Data analytics with r 101

target audience - noobs

This course is for total noobs with no programming experience.

prerequisites

• Basic computer literacy
• Familiarity with using web browsers and software applications
• Willingness to learn and experiment
• No prior programming or data analytics experience required

learning objectives

• Understand the fundamental concepts of data analytics and its importance in decision-making
• Get familiar with R programming environment and installation process
• Learn basic data manipulation techniques using R
• Discover how to visualize data through R packages
• Explore statistical methods to analyze datasets
• Develop the ability to interpret and communicate findings effectively
• Gain confidence in experimenting with real-world datasets and analytical tools

course outline

chapter 1 - introduction to data analytics

• objectives
  • Understand what data analytics is and its relevance in various fields
  • Recognize the role of data analytics in decision-making processes
• outline
  • Definition of data analytics
  • Importance in business, healthcare, education, and more
  • Overview of the data analytics lifecycle

chapter 2 - getting started with R

• objectives
  • Set up the R programming environment on personal computers
  • Get familiar with RStudio and its interface
• outline
  • Introduction to R and RStudio
  • Installation process for R and RStudio
  • Overview of RStudio interface and features

chapter 3 - basic R programming concepts

• objectives
  • Learn fundamental programming concepts in R
  • Understand data types, variables, and operators
• outline
  • Introduction to R syntax
  • Data types: vectors, lists, matrices, data frames
  • Basic operations and calculations in R

chapter 4 - data manipulation in R

• objectives
  • Explore data manipulation techniques using R packages
  • Understand the tidyverse ecosystem
• outline
  • Introduction to the tidyverse
  • Data manipulation with dplyr
  • Reshaping data with tidyr: pivoting and gathering data

chapter 5 - data visualization with R

• objectives
  • Learn how to create various types of visualizations using R
  • Understand the principles of effective data visualization
• outline
  • Introduction to ggplot2
  • Creating basic plots: scatter plots, bar charts, histograms
  • Customizing plots and adding aesthetics

chapter 6 - statistical analysis in R

• objectives
  • Discover the basics of statistical analysis using R
  • Learn to perform descriptive and inferential statistics
• outline
  • Introduction to statistical concepts
  • Using summary statistics for data exploration
  • t-tests, ANOVA, and correlation analysis

chapter 7 - interpreting and communicating findings

• objectives
  • Develop skills to interpret data analysis results
  • Learn best practices for presenting findings to various audiences
• outline
  • Understanding the context of findings
  • Crafting a narrative around data
  • Best practices for data presentation: reports and dashboards

chapter 8 - working with real-world datasets

• objectives
  • Gain hands-on experience by analyzing real-world datasets
  • Build confidence in applying learned techniques
• outline
  • Introduction to sources of datasets (Kaggle, government data)
  • Choosing a dataset and defining questions
  • Step-by-step project: from data cleaning to analysis and presentation

chapter 9 - next steps in data analytics

• objectives
  • Explore further learning resources and career paths in data analytics
  • Understand the importance of continuous learning and practice
• outline
  • Overview of advanced topics in data analytics
  • Recommended resources: online courses, books, communities
  • Career opportunities and skills in demand in the data analytics field