Introduction to Data Science with R

• What is Data Science?
• Significance of Data Science in today’s data-driven world, applications of Data Science, lifecycle of Data Science,and its components
• Introduction to Big Data Hadoop, Machine Learning, and Deep Learning
• Introduction to R programming and RStudio

Hands-on Exercise

•  Installation of RStudio
• Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases

Data Exploration

• Introduction to data exploration
• Importing and exporting data to/from external sources
• What are data exploratory analysis and data importing?
• Data Frames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Hands-on Exercise

• Accessing individual elements of customer churn data
• Modifying and extracting results from the dataset using user-defined functions in R

Data Manipulation

• Need for data manipulation
• Introduction to the dplyr package
• Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
• Combining different functions with the pipe operator and implementing SQL-like operations with sqldf

Hands-on Exercise

•  Implementing dplyr
• Performing various operations for manipulating data and storing it

Data Visualization

• Introduction to visualization
• Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
• Multivariate analysis with geom_boxplot
• Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
• Creating bar plots for categorical variables using geom_bar(), and adding themes with the theme() layer
• Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
• Working with coordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
• Geographic visualization with ggmap() and building web applications with shinyR

Hands-on Exercise

• Creating data visualization to understand the customer churn ratio using ggplot2 charts
• Using plotly for importing and analyzing data
• Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot

Introduction to Statistics

• Why do we need statistics?
• Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures ofspread
• Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution

Hands-on Exercise

• Building a statistical analysis model that uses quantification, representations, and experimental data
• Reviewing, analyzing, and drawing conclusions from the data