Data Analysis with R
Get start with R programming: In this module, we will work with the basics of R programming in RStudio and understand the domain of the dataset and using R packages we’ll learn data importing and exporting basics.
Tidyverse and data pipelining: In this module we learn what are Data Frames and their Vector types. Also, we implement tidy tools on the dataset. Here we learn Data cleaning basics and Data wrangling basics.
Data manipulation: This module teaches how to handle the missing values.Then we perform data reshaping and data grouping operations. We manipulate data using dplyr packages and various functions in them like group_by, summarize, mutate, join etc.Also we learn various Built-in functions as well as for loops and conditional statements.
Data Visualization: In Data Visualization we learn to visualize the manipulated data in various forms such as Bar chart,Histogram, Heat map, scatter plot etc using tools such as ggplot2, RColorBrewer, Leaflet, Plotly. Also we can visualize data in Geographical maps, density plots, frequency polygon plots.
Hands on Assignments
- Here we start with basics such as variable assignments and basic function on the variables. Then we practice on different data types.
- Simple calculations
- Vectors, Matrices, Data frame
- Various types of data formats importing.
- Here we will combine import, some manipulation and export together. This is common workflow for data analysis.
- R is functional programming language. So we perform either built in or own functions on the dataset.
- Identifying messy data and clean it, preparing it for analysis.
- Introduction to dplyr package.
- Five key data manipulation functions.
- Filtering and arranging the data,dplyr aggregate functions.
- Mutating joins and assembling data.
- Variables to visuals.
- Understanding structure of data.
This helps the learner to deploy the mini project in cloud by using different cloud services like
- Elastic Beanstalk
- RDS etc.,