Duration
2 months
Mode
Online/Offline
Batch Size
10
Modules
1
LMS Access
Yes
Timing
Weekday/Weekends
Introduction
R programming is a popular language for data analytics due to its rich ecosystem of packages and libraries designed for data manipulation, visualization, and statistical analysis.
Goal of Course
- Data Import and Manipulation
- Descriptive Statistics
- Data Wrangling using advanced packages
- Exploratory data analysis (EDA) through visualizations
Key Modules
- Introduction to R
- Data Manipulation
- Data Visualization
- Statistical Analysis
- Data Structures and Control Structures
- Packages and Libraries
- Data Wrangling
- Case Studies and Projects
Introduction to R
- Installation and setup of R and RStudio (an integrated development environment for R).
- Basic syntax and data types in R, including vectors, matrices, data frames, and lists.
- Arithmetic operations and basic functions.
Data Manipulation
- Importing and exporting data in various formats (e.g., CSV, Excel, SQL databases).
- Data cleaning and transformation.
- Subsetting and filtering data.
- Aggregating and summarizing data.
Data Visualization
- Using libraries like ggplot2 to create static and interactive visualizations.
- Customizing plots, adding titles, labels, and annotations.
- Exploratory data analysis (EDA) through visualizations.
Statistical Analysis
- Descriptive statistics: mean, median, variance, standard deviation, etc.
- Hypothesis testing and statistical inference.
- Regression analysis (linear regression, logistic regression).
- Time series analysis.
Data Structures and Control Structures
- Understanding data structures like lists, data frames, and factors.
- Conditional statements (if-else), loops (for, while), and functions.
- Working with missing data.
Packages and Libraries
- Learning to use R packages for specific tasks (e.g., dplyr for data manipulation, tidyr for data tidying, lubridate for handling dates and times)
Data Wrangling
- Reshaping data from wide to long format and vice versa.
- Combining datasets (merge, join operations).
- Pivoting and melting data.
Case Studies and Projects
- Practical hands-on projects and exercises to apply what you've learned.
Advanced Topics
- Machine learning with R (using packages like caret, randomForest, xgboost).
- Text mining and natural language processing (NLP) with packages like tm and tidytext.
- Web scraping and API interactions.
Note :you can speak to our team for detailed content and available batch timings.
