R for Data Analysis

Module 1: Introduction to R Programming

• Overview of R and RStudio environment
• Basic syntax, variables, and data types

Module 2: Data Structures in R

• Vectors, lists, matrices, and data frames
• Factors and their handling

Module 3: Data Importing and Exporting

• Reading/writing CSV, Excel, and database connections
• Web scraping and API integration basics

Module 4: Data Manipulation and Cleaning

• Using dplyr and tidyr for data wrangling
• Handling missing data and duplicates

Module 5: Data Visualization Using R

• Base R graphics and ggplot2 package
• Creating bar charts, histograms, box plots, scatter plots

Module 6: Statistical Analysis

• Descriptive and inferential statistics
• Hypothesis testing, regression, and correlation analysis

Module 7: Machine Learning Basics in R

• Introduction to supervised and unsupervised learning
• Applying classification and clustering algorithms

Module 8: Time Series Analysis

• Basics of time series data and forecasting

Module 9: Text Mining and NLP

• Text preprocessing and sentiment analysis

Module 10: Hands-on Projects and Case Studies

• Real-world data analysis applications
• Best practices and reporting

Experience in the Industry Gain knowledge from experts with practical R for Data Analysis project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture  and cognizant  employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.