Description

Course Description:

The Data Analysis with R Programming Certified Course is designed to equip learners with the essential skills needed to perform data analysis using one of the most powerful and widely used open-so  This course provides a strong foundation in data manipulation, visualization, statistical analysis, and reporting using R, making it ideal for aspiring data analysts, statisticians, and researchers.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Data Analysis with R Programming:

• Data Analyst
• Data Scientist
• Business Analyst
• Statistician
• Research Analyst
• Data Engineer
• Financial Analyst
• Marketing Analyst

Essential Skills you will Develop Data Analysis with R Programming:

• R Programming Fundamentals
• Data Manipulation with dplyr & tidyr
• Data Visualization with ggplot2
• Statistical Analysis
• Handling Real-World Datasets

Tools Covered:

• Shiny
• For building interactive web applications using R
• Caret
• Classification and Regression Training for machine learning workflows
• lubridate
• Simplifies working with date and time data
• stringr
• Tools for string processing

Syllabus:

Module 1: Introduction to R and  Overview of R and its applications in data analysis Installing R and Studio Interface: Console, Source, Environment Writing and running R scripts Basic R syntax and operators Data types and variables.

Module 2: Data Structures in R Vectors, Matrices, Lists, and Data Frames
Indexing and sub setting data Factors and categorical data Data coercion and conversion Hands-on exercises.

Module 3: Data Importing and Exporting Reading data from CSV, Excel, and text files Importing data from web APIs and databases Data exporting (write.csv, write.xlsx) Using packages.

Module 4: Data Cleaning and Manipulation Handling missing values Filtering, selecting, and arranging data with Creating new variables (mutate) Grouping and summarizing data String manipulation with.

Module 5: Data Visualization with ggplot2 Introduction to ggplot2 Scatter plots, bar charts, histograms, boxplots Customizing plots (themes, labels, colors)
Faceting and layering Exporting visualizations.

Module 6: Exploratory Data Analysis (EDA) Descriptive statistics (mean, median,)
Frequency distribution and cross-tabulations Outlier detection and treatment
Correlation analysis Visual EDA techniques.

Module 7: Statistical Analysis in R Hypothesis testing (t-test, chi-square, ANOVA)
Linear regression and interpretation Confidence intervals and p-values Introduction to probability distributions Using stats package.

Module 8: Working with Time Series and Date Data Date and time objects in R Time series creation and plotting Moving averages and smoothing Forecasting basics with forecast package Decomposing time series.

Module 9: Advanced Data Handling and Automation Loops and control structures
Writing functions in R Apply family Automating reports with R Markdown Introduction to shiny for interactive apps.

Module 10: Capstone Project and Certification End-to-end data analysis project
Cleaning, analyzing, and visualizing a real-world dataset Documenting the process using R Markdown Project presentation and evaluation Final assessment and certification.

Industry Projects:

• Bike‑share analysis
• Financial services projects
• Customer Insights
• Healthcare Predictive Analytics
• Tools & R Ecosystem

Who is this program for? 

• Students and Fresh Graduates
• Working Professionals
• Researchers and Academicians
• Career Changers
• Data Science Enthusiasts

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com