Hands-on Training from Experts
Foundation Training in
R Language
This course provides a comprehensive foundation in R programming, teaching students data manipulation, statistical analysis, and data visualization. By the end, students will be able to perform data cleaning, exploratory analysis, and apply basic machine learning techniques in R.
Program Features
- Max Students: 10
- Duration: 1 Month
- Practical Training
- Certificate after Completion
- Vocational Training Program
- Investment: 10,000.00
Course Description
The R Language course is designed to equip students with a comprehensive foundation in R programming, tailored for data science and analytics applications. Through this course, participants will learn essential techniques for data manipulation, statistical analysis, and data visualization. The curriculum covers everything from cleaning and transforming data to creating insightful visualizations and conducting exploratory data analysis. Additionally, students will be introduced to basic machine learning concepts using R, enabling them to apply these skills to real-world problems. This course is ideal for those looking to advance their capabilities in data science and leverage R for practical data-driven decision-making.
Course Objectives
The objective of the ” R Language” course is to provide students with a comprehensive foundation in R programming, enabling them to efficiently perform data manipulation, statistical analysis and data visualization. By the end of the course, students will be equipped with the skills necessary to clean and transform data, create informative visualizations, conduct exploratory data analysis and apply basic machine learning techniques using R, preparing them for practical applications in data science and analytics.
Curriculum
Day 1: Introduction to R and RStudio
- Overview of R and its applications
- Installing R and RStudio
- Navigating RStudio interface
- Basic R syntax and commands
Day 2: Data Types and Structures
- Understanding different data types (numeric, character, logical)
- Vectors, matrices, and lists
- Data frames and tibbles
Day 3: Basic Operations in R
- Arithmetic operations
- Relational and logical operators
- Using built-in functions
Day 4: Data Import and Export
- Reading data from CSV, Excel, and text files
- Writing data to files
- Using packages for data import (readr, readxl)
Day 5: Data Cleaning and Transformation
- Handling missing data
- Subsetting and filtering data
- Transforming data with dplyr (select, filter, mutate, arrange)
Day 6: Data Manipulation with dplyr
- Grouping data with group_by
- Summarizing data with summarize
- Combining data frames with join operations
- Principles of effective data visualization
- Creating basic plots with base R
- Understanding ggplot2 syntax
- Creating scatter plots, line plots, and bar plots
- Customizing plots (titles, labels, themes)
- Creating faceted plots
- Adding layers (geoms)
- Working with scales and coordinates
- Introduction to interactive plotting packages (plotly)
- Creating interactive plots from ggplot2
Day 11: Descriptive Statistics
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (variance, standard deviation, IQR)
- Summary statistics with summary() and describe()
- Understanding probability distributions
- Working with normal, binomial, and Poisson distributions
- Generating random numbers
- Concepts of null and alternative hypotheses
- Performing t-tests and chi-squared tests
- Interpreting p-values
- Calculating correlation coefficients
- Simple linear regression
- Multiple regression analysis
- Residual analysis
- Model validation techniques (cross-validation)
- Evaluating model performance (RMSE, R²)
Day 16: Introduction to Machine Learning with R
- Overview of machine learning concepts
- Supervised vs. unsupervised learning
- Implementing linear regression
- Logistic regression
- Decision trees
- Model evaluation metrics for classification
- K-means clustering
- Hierarchical clustering
- Practical examples of clustering
- Project proposal and dataset selection
- One-on-one consultation and guidance
- Conducting data analysis and modeling
- Creating visualizations and reports
- Project presentation and peer review
Intended outcomes
By the end of the “R Language” course, students will be able to proficiently use R and RStudio to perform data manipulation, cleaning and transformation; create clear and informative visualizations using base R and ggplot2; conduct exploratory data analysis to identify patterns and trends; and carry out statistical analysis, including hypothesis testing and regression. They will also gain introductory skills in applying machine learning techniques, enabling them to tackle real-world data science and analytics challenges effectively.
Tuition & Investment
| Enrollment Amount | Registration Amount | No. of Installments |
|---|---|---|
| Rs. 500.00 | Rs. 9500.00 | -- |
| Total Amount | Rs. 10000.00 | |
Fee w.e.f. June 2024 | This fee structure is for limited time and subject to revised up
Schedule and Enrollment
Monday to Friday | 5 Days a Week Classes | Weekdays