Data Analytics & Machine Learning with R Programming

Data Analytics & Machine Learning with R Programming
 

About this Course: 

What you will Learn:

• Fundamentals of R Programming: The course starts with the fundamentals of R, focusing on its core data structure, conditional & logical operators, statements and functions etc.
• Data Analytics with R Programming: The participants will gain hands-on expertise on data manipulation using modern R packages such as dplyr, tidyr, and readr etc. and data visualization using ggplot2. Statistical concepts are thoroughly covered to help participants understand distributions, variability and inference using real-world examples.
• Machine Learning with R Programming: The second half of the course introduces Machine Learning workflows using R’s powerful packages such as caret, mlr3 and randomForest etc. Both supervised and unsupervised learning techniques will be thoroughly explored with real-world examples.

Course Overview:

Module 1: Fundamentals of R Programming

• Basics of R language: Syntax, Data Types, Operators
• R Objects: Vectors, Lists, Matrices, Arrays, Factors, Data Frames
• Variable Assignment, Indexing & Slicing
• Conditional & Logical Operators
• Statements in R:  if-else, while, for
• Writing Functions in R, Anonymous Functions (function ())

Module 2: Data Analytics with R Packages

• Introduction to Data Science in R
• Built-in R Features
• Apply and Math Functions with R
• Regular Expressions, Dates and Timestamps
• Introduction to dplyr and Tidyverse
• Data manipulation: select(), filter(), mutate(), arrange(), summarise()
• Handling Missing Values & Outliers
• Data Cleaning Techniques

Module 3: Data Visualization in R

• Base R Plotting: plot(), hist(), boxplot(), etc.
• Introduction to ggplot2
• Customizing plots: titles, labels, themes
• Advanced visualizations: Violin Plots, Pair Plots
• Combining plots: patchwork, gridExtra, cowplot
• Hands-on project with real-world data visualization

Module 4: ML Basics & Supervised Learning 

• Introduction to Machine Learning in R
• Using caret and mlr3 packages
• Simple & Multiple Linear Regression
• Model Evaluation: RMSE, MAE, R²
• Logistic Regression & Classification Tasks
• Confusion Matrix, Accuracy, Precision, Recall, F1 Score

Module 5: Supervised Learning & Natural Language Processing (NLP)

• Decision Trees using rpart, C50
• k-Nearest Neighbors with class and caret
• Random Forest with randomForest package
• Naive Bayes Classifier
• Basics of Natural Language Processing (NLP) in R
• Text Mining with tm, tidytext, quanteda
• Creating Document Term Matrices and using TF-IDF

Module 6: Unsupervised Learning with R

• Introduction to Clustering in R
• K-Means Clustering
• Hierarchical Clustering with Dendrograms
• DBSCAN using dbscan package
• Dimensionality Reduction with PCA
• Hands-on Unsupervised Learning Project  with R

Prerequisites and eligibility:

• No coding experience in any programming language required. We’ll start from scratch.
• This course can be taken up by any undergraduate/postgraduate student of Basic & Applied Sciences, Engineering, Management and Computer Applications and also by Research Scholars/Faculties/Working Professionals who want to upskill themselves.
• Participants need to have a laptop/PC (with a minimum of 4 GB RAM, 100 GB HDD, Intel i3 processor) and proper internet/Wi-Fi connection.

Contact Person: Dr. Subrat Kotoky

Email: [email protected] / [email protected]

Phone: 9085317465 / 8473874389

Expert Profile:  Mr. Shreyas Shukla

• DP-203: Microsoft Certified: Azure Data Engineer Associate
• DP-900: Microsoft Certified: Azure Data Fundamentals
• AZ-900: Microsoft Certified: Azure Fundamentals

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