Data Science with R Programming Certification Training Course

Module 1: Introduction to Data Science with R

⏳ 1 hour

• Topics: What is Data Science; lifecycle stages; big data, Hadoop, Spark; R setup

• Hands-on: Install R/RStudio; import and explore sample datasets

Module 2: Statistical Inference

⏳ 45 minutes

• Topics: Measures of center and spread; probability distributions; hypothesis testing

• Hands-on: Compute summary statistics and conduct t-tests in R

Module 3: Data Extraction, Wrangling & Exploration

⏳ 1 hour

• Topics: Data pipelines; handling CSV/JSON/XML; exploratory data analysis; visualization basics

• Hands-on: Clean and reshape datasets; generate plots with ggplot2

Module 4: Introduction to Machine Learning

⏳ 45 minutes

• Topics: ML workflow; linear and logistic regression implementation

• Hands-on: Train and evaluate regression models on real datasets

Module 5: Classification Techniques

⏳ 1 hour

• Topics: Decision trees; random forests; Naive Bayes; support vector machines

• Hands-on: Build and compare classification models in R

Module 6: Unsupervised Learning & Clustering

⏳ 45 minutes

• Topics: K-means, C-means, hierarchical clustering; cluster evaluation

• Hands-on: Perform clustering and visualize groupings

Module 7: Recommender Engines

⏳ 45 minutes

• Topics: Association rules; user- vs. item-based filtering; recommendation use cases

• Hands-on: Create a recommendation system using R packages

Module 8: Text Mining

⏳ 45 minutes

• Topics: Bag of Words; TF-IDF; sentiment analysis workflows

• Hands-on: Extract and analyze text data from Twitter

Module 9: Time Series Analysis

⏳ 1 hour

• Topics: Time series components; ARIMA and ETS models; forecasting techniques

• Hands-on: Decompose and forecast time series datasets

Module 10: Deep Learning Basics

⏳ 1 hour

• Topics: Neural network fundamentals; reinforcement learning overview

• Hands-on: Build a simple ANN for classification

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