Data Science with R Programming Certification Training Course Syllabus

Overview: This instructor-led certification course guides beginners through the complete data science lifecycle using R programming, combining live sessions with self-paced learning. The curriculum spans foundational R programming, statistical inference, data wrangling, machine learning, and advanced topics like text mining and time series analysis. With hands-on projects in healthcare, media, social media, and aviation, learners gain practical experience. Estimated time commitment is approximately 8 hours, distributed across 10 modules. Lifetime access ensures ongoing learning and review.

Module 1: Introduction to Data Science with R

Estimated time: 1 hour

• What is Data Science
• Stages of the data science lifecycle
• Introduction to big data, Hadoop, and Spark
• Setting up R and RStudio
• Importing and exploring sample datasets

Module 2: Statistical Inference

Estimated time: 0.75 hours

• Measures of center and spread
• Probability distributions
• Hypothesis testing fundamentals
• Conducting t-tests in R

Module 3: Data Extraction, Wrangling & Exploration

Estimated time: 1 hour

• Building data pipelines
• Handling CSV, JSON, and XML data
• Exploratory data analysis (EDA)
• Data cleaning and reshaping with dplyr and tidyr
• Basic data visualization using ggplot2

Module 4: Introduction to Machine Learning

Estimated time: 0.75 hours

• Overview of the machine learning workflow
• Implementing linear regression in R
• Implementing logistic regression in R
• Evaluating regression models on real datasets

Module 5: Classification Techniques

Estimated time: 1 hour

• Decision trees and their implementation
• Random forests for improved accuracy
• Naive Bayes classifier
• Support vector machines (SVM)
• Comparing classification models in R

Module 6: Unsupervised Learning & Clustering

Estimated time: 0.75 hours

• K-means clustering algorithm
• Fuzzy C-means clustering
• Hierarchical clustering methods
• Evaluating cluster performance
• Visualizing clustering results

Module 7: Recommender Engines

Estimated time: 0.75 hours

• Understanding association rules
• User-based vs. item-based filtering
• Real-world recommendation use cases
• Building a recommender system using R packages

Module 8: Text Mining

Estimated time: 0.75 hours

• Bag of Words model
• TF-IDF (Term Frequency-Inverse Document Frequency)
• Sentiment analysis workflows
• Extracting and analyzing Twitter data in R

Module 9: Time Series Analysis

Estimated time: 1 hour

• Components of time series data
• ARIMA and ETS models
• Forecasting techniques
• Decomposing and modeling time series in R

Module 10: Deep Learning Basics

Estimated time: 1 hour

• Neural network fundamentals
• Introduction to reinforcement learning
• Building a simple artificial neural network (ANN)
• Using R for deep learning tasks

Prerequisites

• Basic computer literacy
• Familiarity with fundamental mathematical concepts
• No prior programming experience required

What You'll Be Able to Do After

• Master R programming fundamentals including data types, operators, and functions
• Perform data extraction, cleaning, and wrangling using dplyr and tidyr
• Apply statistical inference techniques to draw meaningful insights from data
• Implement supervised and unsupervised machine learning algorithms such as linear/logistic regression, decision trees, and clustering
• Build real-world projects including recommendation systems, text mining applications, and time series forecasting models