R. Analytics with Machine learning

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Last Update 12/07/2024

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Course Prerequisites

Please note that this course has the following prerequisites which must be completed before it can be accessed

About This Course

The R Programming for Data Science course is designed to equip participants with the essential skills and knowledge required to effectively use R, a powerful programming language for statistical computing and data analysis. This course covers fundamental R programming concepts, data manipulation, visualization, and statistical analysis techniques. Learners will gain hands-on experience with popular R packages such as dplyr, ggplot2, and tidyr, enabling them to clean, analyze, and visualize data. The course also delves into advanced topics like machine learning, data modeling, and report generation using RMarkdown. Ideal for aspiring data scientists, analysts, and researchers, this course provides practical experience through real-world projects and case studies, preparing participants to tackle complex data challenges and make data-driven decisions in their professional roles.

Skills You Will Learn

R Programming Basics: Understand R syntax, data types, functions, loops, and control structures.
Data Manipulation: Clean, transform, and manipulate data using packages like dplyr and tidyr.
Data Visualization: Create compelling and informative visualizations with ggplot2.
Machine Learning: Implement machine learning algorithms such as linear regression, classification, clustering, and decision trees using R.
Data Import and Export: Import data from various sources (CSV, Excel, databases) and export analysis results.
Advanced R Packages: Utilize advanced R packages for specialized data analysis tasks and machine learning.
Reporting with RMarkdown: Generate dynamic reports and presentations using RMarkdown for reproducible research.
Data Wrangling: Perform complex data wrangling tasks to prepare data for analysis and modeling.
Interactive Data Applications: Build interactive web applications with Shiny for data analysis and visualization.
Project Application: Apply R skills to real-world projects and case studies to gain practical experience.
Problem-Solving: Develop critical thinking and problem-solving skills for data-driven challenges.
Continuous Learning: Cultivate a mindset for continuous learning to stay updated with advancements in R and data science technologies.

Key Highlights: R Programming for Data Science Course

Comprehensive R Fundamentals: Learn the basics of R programming, including syntax, data types, functions, and control structures.
Data Manipulation: Master data manipulation techniques using R packages like dplyr and tidyr for efficient data cleaning and transformation.
Machine Learning: Implement machine learning algorithms such as linear regression, classification, clustering, and decision trees using R.
Advanced R Packages: Explore advanced R packages for specialized data analysis tasks and machine learning.
Hands-On Projects: Apply your skills to real-world projects and case studies for practical understanding and experience.
Interactive Data Applications: Build interactive web applications with Shiny for data analysis and visualization.
Expert Instructors: Learn from experienced professionals with deep expertise in R and data science.
Certification: Earn a certificate upon completion to validate your R programming and data science skills.

Curriculum

72 Lessons23h 15m

Introduction to Data Science and R Programming

Overview of Data Science

The Role of R in Data Science

Setting Up R and RStudio

Basic R Programming Concepts (Data Types, Control Structures, Functions)

Introduction to R Packages (tidyverse, ggplot2, dplyr)

Data Importing and Data Wrangling

Exploratory Data Analysis (EDA)

Data Visualization

Introduction to Machine Learning

Supervised Learning – Regression

Supervised Learning – Classification

Unsupervised Learning

Advanced Machine Learning Techniques

Model Deployment and Productionization

Big Data Technologies with R

Advanced Analytics and Case Studies

Ethics and Best Practices in Data Science

Capstone Project

Your Instructors

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R. Analytics with Machine learning

Course Prerequisites

About This Course

Skills You Will Learn

Key Highlights: R Programming for Data Science Course

Curriculum

Introduction to Data Science and R Programming

Overview of Data Science

The Role of R in Data Science

Setting Up R and RStudio

Basic R Programming Concepts (Data Types, Control Structures, Functions)

Introduction to R Packages (tidyverse, ggplot2, dplyr)

Data Importing and Data Wrangling

Importing Data from Various Sources (CSV, Excel, SQL, JSON)

Data Cleaning Techniques

Handling Missing Values and Outliers

Data Transformation and Manipulation with dplyr

Using tidyr for Data Tidying

Exploratory Data Analysis (EDA)

Descriptive Statistics and Summary Statistics

Data Visualization Techniques with ggplot2

Identifying Patterns and Outliers

Correlation and Causation

Using R for Exploratory Data Analysis

Data Visualization

Principles of Effective Data Visualization

Creating Basic Plots (Histograms, Scatter Plots, Box Plots)

Advanced Visualization Techniques (Faceting, Theming)

Interactive Visualizations with plotly and Shiny

Creating Dashboards with R Shiny

Introduction to Machine Learning

Machine Learning Concepts and Terminology

Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

The Machine Learning Workflow

Introduction to the caret Package

Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

Supervised Learning – Regression

Simple Linear Regression

Multiple Linear Regression

Polynomial Regression

Regularization Techniques (Ridge, Lasso)

Evaluating Regression Models (R-squared, RMSE)

Supervised Learning – Classification

Logistic Regression

K-Nearest Neighbors (KNN)

Decision Trees and Random Forests

Support Vector Machines (SVM)

Evaluating Classification Models (Confusion Matrix, ROC Curve)

Unsupervised Learning

Introduction to Clustering

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Anomaly Detection Techniques

Advanced Machine Learning Techniques

Ensemble Learning Methods (Bagging, Boosting, Stacking)

Gradient Boosting Machines (xgboost, LightGBM, CatBoost)

Introduction to Neural Networks

Deep Learning with TensorFlow and Keras in R

Natural Language Processing (NLP) with R (text mining, tm, quanteda)

Time Series Analysis and Forecasting

Model Deployment and Productionization

Saving and Loading Models

Creating APIs for Model Deployment with Plumber

Using Docker for Containerization

Introduction to Cloud Platforms (AWS, Azure, GCP)

Continuous Integration and Continuous Deployment (CI/CD) for ML Models

Big Data Technologies with R

Introduction to Big Data and Hadoop

Working with Spark and the sparklyr Package

NoSQL Databases (MongoDB, Cassandra) with R

Data Lakes and Data Warehouses

Real-Time Data Processing with Kafka and R

Advanced Analytics and Case Studies

Predictive Analytics

Prescriptive Analytics

Text Analytics and Sentiment Analysis

Image Processing and Computer Vision with R

Case Studies from Various Industries (Finance, Healthcare, Retail)

Ethics and Best Practices in Data Science