Unit 1: Understanding Big Data
Unit 2: R Ecosystem for Big Data
Unit 3: Setting Up Your R Environment
Unit 4: Course Overview
R for Big Data Analytics: dplyr, data.table, sparklyr, and Parallel Computing
Master R for big data: from data manipulation with dplyr and data.table to distributed computing with sparklyr and parallel processing, empowering you to analyze and visualize massive datasets efficiently.
Unit 1: Introduction to dplyr
Unit 2: Selecting and Filtering Data
Unit 3: Transforming and Arranging Data
Unit 1: Grouping and Summarizing Data
Unit 2: Window Functions
Unit 3: Advanced Filtering Techniques
Unit 4: Optimizing dplyr Code
Unit 1: Understanding data.table
Unit 2: Basic Data Manipulation with data.table
Unit 3: data.table vs dplyr
Unit 1: Joins in data.table
Unit 2: Aggregations and Updates by Reference
Unit 3: Optimization and Time Series
Unit 1: Introduction to Parallel Computing
Unit 2: Getting Started with `future`
Unit 3: Advanced `future` Techniques
Unit 4: Parallelizing dplyr with `furrr`
Unit 1: Spark and sparklyr: The Basics
Unit 2: Data Transfer and Spark DataFrames
Unit 3: Lazy Evaluation and Spark SQL
Unit 1: Sparklyr Data Manipulation Basics
Unit 2: dplyr Verbs in sparklyr
Unit 3: Advanced sparklyr Data Manipulation
Unit 1: Introduction to Machine Learning with Sparklyr
Unit 2: Building and Evaluating Machine Learning Models
Unit 3: Advanced Machine Learning Techniques
Unit 4: Model Tuning, Selection, and Deployment
Unit 1: Generalized Linear Models (GLMs)
Unit 2: Mixed-Effects Models
Unit 3: Time Series Analysis
Unit 1: ggplot2 Fundamentals
Unit 2: Common Plot Types
Unit 3: Customization and Advanced Techniques
Unit 1: Introduction to Shiny
Unit 2: Inputs, Outputs, and Reactivity
Unit 3: Data Visualization and Deployment
Unit 1: Identifying Bottlenecks
Unit 2: Optimizing for Speed
Unit 3: Optimizing for Memory
Unit 4: Best Practices
Unit 1: E-commerce Analytics
Unit 2: Financial Services Analytics
Unit 3: Healthcare Analytics