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Data Analytics with R
Course Description
Overview
This Data Analytics with R course is designed to provide students with language fundamentals, libraries, and advanced concepts. Advanced data analytics and graphing with real world data.Objectives
- Understand the language basics of R
- Apply intermediate and advanced R programming
Audience
- Developers
- Data Analysts
Prerequisites
- Basic programming background
Topics
- About Data ScienceData Science Definition
- Process of Doing Data Science.
- Variables and Types
- Control Structures (Loops / Conditionals)
- R Scalars, Vectors, and MatricesDefining R Vectors
- Matricies
- Character data type
- File IO
- Functions
- Introducing Functions
- Closures
- lapply/sapply functions
- Labs for all sections
- Reading data from files
- Data Preparation
- Built-in Datasets
- Visualization
- Graphics Package
- plot() / barplot() / hist() / boxplot() / scatter plot
- Heat Map
- ggplot2 package ( qplot(), ggplot())
- Exploration With Dplyr
- Labs for all sections
- Dealing With NA
- Distributions (Binomial, Poisson, Normal)
- Introducing Linear Regressions
- Text Processing (tm package / Wordclouds)
- Clustering
- Introduction to Clustering
- KMeans
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Training using caret package
- Evaluating Algorithms
- Hadoop
- Big Data Ecosystem
- RHadoop
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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