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Course Outline
Day One: Language Basics
- Course Introduction
- About Data Science
- Data Science Definition
- Process of Doing Data Science.
- Introducing R Language
- Variables and Types
- Control Structures (Loops / Conditionals)
- R Scalars, Vectors, and Matrices
- Defining R Vectors
- Matricies
- String and Text Manipulation
- Character data type
- File IO
- Lists
- Functions
- Introducing Functions
- Closures
- lapply/sapply functions
- DataFrames
- Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- 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
Day Three: Advanced Programming With R
- Statistical Modeling With R
- Statistical Functions
- Dealing With NA
- Distributions (Binomial, Poisson, Normal)
- Regression
- Introducing Linear Regressions
- Recommendations
- Text Processing (tm package / Wordclouds)
- Clustering
- Introduction to Clustering
- KMeans
- Classification
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Training using caret package
- Evaluating Algorithms
- R and Big Data
- Connecting R to databases
- Big Data Ecosystem
- Labs for all sections
Requirements
- Basic programming background is preferred
Setup
- A modern laptop
- Latest R studio and R environment installed
21 Hours
Testimonials (4)
I liked that the trainer made sure we all understood and were following the lectures. if we had a problem, he stopped and helped us fix it.
Cesar - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
The tool was interesting and I see the use. I would like to learn about more about it.
- Teleperformance
Course - Data Analytics With R
New tool which is “R” and I find it interesting to know the existence of such tool for data analysis.
Michael Lopez - Teleperformance
Course - Data Analytics With R
I get answers on all my questions.
