R Programming Language

R Programming Language, available at $59.99, has an average rating of 4.15, with 82 lectures, based on 101 reviews, and has 652 subscribers.

You will learn about R Programming Language for Statistical Computing and Graphical Representation This course is ideal for individuals who are All graduates and pursuing students. It is particularly useful for All graduates and pursuing students.

Enroll now: R Programming Language

Summary

Title: R Programming Language

Price: $59.99

Average Rating: 4.15

Number of Lectures: 82

Number of Published Lectures: 82

Number of Curriculum Items: 82

Number of Published Curriculum Objects: 82

Original Price: ?7,900

Quality Status: approved

Status: Live

What You Will Learn

R Programming Language for Statistical Computing and Graphical Representation

Who Should Attend

All graduates and pursuing students.

Target Audiences

All graduates and pursuing students.

This course is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

Before proceeding with this course, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.

Course Curriculum

Chapter 1: R Programming Language

Lecture 1: Introduction to R Programming

Lecture 2: R Installation & Setting R Environment

Lecture 3: Variables, Operators & Data types

Lecture 4: Structures

Lecture 5: Vectors

Lecture 6: Vector Manipulation & Sub-Setting

Lecture 7: Constants

Lecture 8: RStudio Installation & Lists Part 1

Lecture 9: Lists Part 2

Lecture 10: List Manipulation, Sub-Setting & Merging

Lecture 11: List to Vector & Matrix Part 1

Lecture 12: Matrix Part 2

Lecture 13: Matrix Accessing

Lecture 14: Matrix Manipulation, rep function & Data Frame

Lecture 15: Data Frame Accessing

Lecture 16: Column Bind & Row Bind

Lecture 17: Merging Data Frames Part 1

Lecture 18: Merging Data Frames Part 2

Lecture 19: Melting & Casting

Lecture 20: Arrays

Lecture 21: Factors

Lecture 22: Functions & Control Flow Statements

Lecture 23: Strings & String Manipulation with Base Package

Lecture 24: String Manipulation with Stringi Package Part 1

Lecture 25: String Manipulation with Stringi Package Part 2 & Date and Time Part 1

Lecture 26: Date and Time Part 2

Lecture 27: Data Extraction from CSV File

Lecture 28: Data Extraction from EXCEL File

Lecture 29: Data Extraction from CLIPBOARD, URL, XML & JSON Files

Lecture 30: Introduction to DBMS

Lecture 31: Structured Query Language, MySQL Installation & Normalization

Lecture 32: Data Definition Language Commands

Lecture 33: Data Manipulation Language Commands

Lecture 34: Sub Queries & Constraints

Lecture 35: Aggregate Functions, Clauses & Views

Lecture 36: Data Extraction from Databases Part 1

Lecture 37: Data Extraction from Databases Part 2 & DPlyr Package Part 1

Lecture 38: DPlyr Package Part 2

Lecture 39: DPlyr Functions on Air Quality Data Set

Lecture 40: Plyr Package for Data Analysis

Lecture 41: Tidyr Package with Functions

Lecture 42: Factor Analysis

Lecture 43: Prob.Table & CrossTable

Lecture 44: Statistical Observations Part 1

Lecture 45: Statistical Observations Part 2

Lecture 46: Statistical Analysis on Credit Data set

Lecture 47: Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts

Lecture 48: Box Plots

Lecture 49: Histograms & Line Graphs

Lecture 50: Scatter Plots & Scatter plot Matrices

Lecture 51: Low Level Plotting

Lecture 52: Bar Plot & Density Plot

Lecture 53: Combining Plots

Lecture 54: Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot

Lecture 55: Mat Plot, ECDF & Box Plot with IRIS Data set

Lecture 56: Additional Box Plot Style Parameters

Lecture 57: Set.Seed Function & Preparing Data for Plotting

Lecture 58: QPlot, ViolinPlot, Statistical Methods & Correlation Analysis

Lecture 59: ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal

Lecture 60: Data Exploration and Visualization

Lecture 61: Machine Learning, Types of ML with Algorithms

Lecture 62: How Machine Solve Real Time Problems

Lecture 63: Nearest Neighbor(KNN) Classification

Lecture 64: KNN Classification with Cancer Data set Part 1

Lecture 65: KNN Classification with Cancer Data set Part 2

Lecture 66: Navie Bayes Classification

Lecture 67: Navie Bayes Classification with SMS Spam Data set & Text Mining

Lecture 68: 68. WordCloud & Document Term Matrix

Lecture 69: Train & Evaluate a Model using Navie Bayes

Lecture 70: MarkDown using Knitr Package

Lecture 71: Decision Trees

Lecture 72: Decision Trees with Credit Data set Part 1

Lecture 73: Decision Trees with Credit Data set Part 2

Lecture 74: Support Vector Machine, Neural Networks & Random Forest

Lecture 75: Regression & Linear Regression

Lecture 76: Multiple Regression

Lecture 77: Generalized Linear Regression, Non Linear Regression & Logistic Regression

Lecture 78: 78. Clustering

Lecture 79: K-Means Clustering with SNS Data Analysis

Lecture 80: Association Rules (Market Basket Analysis)

Lecture 81: Market Basket Analysis using Association Rules with Groceries Data set

Lecture 82: Python Libraries for Data Science

Instructors

DATAhill Solutions Srinivas Reddy
Data Scientist

Rating Distribution

1 stars: 5 votes

2 stars: 6 votes

3 stars: 19 votes

4 stars: 51 votes

5 stars: 20 votes

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