R Programming: R for Data Science and Data Analytics A-Z™
4.1 (24 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,123 students enrolled

R Programming: R for Data Science and Data Analytics A-Z™

Learn R Programming Hands-on - Vectors and Data Frames, R Packages & Functions, R in Data Visualization, Apply R for ML
4.1 (24 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,123 students enrolled
Last updated 12/2019
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Current price: $12.99 Original price: $19.99 Discount: 35% off
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This course includes
  • 7.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Install R and R studio on Windows and Ubuntu machine.
  • The core principles of R programming.
  • Manage R Packages and working directory.
  • Build user defined functions.
  • R’s Decision Branching methods and loop operations.
  • About Data types and Data structures.
  • Operations on Vectors, Lists, Matrices, Arrays and Data frames.
  • Manage data from External Sources (csv, Excel, JSON and XML files).
  • Arrange Factor Data and the process of conversion ( vector to factor)
  • Work with External Database.
  • Visualize data in a structured way using ggplot2 package.
  • Understand the statistical concepts (like. Mean, Median, Correlation, Standard deviation, Normal Distribution) with proper R examples.
  • Hypothesis testing in R ( t-test & Chi Squared Test )
  • The concept of Missing Value and their imputation process.
  • Detect and Remove the outliers from data set.
  • The concept, application, Mathematical computation and a complete data analysis using Simple Linear regression.
  • Build and interpret a multiple linear regression model in R and also check the overall quality of the model.
  • Generate a Logistic Regression Model, Predict the outcome from LR model and evaluate your model using Confusion Matrix and ROC- AUC Curve.
Course content
Expand all 34 lectures 07:28:52
+ 01. Exploring R
5 lectures 56:56
1.2 Installation of R / R studio
13:09
1.4 Working with Packages
14:42
1.5 Managing R workplace
12:19
+ 02. R Programming start-up
8 lectures 01:42:59
2.1 R Syntax
09:51
2.2 Variables
08:46
2.3 Data Types
14:59
2.4 Operators
16:57
2.5 Decision Branching
12:07
2.6 Loop Operation
10:07
2.7 String Operation
16:21
2.8 function
13:51
+ 03. Data Handling
6 lectures 01:16:43
3.1 Working with Vector
13:01
3.2 Manage your List
10:40
3.3 Matrix Operations
16:49
3.4 Multi-Dimensional Data
10:55
3.5 Data frame
15:19
3.6 Categorical data
09:59
+ 04. Manage data from External Files
5 lectures 59:47
4.1 Export Import CSV file
13:23
4.2 Importing Excel Data
08:06
4.3 Importing JSON Data
10:49
4.4 Working with XML data
13:08
4.5 Connect to External Database
14:21
+ 05. Data Visualization
3 lectures 48:02
5.1 R Graphics starter
15:59
5.2 More R Graphs
15:58
5.3 Graphics with ggplot
16:05
+ 06 Data Pre-processing
4 lectures 55:01
6.1 Statistical functions (Part 1 & 2)
12:09
6.1 Statistical functions (Part 2)
09:41
6.2 Missing Value Analysis
18:50
6.3 Outlier Detection
14:21
+ 07. Machine Learning with R
3 lectures 49:24
7.2 Simple Linear Regression
17:30
7.3 Multiple Linear Regression
12:03
7.4 Logistic Regression (Classification)
19:51
Requirements
  • Knowledge of Basic Statistics
  • General idea how programing language works
Description

R programming for Data Science and Data Analytics:

Data analysis is one of the leading jobs in the current technology market. As per the forecasts of Glassdoor and World Economic Forum, the demand for data scientists will also increase in the next few years. We are generating huge data every day from different domains like Social Media, Healthcare, Sensor data… we have a great tool to analyze them and the tool is R. R programming is a powerful language used widely for data analysis and statistical computing. It is completely free and has rich repositories for packages.

In this course first, you will learn how to install R and start programming on it. It will also help you to know the programming structures and functions. This R programming in Data Science and Data Analytics covers all the steps of Exploratory data analysis, Data pre-processing, and Modelling process. In EDA sections you will learn how to import data sets and create data frames from it. Then it will help you to visualize the variables using different plots. It will give you an initial structure of your data points. In Data pre-processing sections you will get the full idea of Missing value & outliers treatment and data split methods. Finally, you will be able to generate machine learning models using Linear and Logistic Regression.

This R programming for data science and data analytics is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

Who this course is for:
  • Aspiring data scientists
  • Anyone interested in Statistical Analysis.
  • If you want to learn R programming in easy steps
  • This course is for you if you are tired of R courses that are too complicated
  • This course is for you if you want to learn R Hands-on