Data Science with R - Beginners
3.4 (153 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.
16,271 students enrolled

Data Science with R - Beginners

This training is an introduction to the concept of Data science and its application using R programming language
3.4 (153 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.
16,271 students enrolled
Created by SimpliCode Point
Last updated 12/2018
English
English [Auto-generated]
Current price: $69.99 Original price: $99.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 6 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn R programming, Reproducible Analysis and Data Manipulation
  • Master data visualization, Learn working with Large Datasets, Supervised Learning and Unsupervised Learning
  • Learn Object oriented Programming, start building an R package. Know more on Testing and Package and checking Version Control and Profiling and Optimizing
Course content
Expand all 56 lectures 05:47:21
+ Tools
3 lectures 14:17
Purpose of using R Tool
02:21
Module on Data Visualization
02:01
+ Creating Pie Charts and Bar Chart
2 lectures 13:21
Creating Pie Charts
06:03
Creating Bar Charts
07:18
+ Creating Histograms
3 lectures 15:23
Functions of Histogram
04:48
Method of Using Scatterplots
05:13
Creating Data for Line Charts
05:22
+ Basic Data Visualization
2 lectures 10:05
Case Study for Vector Values
06:16
Module on Advanced Data Visualization
03:49
+ Ggplot Value
19 lectures 01:55:53
with Functions for Plotting Values
06:03
How to Plot Car Value
03:54
Understanding the Ggplot Value
03:58
Basic Example on Scatterplot
07:16
Scatterplot With Encircling
08:24
Learning the Jitter Plot
04:54
Counts Charts in Ggplot
03:40
Section on Bubble Chart
06:20
Diverging Bars with Ggplot
11:24
Diverging Lollipchart with Ggplot
05:34
Implementation of Dot Plot
05:48
Purpose of using Area Charts
09:04
Ordered Bar Chart for Multiple Items
08:25
Simple Demonstration on Pie Chart
07:25
Example on Hierarchical Dendrogram
03:36
Learning about the Population Pyramids
08:49
Understanding the Change Plot
02:43
Case Study on Seasonal Plot
05:29
Basic Understanding on Statistics
03:07
+ Regression
14 lectures 01:49:44
Implementation of Mean Median and Mode
08:56
Understanding the Linear Regression
09:36
Understanding Multiple Regression
08:44
Functions of Logistic Regression
07:44
Learning Normal Distribution Curve
09:07
Understanding the Binomial Distribution
05:54
Involvement of Poisson Regression
06:13
Analysis of Covariance
08:58
Time Series Analysis
10:55
Nonlinear Least Square
08:44
Section on Decision Tree
07:09
The Random Forest Approach
05:44
Learning the Chi Square Test
05:19
Case Study on Survival Analysis
06:41
+ Machine Learning and its Concepts
11 lectures 01:02:00
Understanding the Concept of Probability
04:27
Counting the Number of Combinations
04:23
Generating Random Numbers
07:12
Generating Random Sequences
03:12
Converting Probabilities to Quantiles
03:57
Criteria for Plotting a Density Function
06:06
Concept of Data Manipulation
03:57
Module on Machine Learning
08:00
Machine Learning Concepts with R
06:16
Machine Learning Datasets
06:47
Machine learning project with R
07:43
Requirements
  • No prior knowledge of machine learning required
  • Basic knowledge of R
Description

This training is an introduction to the concept of Data science domain and its application using R programming language. The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data. The tutorials will include the following;

  • Introduction to R programming

  • Reproducible Analysis

  • Data Manipulation

  • Visualizing Data

  • Working with Large Datasets

  • Supervised Learning

  • Unsupervised Learning

  • In depth R programming

  • Object oriented Programming

  • Building an R package

  • Testing and Package Checking

  • Version Control

  • Profiling and Optimizing

 

Who this course is for:
  • Anyone who wants to learn about data and analytics
  • Data Engineers
  • Analysts
  • Architects
  • Software Engineers
  • IT operations
  • Technical managers