Learn Data Science With R Part 3 of 10
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# Learn Data Science With R Part 3 of 10

Graphs and Cleaning Data
New
0.0 (0 ratings)
5 students enrolled
Created by Ram Reddy
Last updated 8/2017
English
Price: \$200
30-Day Money-Back Guarantee
Includes:
• 8 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• Graphs
View Curriculum
Requirements
• Complete Learn Data Science With R Part 1 of 10 and Learn Data Science With R Part 2 of 10
Description

You will learn

Data Science Graphs Types like base plots,grid plots,lattice plots and ggplots

base plots are default and easy plots to start so i will start woth that and ggplot is very good and bit complex so i end with this.

ggplot has seven layers

1. data

2. aes

3.geom

4.themes

5.stats

6.cordinates

7.facets

ggplot is the one great package to achieve gramer of graphics

Who is the target audience?
• Who want to learn deep about graphs using ggplot2 package
Compare to Other Data Science Courses
Curriculum For This Course
8 Lectures
08:02:36
+
Graphs and Cleaning Data
8 Lectures 08:02:36
Preview 01:03:14

Data Science Tutorial Part 17 Base package Graphs Part 2
59:09

Data Science Tutorial Part 18 Base package Graphs Part 3
58:45

Data Science Tutorial Part 19 Grid and Lattice graphs
01:02:43

Data Science Tutorial Part 20 ggplot2 graphs Part 1
01:04:41

Data Science Tutorial Part 21 ggplot2 graphs Part 2
56:15

Data Science Tutorial Part 22 ggplot2 graphs Part 3
55:56

Data Science Tutorial Part 23 Cleaning Data
01:01:53
 4.4 Average rating 55 Reviews 5,010 Students 9 Courses
Data Scientist

Data scientist and founder of RRITEC, a company dedicated to helping scientists better understand and visualize their data. Ram Has hands-on exposure to a wide variety of datasets has informed him of the many problems scientists face when trying to visualize their data.

Some of the main roles are

Develop and improve robust predictive algorithms that are the core of the product

Combine an understanding of business goals with data analysis and machine learning

Investigate new data sources; acquire, analyze, clean and structure data

Utilize state of the art machine learning techniques to improve and expand existing models