Introduction To Data Science Using R Programming Online Course

Introduction To Data Science Using R Programming

All levels
English

Course Description

The power of data is undeniable, especially organized data. This is why currently data scientists rake in an average salary of over $100,000! This is also why Big Data and Data Analytics have become hot topics in todays world. All things considered we have designed this course aimed at complete beginners as well as intermediate students who want to master the art of data analytics and learn exa...

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What you’ll learn

Basic Data Visualization
Advanced Data Visualization
Generating Maps using JSON Structures
Implementation of Statistics
Data Munging/Wrangling
Data Manipulation - Import/Export of Data into CSV or Excel Format

Covering Topics

Section 1 : introduction

Section 2 : Basics of R tool

Section 3 : Basic Data Visualization

Section 4 : Advanced Data Visualization

Section 5 : Leaflet Maps

Section 6 : Statistics

Section 7 : Data Manipulation

Curriculum

  Section 1 : introduction
1
Intro

  Section 2 : Basics of R tool
2
Introduction to Course
3
R programming installation and concepts
4
R programming computations

  Section 3 : Basic Data Visualization
5
Data Visualization - Module Preview
6
Pie charts
7
Bar charts
8
Boxplots
9
Histograms
10
Line charts Preview
11
Scatterplots
12
Case Study Basic data visualization

  Section 4 : Advanced Data Visualization
13
Advanced Data Visualization
14
Basic Illustration of ggplot2 package
15
Facetting Preview
16
Boxplots and Jittered Plots
17
Histograms and Frequency Polygons
18
Bar Charts and Time Series
19
Basic Plot Types
20
Case Study for ggplot2 package Scatterplot Encircling
21
Surface Plots
22
Revealing uncertainity
23
Weighted data
24
Drawing Maps- Vector Boundries
25
Drawing Maps - Point Metadata
26
Diamonds data for research
27
Dealing with overlapping
28
Statistical summaries
29
Scatterplot from excel file
30
Heatmap and area chart from excel file
31
Various bar charts from excel file

  Section 5 : Leaflet Maps
32
Implementing Leaflet with R tool
33
Adding Markers in map
34
Popups and Labels
35
Shiny Framework using Leaflet and R

  Section 6 : Statistics
36
Mean, median and mode
37
Linear Regression
38
Multiple Regression
39
Logistic Regression
40
Normal Distribution
41
Binomial Distribution
42
Poisson Regression
43
Analysis of Covariance
44
Time Series Analysis
45
Case study Time Series from dataset
46
Decision Tree
47
Implementation of decision tree in Dataset
48
Nonlinear Least Square
49
Case Study- Random Forest
50
Survival Analysis

  Section 7 : Data Manipulation
51
Case Study Exporting data in R
52
Data Munging and Visualization
53
Hierarchial Clustering
54
K means clustering

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