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Introduction To Data Science Using R Programming

Video Description

Learn and master data science concepts such as analytics and visualization using R.

About This Video

  • Implement data science concepts using R
  • Implement statistical models using R

In Detail

Data was once only powerful when it came to making business decisions, but today data plays a more important role and is currently the basis of all modern business functions. This course focuses on helping to breakdown R and the R programming language into simple and easy to understand concepts that cover everything you need to know about how to get started with data science. The course will not only help you learn the R language’s basic syntax, but also the computing environment where you will learn exactly how to import data, organize the data, create charts and graphs, and also export data.

The course will cover topics in-depth such as basic data visualization, advanced data visualization, generating maps using JSON structures, implementation of statistics, data munging/wrangling, data manipulation and so much more!

Let see what this course covers:

  • 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

At the end of this course, you will have mastered exactly how to clean and organize data as well as how to import and export data to R! This is the perfect course for anyone who is looking to make the jump into the world of Data Science.

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Introduction
    1. Introduction 00:01:53
  2. Chapter 2 : Basics of R tool
    1. Introduction to Course 00:06:01
    2. R programming installation and concepts 00:07:31
    3. R programming computations 00:09:16
  3. Chapter 3 : Basic Data Visualization
    1. Data Visualization - Module 00:05:07
    2. Pie charts 00:08:15
    3. Bar charts 00:12:36
    4. Boxplots 00:10:19
    5. Histograms 00:07:15
    6. Line charts 00:07:29
    7. Scatterplots 00:10:14
    8. Case Study Basic data visualization 00:04:31
  4. Chapter 4 : Advance Data Visualization
    1. Advanced Data Visualization 00:02:22
    2. Basic Illustration of ggplot2 package 00:08:34
    3. Facetting 00:07:39
    4. Boxplots and Jittered Plots 00:04:25
    5. Histograms and Frequency Polygons 00:07:47
    6. Bar Charts and Time Series 00:12:59
    7. Basic Plot Types 00:08:11
    8. Case Study for ggplot2 package Scatterplot Encircling 00:07:44
    9. Surface Plots 00:07:33
    10. Revealing uncertainity 00:07:31
    11. Weighted data 00:09:12
    12. Drawing Maps- Vector Boundries 00:06:23
    13. Drawing Maps - Point Metadata 00:06:13
    14. Diamonds data for research 00:10:32
    15. Dealing with overlapping 00:08:41
    16. Statistical summaries 00:07:29
    17. Scatterplot from excel file 00:09:36
    18. Heatmap and area chart from excel file 00:09:19
    19. Various bar charts from excel file 00:10:27
  5. Chapter 5 : Advance Data Visualization
    1. Implementing Leaflet with R tool 00:05:48
    2. Adding Markers in map 00:04:34
    3. Popups and Labels 00:10:48
    4. Shiny Framework using Leaflet and R 00:09:14
  6. Chapter 6 : Statistics
    1. Mean, median and mode 00:10:57
    2. Linear Regression 00:08:40
    3. Multiple Regression 00:09:27
    4. Logistic Regression 00:06:49
    5. Normal Distribution 00:09:39
    6. Binomial Distribution 00:06:31
    7. Poisson Regression 00:05:49
    8. Analysis of Covariance 00:08:16
    9. Time Series Analysis 00:10:06
    10. Case study Time Series from dataset 00:04:18
    11. Decision Tree 00:07:06
    12. Implementation of decision tree in Dataset 00:04:13
    13. Nonlinear Least Square 00:07:50
    14. Case Study- Random Forest 00:07:09
    15. Survival Analysis 00:07:10
  7. Chapter 7 : Data Manipulation
    1. Case Study Exporting data in R 00:09:37
    2. Data Munging and Visualization 00:07:57
    3. Hierarchial Clustering 00:06:26
    4. K means clustering 00:07:49