English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 6 Hours | 1.37 GB
Learn data preprocessing, data wrangling, and data visualization for hands-on data science and data analytics applications in R
This course is a sure-fire way to acquire the knowledge and statistical data analysis wrangling and visualization skills you need.
HERE IS WHAT THIS COURSE WILL DO FOR YOU:
- It will introduce some of the most important data visualization concepts to you in a practical manner so that you can apply these concepts to practical data analysis and interpretation.
- You will also be able to decide which wrangling and visualization techniques are best suited to answering your research questions and applicable to your data, and you’ll interpret the results.
- The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners
- After each video, you will learn a new concept or technique which you can apply to your own projects immediately! You’ll reinforce your knowledge through practical quizzes and assignments
Learn
- Read in data into the R environment from different sources
- Carry out basic data pre-processing & wrangling in R Studio
- Learn to identify which visualizations should be used in any given situation
- Build powerful visualizations and graphs from real data
- Apply data visualization concepts to practical data analysis & interpretation
Table of Contents
Welcome to The Course
1 Introduction to the Course and Instructor
2 Install R and RStudio
Read in Data from Different Sources
3 Read in CSV and Excel Data
4 Read in Unzipped Folder
5 Read in Online CSV
6 Read in Googlesheets
7 Read in Data from Online HTML Tables-Part 1
8 Read in Data from Online HTML Tables-Part 2
9 Read Data from a Database
Common Data Pre-Processing Techniques
10 Basic Data Cleaning in R – Remove NA
11 Additional Data Cleaning
12 Indexing and Subsetting Data
13 Summarising Based on Qualitative Attributes
14 Of Long and Wide
15 Pre-processing Tasks and the Pipe Operator
16 Introduction to dplyr for Data Summarizing-Part 1
17 Introduction to dplyr for Data Summarizing-Part 2
18 Start with Tidyverse
19 Column Renaming
20 Tidy Data – Long and Wide
21 Joining Tables
Basic Data Visualization
22 What is Data Visualization
23 Some Principles of Data Visualization
24 Exploratory Data Analysis (EDA) in R
25 More Exploratory Data Analysis with xda
Grammar of Graphics – ggplot2
26 Start with qplot
27 More qplot Visualizations
28 Start with ggplot
29 Scatterplots with ggplot2
30 Faceting With ggplot2
31 More Faceting
32 Insert a Smoothing Line
33 Boxplots
34 More Boxplots
35 Histograms
36 Barplots For Discrete Numerical Variables
37 Insert Error Bars
38 Line Charts
39 Additional ggplot2 Themes
Real Life Data Wrangling and Visualization
40 Use dplyr and ggplot
41 What and How Can We Learn From Data – Nobel Prize Winners
42 Mining More Information About Nobel Prizes
43 Mining and Visualising Information About the Olympic Games-Part 1
44 Of Winter and Summer Olympic Games
45 Of Men and Women
Geographic Visualisations
46 Brief Introduction
47 Work with R’s Inbuilt Geospatial Data-Part 2
48 Use ggplot2 For Geographic Data Visualisations
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