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Data Visualization using R Programming
1 students

Data Visualization using R Programming

Learn why, what, how about data visualization in simple and easy way by using R
Created byDATAhill Solutions Srinivas Reddy
Last updated 1/2020
English

What you'll learn

  • 1. Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts
  • 2. Box Plots
  • 3. Histograms & Line Graphs
  • 4. Scatter Plots & Scatterplot Matrices
  • 5. Low Level Plotting
  • 6. Bar Plot & Density Plot
  • 7. Combining Plots
  • 8. Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot
  • 9. MatPlot, ECDF & BoxPlot with IRIS Dataset
  • 10. Additional BoxPlot Style Parameters
  • Advanced Statistics

Course content

1 section • 15 lectures • 10h 15m total length

  • Introduction1:34

    Explore data visualization with R programming, using pure graphical representations for statistical analysis. Create pie charts, 3D plots, master low- and high-level plotting, and combine visuals without external tools.

  • 1. Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts59:20

    Explore data visualization in R, focusing on pie charts, 3D pie charts, and bar charts, with guidance on high-level vs low-level plotting, labels, colors, and legends.

  • 2. Box Plots54:38

    Explore creating box plots, bar charts, and pie charts in R. Learn to customize plots, save outputs, and interpret box plots with quartiles, median, and whiskers.

  • 3. Histograms & Line Graphs45:26

    Master histograms and line graphs in data visualization using R, building bar charts, box plots, and frequency distributions with xlab, ylab, main, colors, borders, and breaks.

  • 4. Scatter Plots & Scatterplot Matrices1:03:47

    Learn to create and interpret scatter plots and scatterplot matrices in R using base plotting, including customizing axes, labels, and exploring relationships between multiple car attributes.

  • 5. Low Level Plotting56:01

    Explore low-level and high-level plotting in R with box plots, scatter plots, and customization, using car and credit data to analyze distributions and relations.

  • 6. Bar Plot & Density Plot46:31

    Master bar plots and density plots in R, learn to generate multiple plots in one graph, and apply box plots, histograms, and real-time data analysis.

  • 7. Combining Plots35:37

    Learn to combine multiple plots in one graph in R using par and layout, structuring 2x2 grids of bar, scatter, box, and density plots with flexible placement.

  • 8. Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot51:07

    Learn to combine and arrange multiple plots in R using layout and matrix specifications, and create scatter plots, histograms, box plots, and pie charts for basic data visualization.

  • 9. MatPlot, ECDF & BoxPlot with IRIS Dataset1:02:55

    Explore data visualization in r programming by plotting multiple series with matplot, computing empirical cumulative distribution with ecdf, and crafting box plots using the iris dataset.

  • 10. Additional BoxPlot Style Parameters1:01:41

    Explore box plots in R and customize them with additional style parameters such as colors, fills, median lines, outlier handling, and whisker types, using iris data and boxplot examples.

  • 11. What is Data Science30:42

    Explore what data science encompasses, including statistics, data analysis, machine learning, data processing, visualization, and the handling of structured and unstructured data.

  • 12. What is Machine Learning25:06

    Explore fundamentals of machine learning, including supervised and reinforcement learning, compare with traditional programming, and review data science workflows from data collection to model evaluation and real‑world applications.

  • 13. Data Visualization using Pandas19:33

    Visualize a used cars data set with Pandas, exploring distribution, mean and median, and using box plots and scatter plots to examine price, model, color, and transmission.

  • Summary1:42

    Compare Python and R for graphical representations in statistical analysis and explore maps as data visualization techniques.

Requirements

  • Having a basic understanding about statistics
  • Basic Statistics

Description

  • If you want to work in exciting analytics and data visualization project, then this is the starting point for you.

  • Data is the currency of now and potential to use it the right way, at the right time for the right reason gives you possibility beyond imagination.

  • Data visualization is a vast topic and consist of many sub-parts which are a subject in itself, we in our course have tried to paint a clear picture of what you need to know and what people will be looking of you in a visualization project.

  • UX in Data visualization is key in modern times to meet the expectation of your user, this course will highlight what are the benefits of using a good UX and how to do it.

  • This course is structured to provide all the key aspect of Data visualization in most simple and clear fashion.So you can start the journey in Data visualization world.


1. Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts

2. Box Plots

3. Histograms & Line Graphs

4. Scatter Plots & Scatterplot Matrices

5. Low Level Plotting

6. Bar Plot & Density Plot

7. Combining Plots

8. Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot

9. MatPlot, ECDF & BoxPlot with IRIS Dataset

10. Additional BoxPlot Style Parameters

Who this course is for:

  • Beginner Python developers curious about Data Science
  • Beginner to advanced level

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