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  • Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts.
  • Box Plots.
  • Histograms & Line Graphs.
  • Scatter Plots & Scatterplot Matrices.
  • Low Level Plotting.
  • Bar Plot & Density Plot.
  • Combining Plots.
  • Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot.
  • MatPlot, ECDF & BoxPlot with IRIS Dataset.
  • Additional BoxPlot Style Parameters.

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

  • Having a basic understanding about statistics.
  • Beginner Python developers curious about Data Science.
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  • Section 1 : Introduction. 1 Lectures 00:01:34

    • Lecture 1 :
  • Section 2 : Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts. 1 Lectures 00:59:20

    • Lecture 1 :
    • Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts.
  • Section 3 : Box Plots. 1 Lectures 00:54:38

    • Lecture 1 :
    • Box Plots.
  • Section 4 : Histograms & Line Graphs. 1 Lectures 00:45:26

    • Lecture 1 :
    • Histograms & Line Graphs.
  • Section 5 : Scatter Plots & Scatterplot Matrices. 1 Lectures 01:03:47

    • Lecture 1 :
    • Scatter Plots & Scatterplot Matrices.
  • Section 6 : Low Level Plotting. 1 Lectures 00:56:01

    • Lecture 1 :
    • Low Level Plotting.
  • Section 7 : Bar Plot & Density Plot. 1 Lectures 00:46:31

    • Lecture 1 :
    • Bar Plot & Density Plot.
  • Section 8 : Combining Plots. 1 Lectures 00:35:37

    • Lecture 1 :
    • Combining Plots.
  • Section 9 : Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot. 1 Lectures 00:51:07

    • Lecture 1 :
    • Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot.
  • Section 10 : MatPlot, ECDF & BoxPlot with IRIS Dataset. 1 Lectures 01:02:55

    • Lecture 1 :
    • MatPlot, ECDF & BoxPlot with IRIS Dataset.
  • Section 11 : Additional BoxPlot Style Parameters. 1 Lectures 01:01:41

    • Lecture 1 :
    • Additional BoxPlot Style Parameters.
  • Section 12 : What is Data Science. 1 Lectures 00:30:42

    • Lecture 1 :
    • What is Data Science.
  • Section 13 : What is Machine Learning. 1 Lectures 00:25:06

    • Lecture 1 :
    • What is Machine Learning.
  • Section 14 : Data Visualization using Pandas. 1 Lectures 00:19:33

    • Lecture 1 :
    • Data Visualization using Pandas.
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7 Courses

Having 10+ Years of Experience in Software Industry which includes Development, Support & Training. My Experience Includes Managing, Processing, Predicting and Analyzing of Large volume of Business Data. Expertise in Data Management, BI Technologies & Data Science with Data Analytics, Machine Learning, Deep Learning & Artificial Intelligence using R Programming, Python Programming, WEKA and EXCEL. Having publications and patents in various fields such as machine learning, data security, and data science technologies. I received my Masters of Technology in Computer Science & Engineering from JNTU. Professionally, I am a Data Science management consultant with over 8 years of experience in finance, retail, transport and other industries.
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