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What you'll learn?
- 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.
Course Overview
- 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.
Pre-requisites
- Having a basic understanding about statistics.
Target Audience
- Beginner Python developers curious about Data Science.
Curriculum 14 Lectures 10:13:58
-
Section 1 : Introduction.
-
Section 2 : Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts.
- Lecture 1 :
- Data Visualization using R, Pie Charts, 3D Pie Charts & Bar Charts.
-
Section 3 : Box Plots.
- Lecture 1 :
- Box Plots.
-
Section 4 : Histograms & Line Graphs.
- Lecture 1 :
- Histograms & Line Graphs.
-
Section 5 : Scatter Plots & Scatterplot Matrices.
- Lecture 1 :
- Scatter Plots & Scatterplot Matrices.
-
Section 6 : Low Level Plotting.
- Lecture 1 :
- Low Level Plotting.
-
Section 7 : Bar Plot & Density Plot.
- Lecture 1 :
- Bar Plot & Density Plot.
-
Section 8 : Combining Plots.
- Lecture 1 :
- Combining Plots.
-
Section 9 : Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot.
- Lecture 1 :
- Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot.
-
Section 10 : MatPlot, ECDF & BoxPlot with IRIS Dataset.
- Lecture 1 :
- MatPlot, ECDF & BoxPlot with IRIS Dataset.
-
Section 11 : Additional BoxPlot Style Parameters.
- Lecture 1 :
- Additional BoxPlot Style Parameters.
-
Section 12 : What is Data Science.
- Lecture 1 :
- What is Data Science.
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Section 13 : What is Machine Learning.
- Lecture 1 :
- What is Machine Learning.
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Section 14 : Data Visualization using Pandas.
- Lecture 1 :
- Data Visualization using Pandas.
Our learners work at
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Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic. -
Can i play & pause the course as per my convenience?
Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on. -
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Instructor
399712 Course Views
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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