Udemy - Mastering Data Visualization with Python

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Mastering Data Visualization with Python [TutsNode.com] - Mastering Data Visualization with Python 05 Python for Absolute Beginners
  • 073 Pandas DataFrame - Dealing with Rows.mp4 (143.6 MB)
  • 055 Section-5c-Pandas-Refresher.ipynb (104.6 KB)
  • 055 Section-5a-Python-Refresher.ipynb (35.8 KB)
  • 073 Pandas DataFrame - Dealing with Rows.en.srt (35.1 KB)
  • 060 List - Part 1.en.srt (21.9 KB)
  • 066 Numpy - Part 1.en.srt (20.6 KB)
  • 058 Getting Started with Python.en.srt (18.4 KB)
  • 071 Importing .csv Files as DataFrame.en.srt (17.1 KB)
  • 055 Section-5b-Numpy-Refresher.ipynb (16.9 KB)
  • 067 Numpy - Part 2.en.srt (16.1 KB)
  • 069 Pandas - Series and DataFrame.en.srt (14.8 KB)
  • 072 Pandas DataFrame - Dealing with Columns.en.srt (14.3 KB)
  • 055 Piece-Dim.csv (1.6 KB)
  • 057 Jupyter Notebook.en.srt (12.3 KB)
  • 068 Numpy - Part 3.en.srt (11.9 KB)
  • 070 Pandas DataFrame.en.srt (10.3 KB)
  • 055 Download Section 5 Resources.html (1.0 KB)
  • 062 Dictionary.en.srt (9.5 KB)
  • 069 Pandas - Series and DataFrame.mp4 (87.0 MB)
  • 056 Installing Anaconda.en.srt (9.4 KB)
  • 063 Tuple.en.srt (4.6 KB)
  • 061 List - Part 2.en.srt (8.4 KB)
  • 059 Variables and Types.en.srt (8.4 KB)
  • 065 Logical Operators.en.srt (4.5 KB)
  • 064 Set.en.srt (4.1 KB)
  • 060 List - Part 1.mp4 (83.5 MB)
  • 071 Importing .csv Files as DataFrame.mp4 (79.3 MB)
  • 067 Numpy - Part 2.mp4 (75.1 MB)
  • 066 Numpy - Part 1.mp4 (73.4 MB)
  • 058 Getting Started with Python.mp4 (69.4 MB)
  • 072 Pandas DataFrame - Dealing with Columns.mp4 (62.5 MB)
  • 056 Installing Anaconda.mp4 (59.7 MB)
  • 070 Pandas DataFrame.mp4 (52.9 MB)
  • 057 Jupyter Notebook.mp4 (51.9 MB)
  • 068 Numpy - Part 3.mp4 (49.0 MB)
  • 062 Dictionary.mp4 (42.9 MB)
  • 059 Variables and Types.mp4 (38.3 MB)
  • 061 List - Part 2.mp4 (34.8 MB)
  • 063 Tuple.mp4 (25.5 MB)
  • 065 Logical Operators.mp4 (16.9 MB)
  • 064 Set.mp4 (15.9 MB)
01 Introduction
  • 002 Section-1-Python-Refresher.ipynb (77.3 KB)
  • 002 Download Section 1 Resources.html (0.9 KB)
  • 003 Python Refresher - Part 1.en.srt (22.6 KB)
  • external-assets-links.txt (0.1 KB)
  • 006 Pandas Refresher.en.srt (16.0 KB)
  • 004 Python Refresher - Part 2.en.srt (14.4 KB)
  • 005 Numpy Refresher.en.srt (12.6 KB)
  • 006 Pandas Refresher.mp4 (91.0 MB)
  • 001 Study Plan - Please do NOT skip this.en.srt (4.3 KB)
  • 003 Python Refresher - Part 1.mp4 (84.3 MB)
  • 001 Study Plan - Please do NOT skip this.mp4 (68.9 MB)
  • 004 Python Refresher - Part 2.mp4 (57.1 MB)
  • 005 Numpy Refresher.mp4 (52.3 MB)
03 Matplotlib Library for Plots
  • 024 Bar Plot.en.srt (23.1 KB)
  • 032 Arrow and Annotation on the Plot.en.srt (22.7 KB)
  • 022 Line Plot Part 1.en.srt (16.5 KB)
  • 033 Bar Plot and Pie Plot.en.srt (15.7 KB)
  • 024 Bar Plot.mp4 (102.8 MB)
  • 021 Download Section 3 Resources.html (1.0 KB)
  • 032 Arrow and Annotation on the Plot.mp4 (98.4 MB)
  • 031 Creating a Plot with Two Axes.en.srt (12.2 KB)
  • 025 Box Plot.en.srt (9.7 KB)
  • 027 Scatter Plot.en.srt (3.5 KB)
  • 029 Subplots approach - An Introduction.en.srt (8.7 KB)
  • 023 Line Plot Part 2.en.srt (8.5 KB)
  • 030 The First Plot Using Subplots Approach.en.srt (8.3 KB)
  • 026 Histogram.en.srt (7.2 KB)
  • 028 Pie Plot.en.srt (5.8 KB)
  • 022 Line Plot Part 1.mp4 (73.8 MB)
  • 033 Bar Plot and Pie Plot.mp4 (72.0 MB)
  • 025 Box Plot.mp4 (62.8 MB)
  • 029 Subplots approach - An Introduction.mp4 (59.4 MB)
  • 031 Creating a Plot with Two Axes.mp4 (59.0 MB)
  • 021 Section-3-Matplotlib-Library.ipynb (961.6 KB)
  • 023 Line Plot Part 2.mp4 (48.7 MB)
  • 030 The First Plot Using Subplots Approach.mp4 (40.5 MB)
  • 028 Pie Plot.mp4 (34.8 MB)
  • 026 Histogram.mp4 (31.9 MB)
  • 027 Scatter Plot.mp4 (26.8 MB)
04 Seaborn Library for Plots
  • 036 Seaborn Library for Plotting - Introduction.en.srt (17.3 KB)
  • 051 Setting the Plot Styles.mp4 (112.8 MB)
  • 040 Line Plot using the Seaborn Library.en.srt (15.1 KB)
  • 035 Scatter Plot and Histogram.en.srt (15.0 KB)
  • 053 Choosing an Appropriate Color Palette.en.srt (14.7 KB)
  • 042 Displot - Part 2 (Histogram, KDE, ECDF and Rug Plots).en.srt (14.6 KB)
  • 051 Setting the Plot Styles.en.srt (14.3 KB)
  • 046 Box Plot and Violin Plot.en.srt (13.6 KB)
  • 049 Pair Plot (Multiple Scatter + Histogram Plots).en.srt (13.4 KB)
  • 038 Scatter Plot using the Seaborn Library - Part 1.en.srt (13.2 KB)
  • 047 Bar Plot and Point Plot.en.srt (13.0 KB)
  • 037 Types of Plots in Seaborn.en.srt (12.9 KB)
  • 048 Joint Plot (Scatter + Histogram).en.srt (12.5 KB)
  • 034 Download Section 4 Resources.html (1.0 KB)
  • 045 Strip Plot and Swarm Plot.en.srt (10.7 KB)
  • 039 Scatter Plot using the Seaborn Library - Part 2.en.srt (10.0 KB)
  • 041 Displot - Part 1 (Histogram, KDE, ECDF and Rug Plots).en.srt (9.8 KB)
  • 054 Setting the Plot Themes.en.srt (9.6 KB)
  • 044 Catplot - Introduction.en.srt (4.6 KB)
  • 036 Seaborn Library for Plotting - Introduction.mp4 (87.0 MB)
  • 053 Choosing an Appropriate Color Palette.mp4 (84.6 MB)
  • 043 Two Dimensional Displots.en.srt (8.6 KB)
  • 050 Regression or Linear Model Plot.en.srt (8.5 KB)
  • 052 Setting the Plot Context.en.srt (5.2 KB)
  • 035 Scatter Plot and Histogram.mp4 (82.4 MB)
  • 040 Line Plot using the Seaborn Library.mp4 (81.8 MB)
  • 038 Scatter Plot using the Seaborn Library - Part 1.mp4 (72.9 MB)
  • 046 Box Plot and Violin Plot.mp4 (71.9 MB)
  • 049 Pair Plot (Multiple Scatter + Histogram Plots).mp4 (70.8 MB)
  • 054 Setting the Plot Themes.mp4 (69.9 MB)

  • Description


    Description

    This course will help you draw meaningful knowledge from the data you have.

    Three systems of data visualization in R are covered in this course:

    A. Pandas B. Matplotlib C. Seaborn

    A. Types of graphs covered in the course using the pandas package:

    Time-series: Line Plot

    Single Discrete Variable:Bar Plot, Pie Plot

    Single Continuous Variable: Histogram, Density or KDE Plot, Box-Whisker Plot

    Two Continuous Variable: Scatter Plot

    Two Variable: One Continuous, One Discrete: Box-Whisker Plot

    B. Types of graphs using Matplotlib library:

    Time-series: Line Plot

    Single Discrete Variable:Bar Plot, Pie Plot

    Single Continuous Variable: Histogram, Density or KDE Plot, Box-Whisker Plot

    Two Continuous Variable: Scatter Plot

    In addition, we will cover subplots as well, where multiple axes can be plotted on a single figure.

    C. Types of graphs using Seaborn library:

    In this we will cover three broad categories of plots:

    relplot (Relational Plots): Scatter Plot and Line Plot

    displot (Distribution Plots): Histogram, KDE, ECDF and Rug Plots

    catplot (Categorical Plots): Strip Plot, Swarm Plot, Box Plot, Violin Plot, Point Plot and Bar plot

    In addition to these three categories, we will cover these three special kinds of plots: Joint Plot, Pair Plot and Linear Model Plot

    In the end, we will discuss the customization of plots by creating themes based on the style, context, colour palette and font.
    Who this course is for:

    Data Science, Six Sigma and other professionals interested in data visualization
    Professionals interested in creating publication quality plots
    Professionals who are not happy with the plots created in MS Excel, and see them as dull and boring

    Requirements

    Some basic knowledge of Python is expected. However this course does include a quick overview of Python knowledge required for this course.

    Last Updated 3/2021



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Udemy - Mastering Data Visualization with Python

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3.9 GB
seeders:32
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Udemy - Mastering Data Visualization with Python

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