Data Visualization With Python And Project Implementation

Last updated 12/2020
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 11.06 GB | Duration: 22h 54m

Learn to use Python for Data Visualization. Practical project on applying Python for visualizing & predicting Covid-19.
What you’ll learn
How to use Python for Data Visualization
Full-fledged hands-on Project on Data Visualization with Python – "Visualizing Covid-19"
How to develop useful, intuitive and informative visualizations using Python programming
Introduction to Data Visualization – what it is, its importance & benefits
Top Python Libraries for Data Visualization
Introduction to Matplotlib, Install Matplotlib with pip
Basic Plotting with Matplotlib
NumPy and Pandas
Data Visualization tools – Bar chart, Histogram, Pie chart
More Data Visualization tools – Scatter Plot, Area Plot, Stacked Area Plot, Box Plot
Advanced data Visualization tools – Waffle Chart, Word Cloud, Heat map
Specialized data Visualization tools (I) – Bubble charts, Contour plots, Quiver Plot
Specialized data Visualization tools (II) – 3D Plotting in Matplotlib
3D Line Plot, 3 D Scatter Plot, 3D Contour Plot, 3D Wireframe Plot, 3D Surface Plot
Seaborn – Introduction to Seaborn, Seaborn functionalities, Installing Seaborn
Different categories of plot in Seaborn, Some basic plots using seaborn
Data Visualization using Seaborn – Strip Plot, Swarm Plot, Plotting Bivariate Distribution
Scatter plot, Hexbin plot, KDE, Regplot, Visualizing Pairwise Relationship, Box plot, Violin Plots, Point Plot
Requirements
Enthusiasm and determination to make your mark on the world!
Description
A warm welcome to the Data Visualization with Python and Project Implementation course by Uplatz.Data Visualizations allow humans to explore data in many different ways and see patterns and insights that would not be possible when looking at the raw form. Humans crave narrative and visualizations allow us to pull a story out of our stores of data. Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed.As datasets become bigger and more complex, only AI, materialized views, and more sophisticated coding languages will be able to glean insights from them. Advanced analytics is paving the way for the next wave of innovation. The human brain processes visual data better than any other kind of data, which is good because most of the information our brains process is visual. Visual processing and responses both occur more quickly compared to other stimuli.A good visualization could be the difference between hard to digest piles of data and useful business information. With increasing volume of data, it is next to impossible to rely on just one way frequency tables and statistics to understand the data. Good visualizations can accelerate the process of understanding data and gaining insights.Why Python for Data Visualization?Python offers multiple great graphing libraries that come packed with lots of different features. No matter if you want to create interactive, live or highly customized plots python has an excellent library for you.Python programming language has different types of libraries for all kind of projects. Likewise, python has various libraries for visualization of Data, so that user can understand the dataset in very detailed way and analyze it properly.Each library of visualization has its own specification. Using the particular libraries for specific task helps the user to complete the task in more easy and accurate way. Some liberates work better than the others.Python uses two exclusive libraries for data visualization.MatplotlibPython based plotting library offers matplotlib with a complete 2D support along with limited 3D graphic support. It is useful in producing publication quality figures in interactive environment across platforms. It can also be used for animations as well. Matplotlib is a library used for plotting graphs in the Python programming language. It is used plot 2 – dimensional arrays. Matplotlib is built on NumPy arrays. It is designed to work with the border SciPy stack. It was developed by John Hunter in 2002. The benefit of visualization is that user can have visual access to large amounts of the dataset. Matplotlib is a library which is consists of various plots such as histogram, bar, line, scatter, etc. Matplotlib comes with a huge variety of plots. Plots are helpful for understanding patterns, trends and for making correlations. It has instruments for reasoning about quantitative information. As matplotlib was the very first library of data visualization in python, many other libraries are developed on top of it or designed to work parallel to it for the analysis of the dataset.SeabornSeaborn is a library for creating informative and attractive statistical graphics in python. This library is built on top of the Matplotlib library. Seaborn offers various features such as built in themes, color palettes, functions and tools to visualize univariate, bivariate, linear regression, matrices of data, statistical time series etc. that allows us to build complex visualizations. Seaborn is a library of Python programming basically used for making statistical graphics of the dataset. It is also integrated closely with Pandas, which is used for the data structure of Datasets. Seaborn is very helpful to explore and understand data in a better way. It provides a high level of a crossing point for sketching attractive and informative algebraic graphics.Some of the other key Python libraries used in data visualization are:Pandas visualization – easy to use interface, built on MatplotlibGgplot – based on R’s ggplot2, uses Grammar of GraphicsPygalMissingnoPlotly – can create interactive plotsGleamLeatherGeoplotlibBokehFoliumUplatz offers this complete course on Data Visualization with Python. This Data Visualization in Python course will help you use Python’s most popular and robust data visualization libraries. Learn how to use Matplotlib, Seaborn, Bokeh, and others to create useful static and interactive visualizations of categorical, aggregated, and geospatial data.Data Visualization with Python – Course Syllabus1. Introduction to Data VisualizationWhat is data visualizationBenefits of data visualizationImportance of data visualizationTop Python Libraries for Data Visualization2. MatplotlibIntroduction to MatplotlibInstall Matplotlib with pipBasic Plotting with MatplotlibPlotting two or more lines on the same plot3. Numpy and PandasWhat is numpy?Why use numpy?Installation of numpyExample of numpyWhat is a panda?Key features of pandasPython Pandas – Environment SetupPandas – Data Structure with example4. Data Visualization toolsBar chartHistogramPie Chart5. More Data Visualization toolsScatter PlotArea PlotSTACKED Area PlotBox Plot6. Advanced data Visualization toolsWaffle ChartWord CloudHEAT MAP7. Specialized data Visualization tools (Part-I)Bubble chartsContour plotsQuiver Plot8. Specialized data Visualization tools (Part-II)Three-Dimensional Plotting in Matplotlib3D Line Plot3D Scatter Plot3D Contour Plot3D Wireframe Plot3D Surface Plot9. SeabornIntroduction to seabornSeaborn FunctionalitiesInstalling seabornDifferent categories of plot in SeabornSome basic plots using seaborn10. Data Visualization using SeabornStrip PlotSwarm PlotPlotting Bivariate DistributionScatter plot, Hexbin plot, KDE, RegplotVisualizing Pairwise RelationshipBox plot, Violin Plots, Point Plot11. Project on Data Visualization
Overview
Section 1: Introduction to Data Visualization
Lecture 1 Introduction to Data Visualisation
Section 2: Matplotlib
Lecture 2 Matplotlib – part 1
Lecture 3 Matplotlib – part 2
Section 3: NumPy and Pandas
Lecture 4 NumPy and Pandas – part 1
Lecture 5 NumPy and Pandas – part 2
Lecture 6 NumPy and Pandas – part 3
Section 4: Data Visualization Tools
Lecture 7 Data Visualisation Tools – part 1
Lecture 8 Data Visualisation Tools – part 2
Lecture 9 Data Visualisation Tools – part 3
Section 5: More Data Visualization Tools
Lecture 10 More Data Visualisation Tools – part 1
Lecture 11 More Data Visualisation Tools – part 2
Lecture 12 More Data Visualisation Tools – part 3
Section 6: Advanced Data Visualization Tools
Lecture 13 Advanced Data Visualisation Tools – part 1
Lecture 14 Advanced Data Visualisation Tools – part 2
Lecture 15 Advanced Data Visualisation Tools – part 3
Section 7: Specialized Data Visualization Tools (I)
Lecture 16 Specialized Data Visualisation Tools (I) – part 1
Lecture 17 Specialized Data Visualisation Tools (I) – part 2
Lecture 18 Specialized Data Visualisation Tools (I) – part 3
Section 8: Specialized Data Visualization Tools (II)
Lecture 19 Specialized Data Visualisation Tools (II) – part 1
Lecture 20 Specialized Data Visualisation Tools (II) – part 2
Lecture 21 Specialized Data Visualisation Tools (II) – part 3
Lecture 22 Specialized Data Visualisation Tools (II) – part 4
Section 9: Seaborn
Lecture 23 Seaborn – part 1
Lecture 24 Seaborn – part 2
Lecture 25 Seaborn – part 3
Section 10: Data Visualization using Seaborn
Lecture 26 Data Visualisation using Seaborn – part 1
Lecture 27 Data Visualisation using Seaborn – part 2
Lecture 28 Data Visualisation using Seaborn – part 3
Lecture 29 Data Visualisation using Seaborn – part 4
Lecture 30 Data Visualisation using Seaborn – part 5
Section 11: Project on Data Visualization with Python
Lecture 31 Project on Data Visualisation with Python – part 1
Lecture 32 Project on Data Visualisation with Python – part 2
Lecture 33 Project on Data Visualisation with Python – part 3
Lecture 34 Project on Data Visualisation with Python – part 4
Lecture 35 Project on Data Visualisation with Python – part 5
Lecture 36 Project on Data Visualisation with Python – part 6
Data Analysts & Consultants,Python Programmers & Developers,Business Analysts & Consultants,Anyone wishing to make a career in Business Intelligence, Visualization and Analytics,Data Visualization Managers,Data Engineers & Data Scientists,Data Visualization Developers,Data Architects,Data Visualization Leads,Newbies and beginners aspiring to become BI & Visualization professionals,Data Analysts – Python, Tableau, SQL,BI Solutions Manager,Reporting Analysts,Machine Learning Professionals

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