Data Visualization with Python: The Complete Guide
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Data Visualization with Python: The Complete Guide at Eduonix Overview
Duration | 19 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Credential | Certificate |
Data Visualization with Python: The Complete Guide at Eduonix Highlights
- Earn a Certificate on successful course completion from University of Michigan
- Lifetime Access. No Limits!
- A great course for learning Data Visualization
- Self paced Course
Data Visualization with Python: The Complete Guide at Eduonix Course details
- The course will start at the very beginning helping you understand the importance of Data Science, along with becoming familiar with Matplotlib, Python's very own visualization library. From there you will learn about the linear general statistics and data analysis. That's not all, we'll also cover important concepts such as data clustering, hypothesis gradient descent and advanced data visualizations. At the end of this course, you'll have a working knowledge of data visualization using Python and you'll also be able to build your own visualizations from scratch.
Data Visualization with Python: The Complete Guide at Eduonix Curriculum
Section 1 : Introduction to Course
Introduction
Overview of Course
Understanding Concepts of Data Science
Python as a Tool
Crash Course of Python
Sample Scripts with Loops in Python
Object Oriented Programming
Functional Tools
Section 2 : Data Visualization
Understanding Data Visualization
Matplotlib library
Bar Charts
Line Charts
Scatter Plots
A1. Activity for Data Visualization
Section 3 : Linear Algebra
What are Vectors. Various operations of vectors
Vectors
Understanding Matrices
Matrices
A2. Activity for Vectors Implementation
A3. Activity for Matrix Implementation
Section 4 : Statistics
A. Single Set of Data
Single set of data
Concepts of Central Tendencies
Central Tendencies
Dispersion
A4. Activity for implementation of statistics
Section 5 : Probability
Probability Concepts
The Normal Distribution
Central Limit Theorem
A5.Activity for understanding
Section 6 : Data Analysis
Understanding Data Analysis
Exploring One dimensional Data
Exploring Two dimensional data
Exploring many dimensions
Bubble charts representation
Data Munging
A6. Activity for understanding data analysis
Section 7 : Advanced Data Visualization
Visualizing the contecnt of a 2D array
Adding a colormap legend to th figure
Visualizing nonuniform 2D data
Visualizing a 2D scalar Field
Visualizing contour lines
Polar charts
Plotting log charts for research
Section 8 : Export Feature - Data Visualization
Generating a PNG picture
Generating PDF documents
Multiple graph plotting and export
Inserting sub figures
Section 9 : Hypothesis and Gradient Descent
Understanding Hypothesis
Implementation of hypothesis in Python
Gradient Descent
Implementation of Gradient Descent
A7. Activity for illustration of Gradient Descent
A7. Output for Gradient Descent Activity
Section 10 : Data Clustering
Data Clustering concepts
Developing a data cluster model
Illustration of data clustering
A8 Activity for understanding data clusters
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