Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Data Visualization with Python: The Complete Guide
Introduction to Course
Introduction (1:01)
Overview of Course (4:05)
Understanding Concepts of Data Science (6:58)
Python as a Tool (3:23)
Crash Course of Python (10:09)
Sample Scripts with Loops in Python (7:47)
Object Oriented Programming (6:31)
Functional Tools (4:15)
Data Visualization
Understanding Data Visualization (4:13)
Matplotlib library (8:04)
Bar Chart (10:00)
Line Charts (6:42)
Scatter Plots (6:00)
A1. Activity for Data Visualization (7:52)
Linear Algebra
What are Vectors. Various operations of vectors (4:11)
Vectors (7:57)
Understanding Matrices (5:31)
Matrices (9:38)
A2. Activity for Vectors Implementation (9:33)
A3. Activity for Matrix Implementation (7:18)
Statistics
A. Single Set of Data (2:32)
Single set of data (7:08)
Concepts of Central Tendencies (4:54)
Central Tendencies (7:46)
Dispersion (9:04)
A4. Activity for implementation of statistics (7:34)
Probability
Probability Concepts (3:07)
The Normal Distribution (9:09)
Central Limit Theorem (7:23)
A5.Activity for understanding (6:02)
Data Analysis
Understanding Data Analysis (3:43)
Exploring One dimensional Data (8:12)
Exploring Two dimensional data (12:49)
Exploring many dimensions (8:49)
Bubble charts representation (4:17)
Data Munging (8:13)
A6. Activity for understanding data analysis (7:15)
Advanced Data Visualization
Visualizing the contecnt of a 2D array (7:27)
Adding a colormap legend to th figure (3:38)
Visualizing nonuniform 2D data (7:48)
Visualizing a 2D scalar Field (4:46)
Visualizing contour lines (7:32)
Polar charts (5:56)
Plotting log charts for research (8:37)
Export Feature - Data Visualization
Generating a PNG picture (9:13)
Generating PDF documents (5:53)
Multiple graph plotting and export (7:56)
Inserting sub figures (4:35)
Hypothesis and Gradient Descent
Understanding Hypothesis (3:46)
Implementation of hypothesis in Python (13:22)
Gradient Descent (4:08)
Implementation of Gradient Descent (12:44)
A7. Activity for illustration of Gradient Descent (14:54)
A7. Output for Gradient Descent Activity (5:12)
Data Clustering
Data Clustering concepts (11:36)
Developing a data cluster model (10:22)
Illustration of data clustering (14:41)
A8 Activity for understanding data clusters (7:10)
Developing a data cluster model
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock