Visualization for Data Science using Python.

Name: Visualization for Data Science using Python.
Rating: 5.0 (5 reviews)

Pandas, Matplotlib, Seaborn. Analyze Dozens of Datasets & Create Insightful Visualizations

Created byNewton Academy

Last updated 6/2025

English

What you'll learn

Visualizing data, including bar graphs, pie charts, histograms.
Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
Analyzing data, including mean, median, and mode, plus range and IQR and box plots
Univariate and Multivariate data visualization
Code based implementation of different plots like scatter plot, pair plots, box plots, violin plots
Matplotlib and seaborn visualization packages

Course content

5 sections • 79 lectures • 15h 20m total length

Quick Introduction25:18
What is a random variable13:13
Nominal and Ordinal Data23:51
Central tendency - Introduction24:26
Central tendency - Examples12:28
Data Visualization20:50
Types of Quartile, Inter Quartile Range10:16
Learn how percentile, quartiles, and the interquartile range describe data distribution, and compute median, min, max, and 0th/100th percentiles.
Types of Quartile, Inter Quartile Range - Example16:05
Explore how to compute quartiles and interquartile range, interpret 25th, 50th, and 75th percentiles, and read box plots to understand how skewness affects mean, median, and mode.
Standard Deviation & Variance17:35
Sample Standard Deviation22:36
Co Variance9:33
Explore covariance and correlation between two variables and their relation to variance, and learn to interpret positive, negative, or zero relationships via the covariance formula and correlation coefficient.
Normal Distribution23:40
Chi Square Distribution23:05
Chi Square Goodness of Fit21:10
Association between Categorical variables11:39
Correlation26:02

Introduction to EDA13:49
Iris Dataset8:33
Scatter Plot11:12
Explore 2d scatter plots with iris sepal length and width using matplotlib. Learn to create single and multi-plot figures with subplots, labels, and correct scatter syntax.
Two dimensional Scatter plot21:39
Three dimensional scatter plot4:11
Pair plots10:55
One dimensional scatter plot3:41
Histogram, PDF, CDF15:17
Kde plots6:07
Kde plot - Intuition6:32
PDF and its properties9:28
CDF - Code snippet1:29
Mean, Median, Standard deviation, MAD - Code snippet9:16
Box plots5:10
Violin plot2:41

Haeberman Data - Introduction6:47
Explore the Haberman survival dataset through exploratory data analysis using Python libraries like Matplotlib and Seaborn, learning data loading, feature interpretation (age, year of operation, nodes) and survival status.
Data Overview8:44
Univariate Analysis15:28
Bivariate Analysis8:44
Examine bivariate analysis with age and nodes to predict survival using scatter and pair plots; identify limitations of two-dimensional plots and the need for more complex models.

Introduction to Linear Equations13:36
Application of Linear Algebra7:41
What is a scaler9:38
What is a point and distance between 2 points15:40
What is a vector9:15
Understand vectors as magnitude and direction that represent points on the x–y plane, and learn to perform operations directly on expanded form x i + y j to simplify results.
Row and Column Vector21:28
Transpose of a Matrix2:35
Unit Vector9:48
Learn to compute vector magnitude with the L2 norm, or Euclidean distance, and obtain a unit vector by dividing by magnitude, with examples (1,1) -> (1/√2,1/√2) and (3,4) -> (3/5,4/5).
Vector Addition and Subtraction10:56
Inverse of a vector2:54
Dot Product between two vectors11:00
Multiplication of a vector with a scaler2:29
Angle between 2 vectors - Part 12:26
Angle between 2 vectors - Part 24:27
Orthogonal Vectors1:42
Explore orthogonal vectors, defined as perpendicular vectors with a 90-degree angle and a zero dot product, illustrated through examples of v1 and v2 in various placements.
Orthonormal vectors2:50
Equation of a line - Part 111:04
Equation of a line - Part 25:33
Equation of a line - Part 36:28
Explain that a line through the origin is perpendicular to its vector w, with w^T x = 0 and a point on the line like (1,1) for x−y=0.
Equation of a line - Part 415:57
Projection of a point on a line5:42
Distance of a point from a line24:04
How to determine point on the negative and positive side of a line13:28
Matrix Introduction5:30
Explore matrices as rectangular 2d arrays of numbers, distinguish vectors from matrices, and learn matrix size notation (m x n) and indexing like a11, a12.
Matrix Operations15:07
Symmetric, Square, Identity and Diagonal Matrix9:09
Orthogonal Matrix9:14
Minor, Cofactor and Determinant of a Matrix (Optional)12:04
Inverse of a matrix (Optional)15:22
Compute the inverse of a square matrix by determinant, cofactors, and adjoint, then divide the adjoint by the determinant; for orthogonal matrices, A^T equals A^{-1}.

Preface for Dimensionality Reduction - Part 113:18
Preface for Dimensionality Reduction - Part 211:43
Preface for Dimensionality Reduction - Part 312:27
Preface for Dimensionality Reduction - Part 417:56
Preface for Dimensionality Reduction - Part 59:48
Gometric Intuition of PCA9:51
Mathematical formulation of PCA - Part 116:58
Explore how PCA selects a unit direction mu to maximize the variance of standardized data projections, using x_i dot mu, with mu^T mu = 1.
Mathematical formulation of PCA - Part 27:22
Mathematical formulation of PCA - Part 324:34
Learn to formulate PCA as a constrained optimization, derive the covariance eigenproblem S mu = lambda mu, and project data onto top eigenvectors for visualization.
Failure cases of PCA4:01
Connecting Colab to Gdrive6:16
Understanding MNIST dataset12:41
Visualizing MNIST single digit5:27
MNIST Visualization - Method 117:14
MNIST Visualization - Method 22:41

Requirements

Basic understanding of python commands
Foundational Mathematics

Description

VISUALIZATION FOR DATA SCIENCE USING PYTHON IS SET UP TO MAKE LEARNING FUN AND EASY

This 60+ lesson course includes 15 hours of high-quality video and text explanations of everything under Statistics and Visualization. Topic is organized into the following sections:

Data Type - Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data types.
Visualizing data, including bar graphs, pie charts, histograms, and box plots
Analyzing data, including mean, median, and mode, IQR and box-and-whisker plots
Data distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scores
Chi Square distribution and Goodness of Fit
Scatter plots - One, Two and Three dimensional
Pair plots
Box plots
Violin plots
End to end Exploratory Data Analysis of Iris dataset
End to end Exploratory Data Analysis of Haberman dataset
Principle Component Analysis and MNIST dataset.

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

We will start with basics and understand the intuition behind each topic
Video lecture explaining the concept with many real life examples so that the concept is drilled in
Walkthrough of worked out examples to see different ways of asking question and solving them
Logically connected concepts which slowly builds up

Enroll today ! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.

YOU'LL ALSO GET:

Lifetime access to the course
Friendly support in the Q&A section
Udemy Certificate of Completion available for download
30-day money back guarantee

Who this course is for:

Anyone wanting to learn foundational visualization for Data Science
Aspirants for Data Analyst Role

Visualization for Data Science using Python.

What you'll learn

Explore related topics

Course content

Basics of Statistics16 lectures • 5hr 2min

Visualization of Iris Dataset using Seaborn and Matplotlib15 lectures • 2hr 10min

Visualization of Haberman dataset4 lectures • 40min

Linear Algebra29 lectures • 4hr 37min

Principal Component Analysis15 lectures • 2hr 52min

Requirements

Description

Who this course is for: