PyDataViz

Data visualization in
Python: from basics to advanced techniques

what how to turn data into understandable
graphs and find hidden patterns with Python!

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What will you what?

Draw basic types of graphs

histograms, box-plots, scatter-plots and other
visualizations to present data clearly.

Analyze data distributions

calculate mean, variance, standard deviation, and work with z-scores.

Apply descriptive statistics

calculate median, mode, IQR and visualize them using box plots.

Compare data multivariately

create pairwise plots, violin-plots, and other complex visualizations.

Automate visualizations

effectively use Matplotlib and Seaborn for quick analysis.

Prepare data for presentation

customize plot styles, captions, and legends for reports and dashboards.

Who is this course for?

Beginning data analysts who want to learn how to visualize data.

Students who need to visualize data.

Python programmers who want to delve deeper into analysis and visualization.

Course experts

Ekaterina Furman

Senior Data Scientist at Yandex

6+ years working with Python and Machine Learning

Specialization: computer vision and neural network architectures

Dmitry Krasnov

Lead ML Engineer at SberTech

7+ years of experience in NLP and deep learning

Specialization: Transformers and generative models

Anna Khazina

Data Science Team Lead at Mail.ru Group

5+ years of teaching ML and data analysis

Specialization: feature engineering and production ML

Mikhail Brezel

Principal Researcher at MIPT

PhD in Neural Networks

Specialization: reinforcement learning and time series

Course Program

Module 1: Fundamentals of Statistics

Brief introduction

What is a random variable

Nominal and ordinal data

Central tendency - introduction

Central tendency - examples

Data visualization

Quartile types, interquartile range

Examples

Standard deviation and variance

Sample standard deviation

Co Variance

Normal distribution

Chi squared distribution

Chi square Good fit

Relationship between categorical variables

Correlation

Module 2: Visualization of Iris dataset using Seaborn and Matplotlib

Introduction to EDA

Iris dataset

Scatter Plot

Two dimensional Scatter plot

Three dimensional scatter plot

Pair plots

One dimensional scatter plot

Histogram, PDF, CDF

Kde plots

Kde plot - Intuition

PDF and its properties

CDF - Code snippet

Mean, Median, Standard deviation, MAD - Code snippet

Box plots

Violin plot

Module 3: Visualization of Haeberman dataset

Haeberman Data - Introduction

Data Overview

Univariate Analysis

Bivariate Analysis

Module 4: Linear Algebra

Introduction to Linear Equations

Application of Linear Algebra

What is a scaler

What is a point and distance between 2 points

What is a vector

Row and Column Vector

Transpose of a Matrix

Unit Vector

Vector Addition and Subtraction

Inverse of a vector

Dot Product between two vectors

Multiplication of a vector with a scaler

Angle between 2 vectors - Part 1

Part 2

Orthogonal Vectors

Orthonormal vectors

Equation of a line - Part 1

Part 2

Part 3

Part 4

Projection of a point on a line

Distance of a point from a line

How to determine point on the negative and positive side of a line

Matrix Introduction

Matrix Operations

Symmetric, Square, Identity and Diagonal Matrix

Orthogonal Matrix

Minor, Cofactor and Determinant of a Matrix (Optional)

Inverse of a matrix (Optional)

Module 5: Principal Component Analysis

Preface for Dimensionality Reduction - Part 1

Part 2

Part 3

Part 4

Part 5

Gometric Intuition of PCA

Mathematical formulation of PCA - Part 1

Part 2

Part 3

Failure cases of PCA

Connecting Colab to Gdrive

Understanding MNIST dataset

Visualizing MNIST single digit

MNIST Visualization - Method 1

Method 2

How Training Works

Video lessons

Watch lectures at your own pace - anytime and from any device.

Practice

Reinforce your knowledge with real cases and assignments with verification and guidance from mentors.

Expert Support

Get feedback on solutions and work through tough issues with your tutors.

Community

Discuss materials, share your findings and find like-minded people in a shared chat room.

Choose the appropriate tariff

Introductory

Free

Training program - 1 module

Video lessons

No feedback

Open Access

Without certificate

Basic

$20

5 modules

Video lessons

Practical assignments

No feedback

Access to the course - 2 months

Certificate

Starter

$27

5 modules

Video lessons

Practical assignments

Feedback from mentors

Error analysis and recommendations

Access to the course - 6 months

Certificate

VIP

$38

Personalized mentor support

5 modules

Video lessons

Practical assignments

Feedback

Error analysis and recommendations

Access to the course - 12 months

Certificate

Corporate

$330

Groups of 5 to 10 people

5 modules

Video lessons

Practical tasks

Feedback from mentors

Error analysis and recommendations

Access to the course - 12 months

Certificate

What results can you expect at the end of your training?

87%

of course graduates reach their goal and find a job in the IT field.

38%

students get development orders already during the course

51%

reach the mid-level faster than a year after graduation

Choose the appropriate tariff

One of the most structured courses on data visualization! From simple charts in Matplotlib to complex dashboards in Seaborn - everything is explained with real examples. Now my reports look professional and my colleagues often ask for help with visualization. I recommend it!

I was looking for a course that would explain statistics without water - I found it here. I especially liked the practical assignments: I analyzed real datasets and immediately applied the knowledge. Plus - responsive support: all my questions were answered within a day. Now I can confidently work with distributions and box-plots!

I wanted to go deeper into data analysis, but I was afraid of complicated math. Everything is clearly presented here, from averages to PCA. It's cool that they added MNIST parsing - you can see how the methods work in real life. I have already pinned my certificate on LinkedIn - I got my first job offers!

I needed to learn how to present data visually to clients. This course gave me practical skills: how to choose the type of graph, sign the axes, make the visualization “talking”. Now even complex data is easy to understand. Time well spent!

As a person with no experience in Data Science, I appreciated the presentation of the material: each topic starts with a simple analogy, then code, then a task. The finals with EDA for Iris and Haberman are fire! I added these projects to my portfolio and got an internship in an IT company. Thank you!