Master Cluster Analysis with Python and Pandas [2026]

Name: Master Cluster Analysis with Python and Pandas [2026]
Rating: 4.8 (296 reviews)

Master Cluster Analysis and Unsupervised Learning with Pandas and Python for Data Science and Machine Learning [2026]

Highest Rated

Created byHenrik Johansson

Last updated 7/2026

English

What you'll learn

Master Cluster Analysis and Unsupervised Learning both in theory and practice
Master simple and advanced Cluster Analysis models
Use K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Principal Component Analysis, and more…
Evaluate Cluster Analysis models using many different tools
Learn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated Simulations
Gain Understanding of concepts such as truth, predicted truth or model-based conditional truth
Use effective advanced graphical tools to judge models’ performance
Use the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and Python
Master Python 3 programming with Python’s native data structures, data transformers, functions, object orientation, and logic
Use and design advanced Python constructions and execute detailed Data Handling tasks with Python incl. File Handling
Use Python’s advanced object-oriented programming and make your own custom objects, functions and how to generalize functions
Manipulate data and use advanced multi-dimensional uneven data structures
Master the Pandas 2 and 3 library for Advanced Data Handling
Use the language and fundamental concepts of the Pandas library and handle all aspects of creating, modifying, and selecting Data from a Pandas DataFrame
Use file handling with Pandas and how to combine Pandas DataFrames with Pandas concat, join, and merge functions/methods
Perform advanced data preparation including advanced model-based imputation of missing data and the scaling and standardizing of data
Make advanced data descriptions and statistics with Pandas. Rank, sort, cross-tabulate, pivot, melt, transpose, and group data
[Extra Video] Make advanced Data Visualizations with Pandas, Matplotlib, and Seaborn
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources

Course content

4 sections • 67 lectures • 34h 58m total length

Introduction18:32
Setup of the Anaconda Cloud Notebook20:47
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address

Note: Anaconda often updates its resources and user interface plus utilizes anti-drone technology. This may cause minor deviations from graphics and procedures in the video
Download and installation of the Anaconda Distribution (optional)20:39
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional

Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
The Conda Package Management System (optional)46:52
This video describes the Conda Package Management System
Conda requires internet access
Video is optional

Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures

Overview34:51
This video provides an overview of "Master Python for data handling", teaches you some Python and Data Handling theory, and presents a table of contents for "Master Python for Data Handling" as well as some information about Jupyterlabs notebook, dynamic typing, Python programs organization, and some fundamental Python language syntax
Python Integers26:10
Learn to use Python Integers
Python Floats50:14
Learn to use Python Floats
Python Strings25:17
Learn to use Python Strings
Python String Methods37:37
Learn to use some Python string methods to test, search, transform, change, and manipulate string data
Python Strings and DateTime Objects1:06:33
Learn to use date and time data with Python's Datetime module. Learn to calculate time durations and time event data. Learn advanced knowledge about date and time data plus how computers and Python handle datetime data
Python Data Storage Overview4:51
This video provides an overview of the part of this section about Python's data storage abstractions, the set, tuple, dictionary, and the list
Python Set28:46
Learn to use Python's Set
Python Tuple30:04
Learn to use Python's Tuple and how to unpack Python Tuples
Python Dictionary46:00
Learn to use Python's Dictionary
Python List49:51
Learn to use Python's List
Data Transformers and Functions Overview4:30
This video provides an overview of the part of this section about Python's data data transformers and functions
Python While-loop45:03
Learn to use Python's while-loop with some practical examples
Python For-loop57:00
Learn to use Python's for-loops with some practical examples and advanced theory
Python Conditional Code Branching and Logic Operators59:00
Learn to use Python's Conditional Code Branching and Logic Operators. Learn about if statements, if-elif-else statements, match-case patterns, and Logic Operators including some Boolean logic and Python logic statements. Use your learned knowledge to edit and tailor basic descriptive statistics at a detailed level
Python Function Theory29:34
Learn the necessary Python Function Theory to create your own custom Python Functions! Learn some about Python's Lambda Functions and some advanced Python Function Theory
Python Functions: create your own functions1:15:51
Learn practical coding with Python's functions. You are introduced to functions and basic protections for functions. You will learn how to create functions from code-examples from earlier video lectures, and you will learn how to generalize functions up to advanced uneven-multitype-object 2-dimensional list of lists. You will learn about Lambda functions, advanced function arguments, and multiple function returns.
Learn to create your own functions!
Python File Path Theory [Extra Video]15:40
Learn some File Path theory and how Python handles File Paths
Python Object Oriented Programming: Some theory18:47
Learn Python OOP theory relevant for data handling tasks and how object-oriented data structures may affect data handling. Includes an overview of the OOP part of this section
Python Object Oriented Programming II: create your own custom objects39:20
Learn to code object-oriented programming with Python, and to handle Python object-oriented code and custom objects within the ambit of data handling
Python Object Oriented Programming III: Files and Tables27:17
Learn to save files in Python and the practical process of converting custom Python objects to tabular form and saving these into .csv, and Excel files and to load files to Pandas Data Frames
Python Object Oriented Programming IV: Recap and More58:21
This video lecture is a recap and extension of earlier video lectures. You will assemble knowledge from earlier lectures into more powerful knowledge. You will learn to construct a tabular data form with additional calculated variables and how to use the tabular data form for plotting, etc. You will learn how Data Handling fits with advanced object-oriented program structures

Master Pandas for Data Handling: Overview14:32
This video provides and introduction and overview of this section of the video course. "Master Pandas for Data Handling" is updated to current Pandas 2.2 and all known new changes in the future Pandas 3 version.
Pandas Theory and Terminology15:30
Learn the concepts and language of the Pandas DataFrame, the Pandas Series, and the data or object content of a DataFrame/Series object
Creating a Pandas DataFrame from scratch46:00
Learn to create Pandas DataFrame from scratch using Python and Pandas. You will learn how to create Pandas DataFrames using Python Dictionaries, Lists, and lots more.
Pandas File Handling: Overview5:16
This video contains an overview of the Pandas File Handing part of this section.
Pandas File Handling: The .csv file format23:30
Learn to load and save files from/to Pandas DataFrames from .csv files.
Pandas File Handling: Combine CSV files32:00
Learn to combine .csv files with Pandas and to handle and combine various common, uneven and non-uniform data structures into useful Pandas DataFrames
Pandas File Handling: The .xlsx file format23:20
Learn to load and save files from/to Pandas DataFrames from .xlsx files and hierarchical .xlsx files.
Pandas File Handling: SQL-database files and Pandas DataFrame15:08
Learn to load and save files from/to Pandas Dataframes from a SQL-database file.
Pandas Operations & Techniques: Overview5:34
This video contains an overview of the Pandas Operations and Techniques part of this section.
Pandas Operations & Techniques: Object Inspection19:34
Learn to inspect Pandas Dataframes and Dataframe content with Pandas .info() method, Python's .type() method, and more.
Pandas Operations & Techniques: DataFrame Inspection18:53
Learn to inspect the contents of large-sized Pandas DataFrames. Learn to use the .head, .tail, and other general methods to inspect the contents of a DataFrame.
Pandas Operations & Techniques: Column Selections21:04
Learn to select subsets of Columns from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame.
Pandas Operations & Techniques: Row Selections21:11
Learn to select subsets of Rows from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame.
Pandas Operations & Techniques: Conditional Selections21:27
Learn to make conditional selections of subsets from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame.
Pandas Operations & Techniques: Scalers and Standardization23:08
Learn about Scalers, Normalization, and Standardization. Learn to use mean-correction, normalization, and zero-one unity-based normalization.
Pandas Operations & Techniques: Concatenate DataFrames29:21
Learn to Concatenate Pandas DataFrames. Learn to use Pandas .concat() function to add DataFrames together horizontally and vertically. Learn to use the .concat() function with Inner and Outer joins.
Pandas Operations & Techniques: Joining DataFrames19:30
Learn to join Pandas DataFrames. Learn to use Pandas DataFrames .join() method. Learn to use "left joins", "right joins", "inner joins", "outer joins", and "cross joins".
Pandas Operations & Techniques: Merging DataFrames30:48
Learn to merge Pandas DataFrames. Learn to use Pandas DataFrames .merge() method. Learn to use "left joins", "right joins", "inner joins", and "outer joins" to merge different DataFrames on column variables.
Pandas Operations & Techniques: Transpose & Pivot Functions34:31
Learn to Transpose and Pivot Pandas DataFrames. Learn to use the transpose, pivot, pivot_table, and melt functions.
Pandas Data Preparation: Overview & workflow8:15
This video has an overview of the Data Preparation part of the course and includes a workflow for Data preparation or so-called data cleaning.
Pandas Data Preparation II: Edit DataFrame labels20:16
Learn to edit Pandas DataFrame column names, index, and index labels.
Pandas Data Preparation III: Duplicates22:23
Learn about Duplicates. Duplicate rows or observations may impact the quality of data products. Learn how to properly handle Duplicates with Pandas functionality.
Pandas Data Preparation IV: Missing Data & Imputation54:35
Learn to handle Missing data and Missing values with Pandas functionality. Learn Imputation and to augment Pandas with scikit-learn to use advanced model-based imputation of missing data
Pandas Data Preparation V: Data Binnings [Extra Video]46:32
Learn Data Binning with Pandas. Learn to use Administrative Data Binning, Algorithmic Data Binning, and subjective Data Binning. Learn to use Pandas .qcut() and .cut() functions.
Pandas Data Preparation VI: Indicator Features [Extra Video]33:01
Learn to create Indicator Features or Dummy Features with Pandas.
Pandas Data Description: Overview2:43
This video provides an overview of the part of this section about Pandas Data Description
Pandas Data Description II: Sorting and Ranking26:51
Learn to use Pandas functions for Sorting and Ranking data.
Pandas Data Description III: Descriptive Statistics31:40
Learn to create useful descriptive statistics with Pandas .agg() and .describe() functions. Learn to augment Pandas functions with the powerful .apply() and .value_counts() functions.
Pandas Data Description IV: Crosstabulations & Groupings30:06
Learn to create crosstabulations with Pandas .crosstab() function and to use the powerful Pandas .groupby() operation. Learn to augment these functions with a selection of Pandas functionality.
Pandas Data Visualization: Overview6:30
This video provides an overview of the part of this section about Pandas Data Visualization
Pandas Data Visualization II: Histograms42:34
Learn to make Histograms with Pandas, Matplotlib, and Seaborn. You will learn to make simple Histograms, advanced Histograms, multi-dimensional Histograms, and advanced Jointgrid Histograms.
Pandas Data Visualization III: Boxplots33:00
Learn to make traditional and modern Boxplots with Pandas, Matplotlib, and Seaborn. You will learn to make Boxplots, Boxenplots, Violinplots, Swarmplots and to create graphs consisting of many types of boxplots.
Pandas Data Visualization IV: Scatterplots40:00
Learn to make scatterplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple scatterplots, advanced scatterplots, advanced multi-scatterplots, and advanced pairplots consisting of scatterplots.
Pandas Data Visualization V: Pie Charts45:40
Learn to make Pie Charts with Pandas, Matplotlib, and support from Seaborn. You will learn to make Pie Charts, detailed Pie Charts, multiple Pie Charts, and how to properly use Pie Charts for effect.
Pandas Data Visualization VI: Line plots50:24
Learn to make Lineplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple Lineplots, advanced Lineplots, advanced Line-area plots, and advanced multidimensional Line-area plots.

Overview22:30
This video provides an overview of the Master Cluster Analysis and Unsupervised Learning section, and some theory on Cluster Analysis and Unsupervised Learning
K-Means Cluster Analysis47:15
Learn to use K-Means Cluster Analysis in a deep, practical and hands-on fashion. Learn to use practical and useful knee/elbow inertia plots and silhouette score plots. Use visualization tools to compare K-Means Cluster Analysis with subject matter expert classifications on a dataset
Auto-updated K-Means Cluster Analysis, introduction and simulation1:06:20
Extend your knowledge about K-means Cluster Analysis to Auto-updated / prototyped simulations. Learn some about the most important and defining tasks within machine learning and data science. Gain understanding about concepts such as truth, predicted truth, and model-based conditional truth.
Learn about data quality, model quality, practical data analysis, simulations and some new ways to study and graph Cluster Analysis models
Density-Based Spatial Clustering of Applications with Noise (DBSCAN)34:46
Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An exploratory analysis searching for data structures in the sized California Housing Dataset
Four Hierarchical Clustering algorithms21:18
Hierarchical Cluster Models. The Ward, Single, Average, and Complete linkage models. Dendrogram graphs for small-sized datasets. Exploratory analysis searching for structures in the California Housing Dataset
Principal Component Analysis (PCA)53:47
Learn to use Principal Component Analysis in a practical and hands-on fashion with some theory. Learn to use Principal Components as a technique for data transformations and dimensionality reduction
Learn to make Scree plots, heatmaps, and Indices plots plus learn to use these plots for component selections and dimensionality reduction. Learn to create uncorrelated Principal Component Loading to augment supervised learning models

Requirements

Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
Programming experience is not needed and you will be taught everything you need
The course only uses costless software
Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

Description

Welcome to the course Master Cluster Analysis and Unsupervised Learning with Pandas and Python!

Cluster Analysis and Unsupervised learning are one of the most important and defining tasks within machine learning and data science. Cluster Analysis and Unsupervised learning are one of the main methods for data scientists, analysts, A.I., and machine intelligences to create new insights, information or knowledge from data.

This course is a practical and exciting hands-on 3-in-1 master class video course about mastering Cluster Analysis and Unsupervised Learning with Advanced Data Handling using the Python 3 programming language combined with the powerful Pandas 2 + 3 library.

You will be taught to master some of the most useful and powerful Cluster Analysis and unsupervised learning techniques available and you will learn to master the Python programming language and the Pandas library for advanced Data Handling.

You will learn to:

Master Cluster Analysis and Unsupervised Learning both in theory and practice
Master simple and advanced Cluster Analysis models
Use K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Principal Component Analysis, and more…
Evaluate Cluster Analysis models using many different tools
Learn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated Simulations
Gain Understanding of concepts such as truth, predicted truth or model-based conditional truth
Use effective advanced graphical tools to judge models’ performance
Use the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and Python
Master Python 3 programming with Python’s native data structures, data transformers, functions, object orientation, and logic
Use and design advanced Python constructions and execute detailed Data Handling tasks with Python incl. File Handling
Use Python’s advanced object-oriented programming and make your own custom objects, functions and how to generalize functions
Manipulate data and use advanced multi-dimensional uneven data structures
Master the Pandas 2 and 3 library for Advanced Data Handling
Use the language and fundamental concepts of the Pandas library and handle all aspects of creating, changing, modifying, and selecting Data from a Pandas DataFrame object
Use file handling with Pandas and how to combine Pandas DataFrames with Pandas concat, join, and merge functions/methods
Perform advanced data preparation including advanced model-based imputation of missing data and the scaling and standardizing of data
Make advanced data descriptions and statistics with Pandas. Rank, sort, cross-tabulate, pivot, melt, transpose, and group data
[Extra Video] Make advanced Data Visualizations with Pandas, Matplotlib, and Seaborn
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work life.
And much more…

This course is an excellent way to learn to master Cluster Analysis, Unsupervised Learning, Python, Pandas and Advanced Data Handling!

Cluster Analysis and Unsupervised Learning are considered exploratory types of data analysis and are useful for discovering new information and knowledge. Unsupervised Learning and Cluster Analysis are often viewed as one of the few ways for artificial intelligences and machine intelligences to create new knowledge or data information without human assistance or supervision, so-called supervised learning.

Data Handling is the process of making data useful for analysis. Most Data Scientists and Machine Learning Engineers spends about 80% of their working efforts and time on Data Handling tasks. Mastering Data Handling with Python and Pandas is an extremely useful and time-saving skill that functions as a force multiplier for productivity.

This course provides you with the option to use Cloud Computing with the Anaconda Cloud Notebook and to learn to use Cloud Computing resources, or you may use any Python capable environment of your choice.

This course is designed for everyone who wants to

learn to Master Cluster Analysis and Unsupervised Learning
learn to Master Python 3 from scratch or the beginner level
learn to Master Python 3 and knows another programming language
reach the Master - intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning
learn to Master the Pandas library
learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career
learn advanced Data Handling and improve their capabilities and productivity

Requirements:

Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
Programming experience is not needed and you will be taught everything you need
The course only uses costless software
Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Cluster Analysis, Unsupervised Learning, Python, Pandas, and Data Handling.

Enroll now to receive 25+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!

Who this course is for:

Everyone who wants to Master Cluster Analysis and Unsupervised Learning
Everyone who wants to Master Python 3 from scratch or the beginner level
Everyone who wants to Master Python 3 and knows another programming language
Everyone who wants to reach the Master - intermediate Python programmer level as required by many advanced Udemy courses in Python, Data Science, or Machine Learning
Everyone who wants to Master the Pandas library
Everyone who wants to learn Data Handling skills that work as a force multiplier and that they will have use of in their entire career
Everyone who wants to learn advanced Data Handling and improve their capabilities and productivity

Master Cluster Analysis with Python and Pandas [2026]

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 1hr 47min

Master Python for Data Handling22 lectures • 13hr 51min

Master Pandas for Data Handling35 lectures • 15hr 15min

Master Cluster Analysis and Unsupervised Learning6 lectures • 4hr 6min

Requirements

Description

Who this course is for: