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116

Complete Data Science Training with Python for Data Analysis

Learn Statistics, Visualization, Machine Learning & More

By Minerva Singh | in Online Courses

In this easy-to-understand, hands-on course, you'll learn the most valuable Python Data Science basics and techniques. You'll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You'll even understand deep concepts like statistical modeling in Python's Statsmodels package and the difference between statistics and machine learning.

1,229 positive ratings from 6,976 students enrolled

  • Access 116 lectures & 12 hours of content 24/7
  • Get a full introduction to Python Data Science & Anaconda
  • Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, & Broadcasting
  • Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
  • Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
  • Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, & more
  • Discover how to create artificial neural networks & deep learning structures

"It is just what you need to learn when starting with data science" – Sagar Farkale

Minerva Singh | Bestselling Udemy Instructor & Data Scientist

4.2/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

60,288 Total Students
10,120 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Prior knowledge of Python will be useful but not required
  • Basic computer skills

Course Outline

  • Your First Program

  • Introduction to the Data Science in Python Bootcamp

    • What is Data Science? - 3:37
    • Introduction to the Course & Instructor - 11:34
    • Data and Scripts for the Course
    • Introduction to the Python Data Science Tool - 10:57
    • For Mac Users - 4:05
    • Introduction to the Python Data Science Environment - 19:15
    • Some Miscellaneous IPython Usage Facts - 5:25
    • Online iPython Interpreter - 3:26
    • Conclusion to Section 1 - 2:36
  • Introduction to Python Pre-Requisites for Data Science

    • Different Types of Data Used in Statistical & ML Analysis - 3:37
    • Different Types of Data Used Programatically - 3:46
    • Python Data Science Packages To Be Used - 3:16
    • Conclusion to Section 2 - 1:59
  • Introduction to Numpy

    • Numpy: Introduction - 3:46
    • Create Numpy Arrays - 10:51
    • Numpy Operations - 16:48
    • Matrix Arithmetic and Linear Systems - 7:34
    • Numpy for Basic Vector Arithmetic - 6:16
    • Numpy for Basic Matrix Arithmetic - 5:16
    • Broadcasting for Numpy - 3:52
    • Solve for Equations - 5:04
    • Numpy For Statistics - 7:23
    • Conclusions to Section 3 - 2:24
  • Introduction to Pandas

    • What are Pandas? - 12:06
    • Read CSV Data in Python - 5:42
    • Read in Excel File - 5:31
    • Read HTML Data - 12:06
    • Read JSON Data - 3:09
    • Conclusions to Section 4 - 2:06
  • Data Pre-Processing/Wrangling

    • Rationale behind this section - 4:19
    • Remove NA Values - 10:28
    • Basic Data Handling: Starting with Conditional Data Selection - 5:24
    • Drop Column/Row - 4:42
    • Subset and Index Data - 9:44
    • Basic Data Grouping Based on Qualitative Attributes - 9:47
    • Crosstabulation - 4:54
    • Reshaping - 9:26
    • Pivotting - 8:30
    • Rank and Sort Data - 8:03
    • Concatenate - 8:16
    • Merge - 10:47
    • Conclusion to Section 5
  • Introduction to Data Visualization

    • What is Data Visualisation? - 9:33
    • Theory Behind Data Visualisation - 6:46
    • Histograms- Visualise the Distribution of Quantitative Variables - 12:13
    • Boxplot- Visualise the Data Summary - 5:54
    • Scatterplot- Visualise The Relationship Between Quantitative Variables - 11:57
    • Line Chart - 12:31
    • Barplot - 22:25
    • Pie Chart - 5:29
    • Conclusion to Section 6 - 2:14
  • Basic Statistical Data Analysis

    • What is Statistical Data Analysis? - 10:08
    • Some Pointers on Collecting Data for Statistical Studies - 8:38
    • Explore the Quantitative Data: Descriptive Statistics - 9:05
    • Group By Qualitative Categories - 10:25
    • Visualize Descriptive Statistics-Boxplots - 5:28
    • Common Terms Relating to Descriptive Statistics - 5:15
    • Data Distribution- Normal Distribution - 4:07
    • Check for Normal Distribution - 6:23
    • Standard Normal Distribution and Z-scores - 4:10
    • Confidence Interval-Theory - 6:06
    • Confidence Interval-Calculation - 5:20
    • Conclusion to Section 7 - 1:28
  • Statistical Inference & Relationship Between Variables

    • What is Hypothesis Testing? - 5:42
    • Test the Difference Between Two Groups - 7:30
    • Test the Difference Between More Than Two Groups - 10:55
    • Explore the Relationship Between Two Quantitative Variables - 4:26
    • Correlation Analysis - 8:26
    • Linear Regression-Theory - 10:44
    • Linear Regression-Implementation in Python - 11:18
    • Conditions of Linear Regression-Check in Python - 12:03
    • Polynomial Regression - 3:53
    • GLM: Generalized Linear Model - 5:25
    • Logistic Regression - 11:10
    • Conclusion to Section 8 - 1:52
  • Machine Learning for Data Science

    • How is Machine Learning Different from Statistical Data Analysis? - 11:12
    • What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32
  • Unsupervised Learning

    • Some Basic Pointers - 1:38
    • kmeans-theory - 2:31
    • KMeans-implementation on the iris data - 8:01
    • Quantifying KMeans Clustering Performance - 3:53
    • kmeans clustering on real data - 4:16
    • How Do We Select the Number of Clusters? - 5:38
    • Theory of hierarchical clustering - 4:10
    • Implement hierarchical clustering - 9:19
    • Theory of Principal Component Analysis (PCA) - 2:37
    • Implement PCA - 3:52
    • Conclusion to Section 10 - 2:08
    • Data Preparation for Supervised Classification - 9:47
    • Classification accuracy evaluation - 9:42
    • Random Forest (RF) For Regression - 9:20
  • Supervised Learning

    • What is this section about? - 10:10
    • Logistic regression with classification - 8:26
    • Random Forest (RF) For Classification - 12:02
    • Linear Support Vector Machine (SVM) Classification - 3:10
    • Non-Linear Support Vector Machine (SVM) Classification - 2:06
    • Support Vector Regression - 4:30
    • kNN Classification - 7:46
    • kNN Regression - 3:48
    • Gradient Boosting Machine (GBM) Classification - 5:54
    • GBM Classification
    • Gradient Boosting Regression (GBR) - 4:46
    • Voting Classifier - 4:00
    • Conclusion to Section 11 - 2:46
  • Artificial Neural Networks (ANN) and Deep Learning

    • Introduction
    • Perceptrons for Binary Classification - 4:27
    • Getting Started with ANN-binary classification - 3:26
    • Multi-label classification with MLP - 4:53
    • Regression with MLP - 3:48
    • MLP with PCA on a Large Dataset - 7:33
    • Start With Deep Neural Network (DNN)
    • Start with H20 - 4:14
    • Default H2O Deep Learning Algorithm - 3:20
    • Specify the Activation Function - 2:06
    • Deep Learning Predictions - 5:02
    • Conclusion to section 12 - 2:03

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5.0 hours
Lessons
35

Master Clustering Analysis for Data Science Using Python

Learn All About Clustering Algorithms with Python Examples & Datasets

By Nouman Azam | in Online Courses

This course is for you if you want to gain a deeper understanding of the clustering algorithms without having to learn all the complicated maths. The approach in this course is very practical and will start everything from scratch. We will immediately start coding after a couple of introductory tutorials and minimal theories. All the coding will be done in Python which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups worldwide.

4.8/5 rating on Udemy: ★ ★ ★ ★

  • Access 35 lectures & 5 hours of content 24/7
  • Implement different clustering algorithms in python
  • Know when to use a specific algorithm
  • Handle issues of varying cluster sizes, densities, shapes & noise

"It is a very interesting course related to clustering. All the videos are fully explained with proper code implementation." – Harpreet Kaur

Nouman Azam | MATLAB Professor

4.4/5 Instructor Rating: ★ ★ ★ ★

Nouman Azam received his Ph.D. Degree in Computer Science from the University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from National University of Sciences and Technology, Pakistan, and Bachelor's in Computer Sciences from National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005, respectively

Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures, and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java, and Other academic tools used for teaching and instructing purposes.

18,843 Total Students
2,415 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: advanced

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction to the Course
    • Introduction - 4:22
  • KMeans Clustering
    • Code and Data
    • 1 - KMeans intuition - 12:18
    • 2 - Choosing the right number of clusters - 15:35
    • 3 - KMeans in Python (Part 1 - 18:35
    • 4 - KMeans in Python (Part 2) - 9:41
    • 5 - KMeans Limitations - (Part 1-Clusters with different sizes) - 10:30
    • 6 - KMeans Limitations - (Part-2-Clusters with non spherical shapes) - 10:37
    • 7 - KMeans Limitations - (Part 3-Clusters with varying densities) - 5:22
  • Mean Shift Clustering
    • Code and Data
    • 1 - Intuition of Mean Shift - 9:23
    • 2 - Mean Shift in Python - 9:23
    • 3 - Mean Shift Performance in Cases where Kmean Fails (Part 1) - 8:51
    • 4 - Mean Shift Performance in Cases where Kmean Fails (Part 2) - 11:34
  • DBSCAN Clustering
    • Code and Data
    • 1 - Intuition of DBSCAN - 9:21
    • 2 - DBSCAN in python - 12:47
    • 3 - DBSCAN on clusters with varying sizes - 6:29
    • 4 - DBSCAN on clusters with different shapes and densities - 11:27
    • 5 - DBSCAN for handling noise - 8:00
    • 6 - Practical Activity
  • Hierarchical Clustering
    • Code and Data
    • 1 - Hierarchical Clustering Intuition (Part 1) - 9:50
    • 2 - Hierarchical Clustering Intuition (Part 2) - 15:47
    • Hierachical Clustering in python - 11:27
  • HDBSCAN Clustering
    • Code and Data
    • 1 - HDBSCAN Intuition - 18:53
    • 2 - HDBSCAN in Python - 9:41
    • 3 - HDBSCAN clustering on different sizes, shapes and densities - 6:58
    • 4 - HDBSCAN for handling noise - 13:31
  • Application of Clustering
    • Code and Data
    • 1 - Image Compression (Part 1) - 11:37
    • 2 - Image Compression (Part 2) - 10:55
    • 3 - Clustering Sentences (Part 1) - 11:23
    • 4 - Clustering sentences (Part 2) - 8:48

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15.0 hours
Lessons
246

Python 3 Complete Masterclass: Make Your Job Tasks Easier!

Make the Right Choice When Starting to Learn Programming

By Mihai Catalin Teodosiu | in Online Courses

Do you want to become a Python Developer without having to spend a lot of money on books and boring theoretical courses? Do you often hear Python just about everywhere? Then, this is the course for you! This hands-on training takes you from "Hello World!" to advanced Python topics in just a few hours. You will learn every Python 3 concept and then put your knowledge into practice by answering quizzes, exercises, and doing the actual coding. This course is aimed at anyone having little or no experience in coding and a great desire to start learning Python from scratch.

1,395 positive ratings from 8,630 students enrolled!

  • Access 246 lectures & 15 hours of content 24/7
  • Master all the Python 3 key concepts from scratch
  • Gain real-life skills on Excel Automation, Data Analysis & Visualization, Web Scraping, and more
  • Be able to work with advanced Python tool

"As a Python beginner, I find this course is concise, easy to understand and structured. Also, Mihai responded to my question during the course promptly. I highly recommend this training." – Johnny Wang

Mihai Catalin Teodosiu | Python Enthusiast | Network / QA Automation Engineer

4.6/5 Instructor Rating ★ ★ ★ ★

Mihai Catalin Teodosiu holds a degree in Telecommunications and Information Technology from University Politehnica of Bucharest, Romania, as well as the CCNP, CCNA, CCDA, JNCIA, and ISTQB CTFL certifications. He has been working as a Network Quality Assurance Engineer since 2010, testing the OS for Nortel/Avaya L3 switches.

  • 5+ years experience in the Networking and Testing/Quality Assurance industries.
  • Certified professional: Cisco, Juniper & International Software Testing Qualifications Board certifications
  • Teaching courses on Udemy, GNS3 Academy & other e-learning platforms
  • Thousands of satisfied students, 4.97 / 5 average course rating
  • Thousands of followers on LinkedIn, Twitter, Facebook & Blogger

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Any device with basic specifications

Course Outline

  • Python 3 - Basics
    • Let's Connect!
    • How to Install Python 3 on Windows - 2:57
    • How to Install Python 3 on macOS - 2:35
    • UPDATE! Python Versions
    • Installing Python 3 on Windows, Linux and MacOS
    • The Python Interpreter & IDLE in Windows - 3:19
    • The Python Interpreter & IDLE in macOS - 2:45
    • Python 3 Basics - Scripts in Windows - 3:58
    • UPDATE! Saving a Python script in Notepad++
    • Python 3 Basics - Scripts in macOS - 4:21
    • Python 3 - Reasons for the 'No such file or directory' error (or similar) - 5:07
    • Python 3 Basics - User Input - 3:57
    • Notebook - User Input
    • Python 3 Basics - Variables - 6:19
    • Notebook - Variables
    • Python 3 Basics - Keywords
    • Python 3 - Data Types - 1:51
  • Python 3 - Strings
    • Why learn about each of Python's data types? - 7:38
    • Python 3 Strings - Introduction - 6:57
    • Python 3 Strings - Methods - 8:54
    • Python 3 Strings - Operators & Formatting - 7:23
    • Python 3 Strings - Formatting Using F-strings - 2:25
    • Python 3 Strings - Slices - 7:42
    • Python 3 Strings - Slicing Using a Step - 4:08
    • Notebook - Strings
  • Python 3 - Numbers and Booleans
    • Python 3 Numbers - Math Operators - 6:15
    • Notebook - Numbers and Math Operators
    • Python 3 Booleans - Logical Operators - 5:58
    • Notebook - Booleans and Logical Operators
  • Python 3 - Lists
    • Python 3 Lists - Introduction - 3:42
    • Python 3 Lists - Methods - 8:27
    • Python 3 Lists - Slices - 5:40
    • Notebook - Lists
  • Python 3 - Sets
    • Python 3 Sets - Introduction - 3:21
    • Python 3 Sets - Methods - 2:51
    • Python 3 Sets - Frozensets - 3:03
    • Notebook - Sets and Frozensets
  • Python 3 - Tuples
    • Python 3 Tuples - Introduction - 4:48
    • Python 3 Tuples - Tuples vs. Lists - 2:50
    • Python 3 Tuples - Methods - 3:25
    • Notebook - Tuples
  • Python 3 - Ranges
    • Python 3 Ranges - Introduction - 4:06
    • Python 3 Ranges - Methods - 2:40
    • Notebook - Ranges
  • Python 3 - Dictionaries
    • Python 3 Dictionaries - Introduction - 3:11
    • Python 3 Dictionaries - Methods - 6:25
    • Python 3 Dictionaries - Updates in v3.6 and v3.7 - 2:09
    • Python 3 - Conversions Between Data Types - 6:51
    • Notebook - Dictionaries and Conversions Between Data Types
  • Python 3 - Conditionals, Loops and Exceptions
    • Python 3 Conditionals - If / Elif / Else - 15:20
    • Notebook - If / Elif / Else Conditionals
    • Python 3 Loops - For / For-Else - 8:42
    • Notebook - For / For-Else Loops
    • Python 3 Loops - While / While-Else - 6:05
    • Notebook - While / While-Else Loops
    • Python 3 Nesting - If / For / While - 10:10
    • Notebook - Nesting
    • Python 3 - Break / Continue / Pass - 7:40
    • Notebook - Break / Continue / Pass
    • Python 3 - Exceptions - 2:27
    • Python 3 - Try / Except / Else / Finally - 9:42
    • Notebook - Try / Except / Else / Finally
  • Python 3 - Handling Errors and Exceptions in Python
    • Python 3 - Fixing Syntax Errors - 5:24
    • Python 3 - Fixing Exceptions - 8:45
  • Python 3 - Functions and Modules
    • Python 3 Functions - Basics - 9:51
    • Python 3 Functions - Arguments - 8:03
    • Notebook - Functions - Basics
    • Python 3 Functions - Namespaces - 10:48
    • Python 3 Modules - Importing - 11:30
    • Python 3 Modules - Helpful Functions: dir() and help() - 2:20
    • Notebook - Modules and Importing
    • Python 3 Modules - Installing a Non-Default Module in Windows - 3:54
    • Python 3 Modules - Installing a Non-Default Module in macOS
  • Python 3 - File Operations
    • Python 3 Files - Opening & Reading - 12:10
    • Python 3 Files - Quick Note for Windows Users - 2:48
    • Python 3 Files - Additional Way of Avoiding the Unicode Error - 1:38
    • Python 3 Files - Writing & Appending - 7:46
    • Python 3 Files - Closing. The "with" Method - 2:28
    • Python 3 Files - Deleting File Contents - 4:43
    • Python 3 Files - Access Modes Summary
    • Notebook - File Operations
  • Python 3 - Regular Expressions
    • Python 3 Regex - match() & search() - 16:24
    • Python 3 Regex - findall() & sub() - 6:16
    • Python 3 Regex - Regular Expressions Summary
    • Notebook - Regular Expressions
    • Bonus Video: Special Sequences - 6:01
    • Bonus Video: Sets of Characters - 5:07
    • Bonus Video: OR in Regular Expressions - 3:48
    • Bonus Video: split() & subn() - 3:33
    • Bonus Video: Additional Regex Syntax Elements - 4:45
    • Bonus Video: AttributeError: 'NoneType' object has no attribute - 3:34
  • Python 3 - Classes and Objects
    • Python 3 Classes - Objects - 11:45
    • Python 3 Classes - Inheritance - 6:19
    • Notebook - Classes and Objects
  • Python 3 - Other Advanced Concepts
    • Python 3 - List / Set / Dictionary Comprehensions - 4:53
    • Notebook - List / Set / Dictionary Comprehensions
    • Python 3 - Lambda Functions - 4:40
    • Notebook - Lambda Functions
    • Python 3 - map() and filter() - 2:29
    • Notebook - map() and filter()
    • Python 3 - Iterators and Generators - 6:48
    • Notebook - Iterators and Generators
    • Python 3 - Itertools - 5:43
    • Notebook - Itertools
    • Python 3 - Decorators - 2:37
    • Notebook - Decorators
    • Python 3 - Threading Basics - 5:36
    • Notebook - Threading Basics
    • Python 3 - Coding Best Practices - 2:36
  • Python 3 - Cheat Sheet
    • Download the Python 3 Cheat Sheet
  • Python 3 - E-Book
    • Download the Python 3 E-Book
  • APPLICATION: Build a Scientific Calculator with Python 3
    • Planning the Application - 3:01
    • Designing and Building the User Menu - 3:08
    • Implementing Addition, Subtraction, Multiplication, Division - 6:10
    • Implementing Modulo, Raising to a Power, Square Root, Logarithm - 3:43
    • Implementing Trigonometric Functions: sin, cos, tan - 3:37
    • Testing Each Function of the Application - 3:50
    • Download the Code - Interactive Scientific Calculator
    • Creating Executable Files (.exe) from Python Scripts (.py) - 3:59
  • Automate Excel Tasks with Python 3
    • Setting Up the Working Environment - 2:35
    • Loading an Excel Workbook In Python and Creating/Removing Sheets - 5:28
    • Notebook - Handling Workbooks
    • Getting General Information About a Sheet - 3:58
    • Notebook - Sheet Information
    • Working with Sheet Cells Using Python - 4:01
    • Notebook - Cell Information
    • Working with Cell Styles Using Python - 9:03
    • UPDATE! Change in cell.column in recent versions of openpyxl
    • Notebook - Cell Styles
    • Download the Excel-Python Cheat Sheet
    • APPLICATION - Migrating Records from a Text File to an Excel Workbook - 18:12
    • Download the Code - Excel Application
  • Automate Database Tasks with Python 3
    • Installing the Database Server Software - 2:45
    • UPDATE! Downloading and Installing PostgreSQL
    • Installing the Necessary Python Module - 1:32
    • Creating a New Database, Schema and User - 5:26
    • UPDATE! Change in database connection via PSQL
    • Notebook - Creating a New Database, Schema and User
    • Connecting Python to the Database - 2:45
    • Notebook - Connecting Python to the Database
    • Creating Database Tables with Python - 4:26
    • UPDATE! Handling the InFailedSqlTransaction exception
    • Notebook - Creating Database Tables with Python
    • Inserting Records Into a Table with Python - 3:18
    • Notebook - Inserting Records Into a Table with Python
    • Updating Records Into a Table with Python - 2:42
    • Notebook - Updating Records Into a Table with Python
    • Deleting Records From a Table with Python - 1:54
    • Notebook - Deleting Records From a Table with Python
    • Querying the Database with Python - 5:15
    • Notebook - Querying the Database with Python
    • Fetching Information From the Database with Python - 4:04
    • Notebook - Fetching Information From the Database with Python
    • Committing and Rolling Back Transactions with Python - 3:38
    • Notebook - Committing and Rolling Back Transactions
    • Download the PostgreSQL Syntax Cheat Sheet
    • Download the PostgreSQL-Python Cheat Sheet
    • APPLICATION - Migrating Records from a Text File to the Database - 9:14
    • Download the Code - Database Application
  • Automate Network Tasks with Python 3
    • Network Setup Overview - 1:27
    • Installing the Virtualization Software - 1:36
    • Installing the Virtualization Software on Windows, Linux, MacOS
    • Downloading & Installing the Network Device VM - 2:16
    • Note about Arista vEOS versions
    • Signing Up to the Arista Software Download Portal
    • Importing the VM & Tweaking the VM Settings - 3:08
    • UPDATE! vEOS First Boot and the ZeroTouch Feature
    • Connecting the Local PC to the Devices in Windows - 4:52
    • Connecting the Local PC to the Devices in macOS
    • Necessary Switch/Router Configuration
    • Checking the SSH Configuration and Testing the Connectivity - 3:03
    • UPDATE! Putty asking for Host Key / Password
    • Any Connection Issues? Check Out This Troubleshooting Checklist!
    • Planning the Application - 5:46
    • Checking IP File Validity - 4:09
    • Notebook - Checking IP File Validity
    • Checking IP Address Validity - 12:51
    • Notebook - Checking IP Address Validity
    • Checking IP Address Reachability - 3:57
    • Notebook - Checking IP Address Reachability
    • Note about pinging in Windows vs. Mac OS / Linux
    • Checking Username/Password File Validity - 1:45
    • Notebook - Checking Username/Password File Validity
    • Checking Command File Validity - 1:08
    • Notebook - Checking Command File Validity
    • Establishing the SSH Connection - 13:13
    • Notebook - Establishing the SSH Connection
    • Enabling Simultaneous SSH Connections - 2:12
    • Notebook - Enabling Simultaneous SSH Connections
    • Putting Everything Together - 2:56
    • Download the Code - Network Application and Modules
    • Reading Device Configuration - 9:19
    • Extracting Network Parameters - 12:13
    • Configuring Multiple Devices Simultaneously - 2:58
  • Automate Data Analysis Tasks with Python 3
    • Running Python Code - The Next Level: IPython and Jupyter Notebook - 9:08
    • Notebook - IPython and Jupyter Notebook
    • Introduction to Pandas - Basic Operations - 9:14
    • Notebook - Introduction to Pandas
    • Handling Files with Pandas - TXT, CSV, JSON, XLSX - 17:27
    • Notebook - Handling TXT, CSV, JSON, XLSX Files with Pandas
    • Reading HTML Content from URLs and HTML Files with Pandas - 4:52
    • Notebook - Reading HTML Content with Pandas
    • Indexing and Slicing Tables with Pandas - 21:45
    • Notebook - Indexing and Slicing Tables with Pandas
    • Adding, Updating, Deleting Table Rows and Columns - 14:22
    • Notebook - Adding, Updating, Deleting Table Rows and Columns
    • APPLICATION - Reading and Writing Data in PostgreSQL Databases Using Pandas - 18:18
    • UPDATE! Preparing for testing the application
    • Download the Code - SQL Data Analysis Application
  • Data Visualization with Bokeh and Python 3
    • Introduction to Bokeh - 3:38
    • Bookmark These 3 Important Documentation Links
    • Creating a Basic Line Plot Based on Python Lists - 11:17
    • UPDATE! BokehDeprecationWarning: 'legend' keyword is deprecated
    • Notebook - Creating a Basic Line Plot Based on Python Lists
    • Creating a Bar Plot Based on Excel Data - 17:42
    • Notebook - Creating a Bar Plot Based on Excel Data
    • Creating a Pie Chart Based on CSV Data - 11:28
    • UPDATE! Using 'legend_field' instead of 'legend'
    • Notebook - Creating a Pie Chart Based on CSV Data
    • Plotting Multiple Stock Prices Simultaneously - 9:04
    • Notebook - Plotting Multiple Stock Prices Simultaneously
    • Plotting Bitcoin Prices as an Interactive Plot with a Range Tool - 12:55
    • UPDATE! Code change according to a new website structure
    • Notebook - Plotting Bitcoin Prices as an Interactive Plot with a Range Tool
    • Plotting Bitcoin Prices as an Interactive Plot with Candlesticks - 9:49
    • Notebook - Plotting Bitcoin Prices as an Interactive Plot with Candlesticks
  • Automate Unit Testing with Python 3
    • Installing pytest and Writing Your First Test - 12:30
    • Notebook - Introduction to pytest
    • Running Multiple Tests. Test Discovery Rules in Action - 6:08
    • Notebook - Running Multiple Tests
    • Testing a Basic Script - Preparing the Test Bed - 9:33
    • Download the Code for Testing
    • Fixture Functions - 4:26
    • Notebook - Fixture Functions
    • Sharing a Fixture Instance & Fixture Finalization - 9:17
    • Notebook - Sharing a Fixture Instance & Fixture Finalization
    • Parametrizing Fixtures - 4:36
    • Notebook - Parametrizing Fixtures
    • Marking Test Functions Using Attributes - 6:22
    • Notebook - Marking Test Functions Using Attributes
    • Marking Test Functions Using Custom Markers - 4:23
    • Notebook - Marking Test Functions Using Custom Markers
  • Automate Web Scraping with Python 3
    • Installing the Necessary Modules - 1:47
    • Notebook - Installing the Necessary Modules
    • Extracting and Parsing Web Content - 4:34
    • Notebook - Extracting and Parsing Web Content
    • Tags, Names and Attributes - 10:07
    • Notebook - Tags, Names and Attributes
    • Searching the Tree of HTML Tags: find() and find_all() - 6:09
    • Notebook - Searching the Tree of HTML Tags: find() and find_all()
    • APPLICATION - Extracting the Product Names, Links and Prices. Saving to Excel - 12:44
    • Download the Code - Scraping Web Data and Saving to Excel
    • APPLICATION - Handling Website Pagination When Extracting Data - 6:24
    • Download the Code - Handling Website Pagination When Extracting Data
  • 10 Ways to Earn Money and Build a Portfolio with Your Python Skills
    • Putting Your Skills to Work - Part 1 - 8:46
    • Putting Your Skills to Work - Part 2 - 8:45
    • Download the Presentation
  • Final Section
    • Follow My Work and Join My LinkedIn Group

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10.0 hours
Lessons
200

Python 3 Network Programming: Build 5 Network Apps

Build Your Own Network Scripts & Upgrade Your Network Engineering Skills

By Mihai Catalin Teodosiu | in Online Courses

Python Network Programming course is aimed not only at network professionals but at anyone having little or no experience in coding or network automation and a great desire to start learning Python from scratch. This hands-on Python Network Programming training takes you from "Hello World!" to building complex network applications in no time. During this course, you will learn Python concepts that are relevant to your networking job and build some amazing network tools. This class simply enables you to save time and effort whilst acquiring these in-demand skills and upgrading your career.

3,718 positive ratings from 24,265 students enrolled!

  • Access 200 lectures & 10 hours of content 24/7
  • Master all the Python 3 key concepts starting from scratch
  • Use Python 3 for connecting via SSH to any network device & reading/writing configuration from multiple devices simultaneously
  • Build an interactive subnet calculator w/ a user menu
  • Get the full Python 3 code of 5 amazing network applications & customize each of them according to your networking needs

    "Good course to get the grasp on Python. I feel that I understand the Python structure much more than before and can find a way to get the work done." – Mirza Ali

Mihai Catalin Teodosiu | Python Enthusiast | Network / QA Automation Engineer

4.6/5 Instructor Rating ★ ★ ★ ★

Mihai Catalin Teodosiu holds a degree in Telecommunications and Information Technology from University Politehnica of Bucharest, Romania, as well as the CCNP, CCNA, CCDA, JNCIA, and ISTQB CTFL certifications. He has been working as a Network Quality Assurance Engineer since 2010, testing the OS for Nortel/Avaya L3 switches.

  • 5+ years experience in the Networking and Testing/Quality Assurance industries.
  • Certified professional: Cisco, Juniper & International Software Testing Qualifications Board certifications
  • Teaching courses on Udemy, GNS3 Academy & other e-learning platforms
  • Thousands of satisfied students, 4.97 / 5 average course rating
  • Thousands of followers on LinkedIn, Twitter, Facebook & Blogger

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Familiarity with networking concepts like: Switching, TCP/IP, CLI, SSHv2, Telnet, OSI Layers

Course Outline

  • Python 3 - Basics
    • Let's Connect!
    • How to Install Python 3 in Windows - 2:57
    • How to Install Python 3 in macOS - 2:35
    • UPDATE! Python Versions
    • Installing Python 3 on Windows, Linux and MacOS
    • The Python Interpreter & IDLE in Windows - 3:19
    • The Python Interpreter & IDLE in macOS - 2:35
    • Python 3 Basics - Scripts in Windows - 3:58
    • UPDATE! Saving a Python script in Notepad++
    • Python 3 Basics - Scripts in macOS - 4:21
    • Python 3 - Reasons for the 'No such file or directory' error (or similar) - 5:07
    • Python 3 Basics - User Input - 3:57
    • Notebook - User Input
    • Python 3 Basics - Variables - 6:19
    • Notebook - Variables
    • Python 3 Basics - Keywords
    • Python 3 - Data Types - 1:51
  • Python 3 - Strings
    • Why learn all the Python topics below before building the network apps? - 3:04
    • Python 3 Strings - Introduction - 6:57
    • Python 3 Strings - Methods - 8:54
    • Python 3 Strings - Operators & Formatting - 7:23
    • Python 3 Strings - Formatting Using F-strings - 2:25
    • Python 3 Strings - Slices - 7:42
    • Python 3 Strings - Slicing Using a Step - 4:08
    • Notebook - Strings
  • Python 3 - Numbers and Booleans
    • Python 3 Numbers - Math Operators - 6:15
    • Notebook - Numbers and Math Operators
    • Python 3 Booleans - Logical Operators - 5:58
    • Notebook - Booleans and Logical Operators
  • Python 3 - Lists
    • Python 3 Lists - Introduction - 3:42
    • Python 3 Lists - Methods - 8:27
    • Python 3 Lists - Slices - 5:40
    • Notebook - Lists
  • Python 3 - Sets
    • Python 3 Sets - Introduction - 3:21
    • Python 3 Sets - Methods - 2:51
    • Python 3 Sets - Frozensets - 3:03
    • Notebook - Sets and Frozensets
  • Python 3 - Tuples
    • Python 3 Tuples - Introduction - 4:48
    • Python 3 Tuples - Tuples vs. Lists - 2:50
    • Python 3 Tuples - Methods - 3:25
    • Notebook - Tuples
  • Python 3 - Ranges
    • Python 3 Ranges - Introduction - 4:06
    • Python 3 Ranges - Methods - 2:40
    • Notebook - Ranges
  • Python 3 - Dictionaries
    • Python 3 Dictionaries - Introduction - 3:11
    • Python 3 Dictionaries - Methods - 6:25
    • Python 3 - Conversions Between Data Types - 6:51
    • Notebook - Dictionaries and Conversions Between Data Types
  • Python 3 - Conditionals, Loops and Exceptions
    • Python 3 Conditionals - If / Elif / Else - 15:20
    • Notebook - If / Elif / Else Conditionals
    • Python 3 Loops - For / For-Else - 8:42
    • Notebook - For / For-Else Loops
    • Python 3 Loops - While / While-Else - 6:05
    • Notebook - While / While-Else Loops
    • Python 3 Nesting - If / For / While - 10:10
    • Notebook - Nesting
    • Python 3 - Break / Continue / Pass - 7:40
    • Notebook - Break / Continue / Pass
    • Python 3 - Exceptions - 2:27
    • Python 3 - Try / Except / Else / Finally - 9:42
    • Notebook - Try / Except / Else / Finally
  • Python 3 - Functions and Modules
    • Python 3 Functions - Basics - 9:51
    • Python 3 Functions - Arguments - 8:03
    • Notebook - Functions - Basics
    • Python 3 Functions - Namespaces - 10:48
    • Python 3 Modules - Importing - 11:30
    • Python 3 Modules - Helpful Functions: dir() and help() - 2:20
    • Notebook - Modules and Importing
    • Python 3 Modules - Installing a Non-Default Module in Windows - 3:54
    • Python 3 Modules - Installing a Non-Default Module in macOS
  • Python 3 - File Operations
    • Python 3 Files - Opening & Reading - 12:10
    • Python 3 Files - Quick Note for Windows Users - 2:48
    • Python 3 Files - Additional Way of Avoiding the Unicode Error - 1:38
    • Python 3 Files - Writing & Appending - 7:46
    • Python 3 Files - Closing. The "with" Method - 2:28
    • Python 3 Files - Access Modes Summary
    • Notebook - File Operations
  • Python 3 - Regular Expressions
    • Python 3 Regex - match() & search() - 16:24
    • Python 3 Regex - findall() & sub() - 6:16
    • Python 3 Regex - Regular Expressions Summary
    • Notebook - Regular Expressions
    • Bonus Video: Special Sequences - 6:01
    • Bonus Video: Sets of Characters - 5:07
    • Bonus Video: OR in Regular Expressions - 3:48
    • Bonus Video: split() & subn() - 3:33
    • Bonus Video: Additional Regex Syntax Elements - 4:45
    • Bonus Video: AttributeError: 'NoneType' object has no attribute - 3:34
  • Python 3 - Classes and Objects
    • Python 3 Classes - Objects - 11:45
    • Python 3 Classes - Inheritance - 6:19
    • Notebook - Classes and Objects
  • Python 3 - Other Advanced Concepts
    • Python 3 - List / Set / Dictionary Comprehensions - 4:53
    • Notebook - List / Set / Dictionary Comprehensions
    • Python 3 - Lambda Functions - 4:40
    • Notebook - Lambda Functions
    • Python 3 - map() and filter() - 2:29
    • Notebook - map() and filter()
    • Python 3 - Iterators and Generators - 6:48
    • Notebook - Iterators and Generators
    • Python 3 - Itertools - 5:43
    • Notebook - Itertools
    • Python 3 - Decorators - 2:37
    • Notebook - Decorators
    • Python 3 - Threading Basics - 5:36
    • Notebook - Threading Basics
    • Python 3 - Coding Best Practices - 2:36
  • Python 3 - Download the Cheat Sheet
    • Download the Python 3 Cheat Sheet
  • Python 3 - Download the E-Book
    • Download the Python 3 E-Book
  • Setting Up the Working Environment
    • Network Setup Overview - 1:27
    • Installing the Virtualization Software - 1:36
    • Installing the Virtualization Software on Windows, Linux, MacOS
    • Downloading & Installing the Network Device VM - 2:16
    • Note about Arista vEOS versions
    • Signing Up to the Arista Software Download Portal
    • Importing the VM & Tweaking the VM Settings - 3:08
    • UPDATE! vEOS First Boot and the ZeroTouch Feature
    • Connecting the Local PC to the Devices in Windows - 4:52
    • Connecting the Local PC to the Devices in macOS
    • Necessary Switch/Router Configuration
    • Checking the SSH Configuration and Testing the Connectivity - 3:03
    • UPDATE! Putty asking for Host Key / Password
    • Any Connection Issues? Check Out This Troubleshooting Checklist!
  • Network Application #1 - Reading / Writing Device Configuration via SSH
    • Planning the Application - 5:46
    • Checking IP File Validity - 4:09
    • Logical Flow Diagram
    • Notebook - Checking IP File Validity
    • Checking IP Address Validity - 12:51
    • Notebook - Checking IP Address Validity
    • Checking IP Address Reachability - 3:57
    • Notebook - Checking IP Address Reachability
    • Note about pinging in Windows vs. Mac OS / Linux
    • Checking Username/Password File Validity - 1:45
    • Notebook - Checking Username/Password File Validity
    • Checking Command File Validity - 1:08
    • Notebook - Checking Command File Validity
    • Establishing the SSH Connection - 13:13
    • Notebook - Establishing the SSH Connection
    • Enabling Simultaneous SSH Connections - 2:12
    • Notebook - Enabling Simultaneous SSH Connections
    • Putting Everything Together - 2:56
    • Download the Code - Network Application and Modules
    • Reading Device Configuration - 9:19
    • Extracting Network Parameters - 12:13
    • Configuring Multiple Devices Simultaneously - 2:58
  • Network Application #2 - Building an Interactive Subnet Calculator
    • What Are We Going to Build? - 2:44
    • Planning the Application - 2:11
    • Logical Flow Diagram
    • Checking IP Address and Subnet Mask Validity - 5:08
    • Notebook - Checking IP Address and Subnet Mask Validity
    • Converting to Binary. Calculate Hosts per Subnet. Wildcard Masks - 12:34
    • Notebook - Converting to Binary. Calculate Hosts per Subnet. Wildcard Masks
    • Converting to Binary. Find the Network and Broadcast Addresses - 10:50
    • Notebook - Converting to Binary. Find the Network and Broadcast Addresses
    • Random IP Address Generation Algorithm - 8:23
    • Notebook - Random IP Address Generation Algorithm
    • Testing the Application - 4:11
    • Download the Full Application Code
  • Network Application #3 - Extracting Network Parameters & Building Graphs
    • Planning the Application - 2:12
    • Logical Flow Diagram
    • Connecting to the Network Device via SSH - 6:17
    • Notebook - Configuring the Arista Switch for SSH Connectivity
    • Extracting the CPU Utilization Value and Saving It to a Text File - 7:51
    • Notebook - Extracting the CPU Utilization Value and Saving It to a Text File
    • Polling the Switch Every 10 Seconds - 2:51
    • Notebook - Polling the Switch Every 10 Seconds
    • Matplotlib - Building the Switch CPU Utilization Graph - 5:57
    • Notebook - Matplotlib - Building the Switch CPU Utilization Graph
    • Testing the Application - 4:43
    • Download the Full Code - Network Application, Graph Building Script and Modules
  • Network Application #4 - Building a Basic Network Packet Sniffer
    • Planning the Application - 3:13
    • Logical Flow Diagram
    • Setting Up a Linux VM in VirtualBox - 4:46
    • Notebook - Downloading the Linux VM
    • Configuring the Linux VM - 9:42
    • Notebook - Configuring the Linux VM - Make Sure You Follow These Steps
    • Meeting Scapy - 10:08
    • Notebook - Meeting Scapy
    • Importing the Necessary Modules - 3:26
    • Notebook - Importing the Necessary Modules
    • Asking the User for Input: Interface, Number of Packets, Interval, Protocol - 6:32
    • Notebook - Asking the User for Input
    • Extracting Parameters from Packets and Writing to a Log File - 4:49
    • Notebook - Extracting Parameters from Packets and Writing to a Log File
    • Testing the Application - Running the Sniffer and Filtering Packets by Protocol - 7:20
    • Notebook - Running the Sniffer
    • Download the Full Application Code
  • Network Application #5 - Config File Management and E-mail Notifications
    • Planning the Application - 3:22
    • Logical Flow Diagram
    • Importing the Modules and Defining the Necessary Parameters - 3:48
    • Notebook - Installing Necessary Modules and Official Documentation
    • Notebook - Importing the Modules and Defining the Necessary Parameters
    • Connecting to the Arista Switch via SSH using Netmiko - 1:51
    • Notebook - Connecting to the Arista Switch via SSH Using Netmiko
    • Handling the Configuration Files and Extracting the Configuration Changes - 6:03
    • Notebook - Extracting the Configuration Changes
    • Sending E-mails to the Network Admin with Device Configuration Changes - 3:43
    • Notebook - Sending E-mails to the Network Admin
    • Creating a Schedule for Sending E-mails on a Daily Basis in Linux - 5:15
    • Notebook - Creating a Schedule for Sending E-mails
    • Testing the Application - 5:15
    • Download the Full Application Code
  • [BONUS] Running Python Code via Remote Servers
    • Running CLI Commands via a Remote Server - 7:20
    • Running a Local Python Script via a Remote Server - 2:37
    • Running a Remote Python Script via a Remote Server - 2:25
    • Notebook - Running Python Code via Remote Servers
  • Final Section
    • Follow My Work and Join My LinkedIn Group

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Lifetime
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3.0 hours
Lessons
62

Python 3 Network Programming – Sequel: Build 5 More Apps

Following Up on the Famous Python 3 Network Programming Course, You Will Build 5 More Network Apps from Scratch

By Mihai Catalin Teodosiu | in Online Courses

Equipped with working files, cheat sheets, and Python code samples, you will be able to work alongside me on each lecture and each application. Full code for each application is provided so you can save time and start coding and testing on the spot. This Python Network Programming course is aimed exclusively at network professionals, engineers, and/or admins.

4.9/5 rating from 381 students enrolled: ★ ★ ★ ★

  • Access 62 lectures & 3 hours of content 24/7
  • Get the full Python 3 code of all 5 network applications
  • Customize each code according to your networking needs
  • Put the networks automation skills you've gained from this course

"This course is the best ! It's simple and covers a lot of topics. Thank you for making this course available for all the network geeks out there." – Ashwin

Mihai Catalin Teodosiu | Python Enthusiast | Network / QA Automation Engineer

4.6/5 Instructor Rating ★ ★ ★ ★

Mihai Catalin Teodosiu holds a degree in Telecommunications and Information Technology from University Politehnica of Bucharest, Romania, as well as the CCNP, CCNA, CCDA, JNCIA, and ISTQB CTFL certifications. He has been working as a Network Quality Assurance Engineer since 2010, testing the OS for Nortel/Avaya L3 switches.

  • 5+ years experience in the Networking and Testing/Quality Assurance industries.
  • Certified professional: Cisco, Juniper & International Software Testing Qualifications Board certifications
  • Teaching courses on Udemy, GNS3 Academy & other e-learning platforms
  • Thousands of satisfied students, 4.97 / 5 average course rating
  • Thousands of followers on LinkedIn, Twitter, Facebook & Blogger

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Prior Python 3 knowledge
  • Familiarity with networking concepts such as: TCP/IP, CLI, SSH, SNMP, OSI, Switching, Routing

Access
Lifetime
Content
11.0 hours
Lessons
106

Learning Python 3 Programming for the Absolute Beginner

Get a Full Working Knowledge of the Python 3 Programming Language

By Lee Assam | in Online Courses

Learn the Python 3 Programming Language as quickly and efficiently as possible with hands-on practice challenges and solutions. This course is catered to beginners who want to learn the Python 3 Programming Language or developers who already know another language and want to learn Python 3. All major concepts are taught and the course contains challenge questions with fully explained solutions to cement all the concepts you will learn.

New course with 4.8/5 rating on Udemy: ★ ★ ★ ★

  • Access 106 lectures & 11 hours of content 24/7
  • Learn Python from an experienced professional software developer
  • Do hands-on practice exercises w/ fully explained solutions for all topics taught
  • Become comfortable using Python to solve problems

"Great course! Excellent step by step walkthroughs describing different capabilities of Python. Did a great job describing foundational topics including data structures and object-oriented programming." – Kelby Lee

Lee Assam | Electrical and Software Engineer | University Instructor

4.4/5 Instructor Rating: ★ ★ ★ ★

Lee Assam holds a Bachelor's Degree in Electrical and Computer Engineering and a Master's Degree in Computer Science. His passion for innovation has resulted in several wins in Hack Day competitions. He is a US Patent holder and has numerous US Patent Applications currently being reviewed. His hobby is Arduino and the Internet of Things. He has been playing around with the Arduino and the Raspberry Pi platforms since their inception, and he uses his Electrical Engineering background coupled with software development skills to create and develop exciting projects.

25,747 Total Students
2,262 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required:

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction
    • Welcome - 4:57
    • Resources and Materials - 5:56
  • Software Setup
    • Installing Python and PyCharm - 4:33
  • Write your first Python Program
    • Create a Hello World App - 7:17
  • Background of the Python Programming Language
    • History and Characteristics of Python - 9:57
  • Variables and Comments
    • Foundation : Variables - 3:06
    • Variables - 8:42
    • Comments - 3:11
  • Print Statements
    • Using the print() function in Python - 12:11
    • Challenge : Print( ) statements - 2:36
    • Solution : Print( ) statements - 4:52
  • Working with Common Data Types
    • Foundation : Introduction to Data Types - 1:45
    • Data Types - 10:19
    • Casting and Conversion - 6:20
    • Foundation : Working with Strings - 1:48
    • Working with Strings - Part 1 - 13:30
    • Working with Strings - Part 2 - 12:56
    • Challenge : Strings - 1:47
    • Solution : Strings - 8:45
  • Operators
    • Foundation : Operators - 6:47
    • Working with Operators - 8:34
    • Challenge : Operators - 0:48
    • Solution : Operators - 1:59
  • Working with Data Structures
    • Foundation : Introduction to Python Data Structures - 6:10
    • Foundation : Lists - 2:16
    • Working with Lists - Part 1 - 10:38
    • Working with Lists - Part 2 - 12:34
    • Challenge : Lists - 1:17
    • Solution : Lists - 7:02
    • Foundation : Sets - 2:32
    • Working with Sets - Part 1 - 7:35
    • Working with Sets - Part 2 - 7:52
    • Challenge : Sets - 1:23
    • Solution : Sets - 4:18
    • Foundation : Tuples - 1:47
    • Working with Tuples - 12:47
    • Challenge : Tuples - 0:53
    • Solution : Tuples - 3:47
    • Foundation : Dictionaries - 2:31
    • Working with Dictionaries - Part 1 - 8:52
    • Working with Dictionaries - Part 2 - 5:38
    • Challenge : Dictionaries - 0:53
    • Solution : Dictionaries - 4:43
  • Conditionals and Looping
    • Foundation : Conditionals and Looping - 7:16
    • If statements - Part 1 - 10:53
    • If statements - Part 2 - 9:30
    • While statements - 11:12
    • For statements - 8:38
    • Challenge : Conditionals and Looping - 1:24
    • Solution 1 : Conditionals and Looping - 3:46
    • Solution 2 : Conditionals and Looping - 4:27
  • Working with Functions
    • Foundation : Functions - 3:52
    • Functions - Part 1 - 9:10
    • Functions - Part 2 - 8:52
    • Functions - Parameters passing by reference - 7:36
    • Recursive Functions - 7:21
    • Challenge : Functions - 1:27
    • Solution : Functions - 8:47
  • Object-Oriented Programming - Working with Classes
    • Foundation : Object Oriented Programming in Python - 5:48
    • Foundation : Inheritance - 5:53
    • Foundation : Encapsulation - 4:28
    • Foundation : Abstraction - 2:10
    • Foundation : Polymorphism - 3:34
    • Defining classes, constructors and methods - 9:07
    • Inheritance and Private / Public properties - 11:23
    • Private Attributes or Properties - 7:22
    • intro-challenge-object-oriented - 2:02
    • Solution : Temperature Class - Object-Oriented Programming - 8:51
    • Solution : Circle Class - Object-Oriented Programming - 11:56
  • Exception and Error Handling
    • Foundation : Exception Handling in Python with try and except - 6:53
    • Try Except Else statements - 8:12
    • Finally statements and assertions - 12:15
    • Challenge : Exceptions - 0:33
    • Solution : Exceptions - 5:45
  • Modules
    • Foundation : Modules - 2:35
    • Creating a Module - 9:29
    • Importing a Module - 10:24
    • Using if name for checking if your program is executing - 8:13
    • Challenge : Modules - 2:47
    • Solution : Modules - Create the Module - 7:57
    • Solution : Modules - Create the Main Program - 5:10
  • Input and Output
    • Foundation : Getting Input from the User - 1:35
    • Using the sys module - 6:49
    • Using the argparse library - 9:22
    • Foundation : Working with Files - 5:04
    • Working with Files - Part 1- Creating, Writing to and Reading from Files - 9:59
    • Working with Files - Part 2 - Exploring other ways of reading data from a file - 4:26
    • Working with Files - Part 3 - File Seek and Editing Files - 9:56
    • Foundation : Working with File and Directory Commands - 1:18
    • Working with Files and Directories - 8:22
  • Working with Data Files
    • Foundation : Working Data Files (CSV and JSON) - 4:00
    • Reading CSV Files - Part 1 - 12:34
    • Reading CSV Files - Part 2 - 7:07
    • Writing CSV Files - 11:54
    • Reading JSON Files - Reading (Deserialization) - Part 1 - 7:48
    • Working with JSON Files - Writing (Serialization) - Part 2 - 8:00
    • Working with Pandas - Loading CSV Files in a DataFrame - Part 1 - 11:12
    • Working with Pandas - Writing out CSV Files - Part 2 - 7:35
  • Working with HTTP
    • Foundation : Working with HTTP in Python - 1:26
    • GET Requests - Part 1 - 12:55
    • GET Requests - Adding QueryString Parameters - Part 2 - 11:09
    • Open Weather Map API Review - 5:23
    • GET Requests to Open Weather Map - Part 3 - 7:29
    • Handling Network Errors for GET Requests - Part 4 - 8:20
    • POST Requests - 5:55
  • Closing
    • Closing Comments

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Lessons
73

Learn Python 3 from Beginner to Advanced

Get Your Hands Dirty Learning One of the Most Important Programming Languages

By Ermin Kreponic | in Online Courses

Python is widely considered one of the best first programming languages to learn because of its general-purpose nature and its relatively simple syntax and readability. This course will introduce you to the newest version of Python, Python 3, and teach you how to utilize this important language through hands-on examples. If you've ever wanted to learn to code, this is the place to start!

4,418 positive ratings from 119,561 students enrolled!

  • Access 73 lectures & 8 hours of content 24/7
  • Install Python & set up sublime text to build Python
  • Understand the various data types & variables in Python
  • Differentiate between comments, expressions, & strings
  • Explore branching, loops, functions, exception handling, & more
  • Learn how to write programs w/ real-life applications

" I have been looking for a good course, and frankly, some of them are expensive and bad. This is definitely more than I was expecting." – Jorge Ortega.

Ermin Kreponic | IT Expert

4.2/5 Instructor Rating: ★ ★ ★ ★

Ermin Kreponic is a strongly motivated young IT expert, Linux enthusiast with a passion for troubleshooting network related problems. He has an exceptional eye for details and a sense of urgency when it comes down to problem-solving.

736,320 Total Students
59,390 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: all levels

Requirements

  • Very basic computer skills

Course Outline

  • Welcome to Python 3
    • Introduction - 5:16
  • Setup
    • Installing Python - 4:57
    • Setting up Sublime Text to Build Python - 5:32
  • Introduction
    • First Program in Python - 1:14
    • Data Types - 1:38
    • Variables - 4:10
    • Indentation - 1:16
    • How to Clear Screen - 0:46
  • Comments
    • Single-line Comments - 2:15
    • Multi-line Comments - 1:47
  • Expressions
    • Basic Arithmetic - 2:55
    • Division Characteristics - 4:06
    • Operator Precedence - 2:17
    • Complex Arithmetic - 5:56
    • Binary Number Manipulation - 5:44
  • Strings
    • Basic String Manipulation - 9:41
    • Using the format Method - 7:34
    • Specific Characters - 4:00
  • Branching
    • Logical Operators and Conditional Statements - 7:50
    • if Statement - 4:38
    • if else Statement - 4:40
    • if elif Statement - 5:45
    • Ternary Operator - 2:51
  • Loops
    • for Loop Part 1 - 4:47
    • for Loop Part 2 - 3:34
    • for Loop Part 3 - 5:40
    • while Loop - 2:23
    • break and continue Statements - 2:11
  • Functions
    • Defining and Calling Functions and Returning Values - 5:26
    • Passing Arguments, Default Parameters, Scope, and Nested Functions - 11:45
    • Recursive Functions - 6:55
    • Lambda Functions - 4:56
  • Exception Handling
    • Exceptions and Errors - 3:43
    • Handling Exceptions - 8:32
    • Throwing Exceptions - 6:11
  • Data Input
    • Data Input Setup and Input Function - 6:05
    • File Management: Reading - 9:18
    • File Management: Writing - 4:10
  • Useful Data Structures
    • Tuples - 9:31
    • Tuple Functions - 1:25
    • Lists - 6:03
    • List Functions - 5:33
    • Dictionaries - 6:56
    • Shallow Copies - 2:48
    • Sets - 5:26
    • Set Functions - 1:23
  • Modules and Packages
    • Modules - 4:12
    • Packages - 3:28
    • Built-in Modules - 10:02
  • Object Oriented Programming (OOP)
    • Introduction to OOP - 3:16
    • Class Definition and Object Instantiation - 9:45
    • Class Methods Part 1 - 4:45
    • Class Methods Part 2 - 9:55
    • Operator Overloading - 13:19
    • Class Inheritance Part 1 - 10:28
    • Class Inheritance Part 2 - 2:58
    • Extra Notes in Python - 5:14
  • Data Visualization
    • Installing Modules for Visualization - 14:35
    • Visualization Part 1 - 15:49
    • Visualization Part 2 - 16:13
    • Visualization Part 3 - 8:09
    • Pandas Library - 8:56
  • Numpy Library
    • Instaling the Numpy Library - 4:59
    • Creating Numpy Objects - 11:52
    • Useful Functions from the Numpy Library - 19:09
    • Basic Operations with Numpy Library - 7:57
  • Debugging
    • The pdb Module - 9:44
    • Commands for Debugging Part 1 - 10:19
    • Commands for Debugging Part 2 - 13:24
  • Regular Expressions
    • Creating, Evaluating, and Compiling Regular Expressions - 10:44
    • Patterns - 18:27
    • Division and Grouping the Results - 8:30
    • Setting the Search Parameters - 12:23

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70

Python for Beginners: Learn Python from Scratch

Fast & Easy Python Class for People With Zero Prior Programming Knowledge

By Arkadiusz Wlodarczyk | in Online Courses

Python is widely considered one of the best first programming languages to learn because it's readable, fast, universal, and very commonly used in professional settings, including at companies like Facebook, Dropbox, and IBM. If you want to learn a language that can help you earn good money and become more employable, then check out this quick and easy course.

4.7/5 rating from 674 students enrolled: ★ ★ ★ ★

  • Access 70 lectures & 10 hours of content 24/7
  • Get a beginner's guide to Python
  • Cover Python basics like variables, strings, & operators
  • Understand conditional statements, loops, & more advanced types

"Each step is explained very clearly, and given enough examples to understand each lesson thoroughly" – Louis Harrison

Arkadiusz Wlodarczyk | Programming Expert

4.4/5 Instructor Rating: ★ ★ ★ ★

Arkadiusz Wlodarczyk has experience developing websites for over 14 years, and has programmed for over 10 years. Based in Poland, he is the author of 27 popular video courses about programming, web development, and math. He has also created 7 video courses in English, and has taught tens of thousands of students enrolled across all his courses.

210,449 Total Students
27,949 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Any device with basic specifications

Course Outline

  • Python Basics
    • What is Python? Installation and first execution - 8:47
    • Variables, creating and running external srcript, interactive Shell - 14:06
    • Comments - 3:17
    • Types of Variables - 9:15
    • Math Operators - 10:12
    • Semicolon and ENTER - assigning multiple values to variables at once - 4:30
    • Exercise: adding VAT to products - 8:12
    • Assignment operators - 2:18
    • Playing with Strings (Slicing, adding and having fun) - 10:01
  • Functions and Libraries basics
    • Importing libraries - basics - 10:51
    • ATTENTION - important lecture about common mistake regarding functions - 5:22
    • Taking data from user and type conversion (casting) - 11:52
  • Conditional statements
    • Comparison (Relational) Operators - 5:10
    • Instruction 'if' why INDENTATION is IMPORTANT in Python | DO NOT SKIP! - 10:50
    • EXERCISE: Simple Calculator - 11:05
    • Values different than 0 - 3:04
    • Logical Operators - 9:58
  • Loops
    • Loop while - 6:06
    • EXERCISE: Adding numbers taken from the user - 8:08
    • Loop for - 6:12
    • Instruction break and continue - 11:20
    • EXERCISE: Guess the number - 8:34
  • Lists
    • What are Lists? Basic operations on lists - 9:36
    • Checking if element is 'in' or 'not in' the list - 2:41
    • Operating on lists with Functions - 13:34
  • Advanced Types
    • Tuples - what does immutable mean? - 5:20
    • Dictionary - 10:07
    • Sets - 7:12
    • Operations on sets - 7:32
    • Nested types - 12:49
    • Processing nested types - 3:28
    • Dictionary inside Dictionary, Dictionary inside List - when to choose which? - 8:37
    • Extracting (Iterating Through) values from nested dictionaries - 19:33
    • EXERCISE: Dynamic dictionary with definitions - 12:20
  • Transformations
    • List Comprehensions - 10:14
    • Generator expression - 11:29
    • Dictionary Comprehensions - 14:04
    • Set Comprehensions - 2:51
    • EXERCISE: Finding numbers that are divisible by 7, but are not divisible by 5 - 10:24
  • Functions Basics
    • How to create a function? - 10:24
    • Multiple parameters in function (passing more arguments) - 4:46
    • Returning values from function - 12:26
  • Functions - Advanced
    • Multi module application | How to import your own module? - 9:52
    • enum - what it is and why you should use it? - 13:01
    • EXERCISE: Sum of all numbers up to the one entered by user | IMPORTANT lecture - 13:49
    • Measuring PERFORMANCE of code | How well (fast) some part of code work | time - 15:06
    • Function as argument of another function | How to measure performance of func - 7:19
    • default arguments - 7:21
    • named (keyword) and unnamed (positional) arguments - 8:31
    • EXERCISE - checking if value is in container - 6:48
    • Variable Length Argument (Multiple Arguments sent and saved in single parameter) - 11:45
    • Local vs Global Variables - scope - lifetime of variables - 9:12
    • Mutable vs immutable objects - 20:56
    • Shallow vs Deep copy of object - 14:08
    • Lambda | Anonymous functions - what are they? when should you use them? - 11:49
  • Random numbers
    • Drawing random numbers - creating a program that checks if you hit the monster - 14:47
    • Random events - choice vs choices function - 9:04
    • shuffle - shuffling cards in 'war' game - 3:04
    • EXERCISE: Drawing elements without REPETITION - lottery game - 6 numbers from 49 - 12:46
    • EXERCISE | GAME | Drawing random chests colours with random rewards - 25:02
    • EXERCISE | GAME | Drawing approximate value to a certain value - 8:05
  • Working with Files in Python and Exceptions | I/O operations
    • What is a file? How to create it? Why do we need to CLOSE it? How to save data? - 10:09
    • Exceptions, try, finally block - 4:22
    • Reading the content of file - read vs readlines, splitting lines, encoding - 7:29
    • Opening the file using: with... as...: - 3:01
    • seek and tell - changing and reading the position of last operation in file - 5:04
    • append - adding text at the end of file - 2:21
  • Generator functions
    • Generator functions - yield keyword - 13:30
    • EXERCISE: Generate infinite amount of numbers multiplied by themselves - 6:19
    • send method - how to send a value into a generator? - 11:54

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Statistics & Machine Learning Techniques for Regression Analysis with Python

Learn Regression Analysis for Practical Statistical Modeling & Machine Learning In Python

By Minerva Singh | in Online Courses

This course offers a complete guide to practical data science using Python. You'll cover all aspects of practical data science in Python. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge and boost your career to the next level.

150 positive ratings from 2,561 students enrolled

  • Access 50 lectures & 6 hours of content 24/7
  • Get a full introduction to Python Data Science & Anaconda
  • Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors & Broadcasting
  • Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
  • Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
  • Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts & more

Minerva Singh | Bestselling Udemy Instructor & Data Scientist

4.2/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

60,288 Total Students
10,120 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Prior exposure to common machine learning terms

Course Outline

  • Introduction to the Data Science in Python Bootcamp
    • Welcome to the Course - 1:40
    • Data and Scripts for the Course
    • Introduction to the Python Data Science Tool - 10:57
    • For Mac Users - 4:05
    • Introduction to the Python Data Science Environment - 19:15
    • Some Miscellaneous IPython Usage Facts - 5:25
    • Online iPython Interpreter - 3:26
    • Conclusion to Section 1 - 2:36
  • Introduction to Pandas
    • What are Pandas? - 12:06
    • Read CSV Data in Python - 5:42
    • Read in Excel File - 5:31
    • Read HTML Data - 12:06
    • Read JSON Data - 9:14
    • Conclusions to Section 4 - 2:06
  • Data Pre-Processing/Wrangling
    • Remove NA Values - 10:28
    • Basic Data Handling: Starting with Conditional Data Selection - 5:24
    • Basic Data Grouping Based on Qualitative Attributes - 9:47
    • Rank and Sort Data - 8:03
    • Concatenate - 8:16
    • Merge - 10:47
  • Basic Statistical Data Analysis
    • What is Statistical Data Analysis? - 10:08
    • Some Pointers on Collecting Data for Statistical Studies - 8:38
    • Explore the Quantitative Data: Descriptive Statistics - 9:05
    • Group By Qualitative Categories - 10:25
    • Visualize Descriptive Statistics-Boxplots - 5:28
    • Common Terms Relating to Descriptive Statistics - 5:15
    • Data Distribution- Normal Distribution - 4:07
    • Check for Normal Distribution - 6:23
    • Standard Normal Distribution and Z-scores - 4:10
    • Confidence Interval-Theory - 6:06
    • Confidence Interval-Calculation - 5:20
  • Regression Modelling for Defining Relationship bw Variables
    • Explore the Relationship Between Two Quantitative Variables - 4:26
    • Correlation Analysis - 8:26
    • Linear Regression-Theory - 10:44
    • Linear Regression-Implementation in Python - 11:18
    • Conditions of Linear Regression-Check in Python - 12:03
    • Polynomial Regression - 3:53
    • GLM: Generalized Linear Model - 5:25
    • Logistic Regression - 11:10
  • Machine Learning for Data Science
    • How is Machine Learning Different from Statistical Data Analysis? - 5:36
    • What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32
  • Machine Learning Based Regression Modelling
    • What is this section about - 10:10
    • Data Preparation for Supervised Learning - 9:47
    • Pointers on Evaluating the Accuracy of Classification and Regression Modelling - 9:42
    • Random Forest (RF) Regression - 9:20
    • Support Vector Regression - 4:30
    • kNN Regression - 3:48
    • Gradient Boosting-regression - 4:46
    • Theory Behind ANN and DNN - 9:17
    • Regression with MLP - 3:48

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46

Working with Classes: Classify & Cluster Data with Python

Harness the Power of Machine Learning for Unsupervised & Supervised Learning In Python

By Minerva Singh | in Online Courses

In this course, you’ll start by absorbing the most valuable Python Data Science basics and techniques. You'll get up to speed with packages like Numpy, Pandas, and Matplotlib and work with real data in Python. You'll even delve into concepts like unsupervised learning, dimension reduction, and supervised learning.

150 positive ratings from 2,561 students enrolled

  • Access 46 lectures & 4 hours of content 24/7
  • Harness the power of Anaconda/iPython for practical data science
  • Carry out basic data pre-processing & wrangling in Python
  • Implement dimensional reduction techniques (PCA) & feature selection
  • Explore neural network & deep learning-based classification

"I have found this course material absolutely wonderful! Some exceptional tips on machine learning and python makes this course fabulous!" – Sadia Mumu

Minerva Singh | Bestselling Udemy Instructor & Data Scientist

4.2/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

60,288 Total Students
10,120 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Prior exposure to common machine learning terms

Course Outline

  • Introduction to the Course
    • What is Data Science? - 3:37
    • Data and Scripts for the Course
    • Introduction to the Python Data Science Tool - 10:57
    • For Mac Users - 4:05
    • Introduction to the Python Data Science Environment - 19:15
    • Some Miscellaneous IPython Usage Facts - 5:25
    • Online iPython Interpreter - 3:26
  • Introduction to Pandas
    • What are Pandas? - 12:06
    • Read CSV Data in Python - 5:42
    • Read in Excel File - 5:31
    • Read HTML Data - 12:06
    • Read JSON Data - 9:14
  • Data Pre-Processing/Wrangling
    • Remove NA Values - 10:28
    • Basic Data Handling: Starting with Conditional Data Selection - 5:24
    • Subset and Index Data - 9:44
    • Basic Data Grouping Based on Qualitative Attributes - 9:47
    • Rank and Sort Data - 8:03
    • Concatenate - 8:16
    • Merge - 10:47
  • Unsupervised Learning: Clustering and Dimensionality Reduction
    • Some Basic Pointers - 1:38
    • kmeans-theory - 2:31
    • KMeans-implementation on the iris data
    • Quantifying KMeans Clustering Performance - 3:53
    • kmeans clustering on real data - 4:16
    • How Do We Select the Number of Clusters? - 5:38
    • Theory of hierarchical clustering - 4:10
    • Implement hierarchical clustering - 9:19
    • Theory of Principal Component Analysis (PCA) - 2:37
    • Implement PCA - 3:52
  • Supervised Learning
    • What is this section about? - 10:10
    • Logistic regression with classification - 8:26
    • Random Forest (RF) For Classification - 12:02
    • Linear Support Vector Machine (SVM) Classification - 3:10
    • Non-Linear Support Vector Machine (SVM) Classification - 2:06
    • kNN Classification - 7:46
    • kNN Regression - 3:48
    • Gradient Boosting Machine (GBM) Classification - 5:54
    • GBM Classification
    • Voting Classifier - 4:00
  • Artificial Neural Networks (ANN) and Deep Learning
    • Introduction
    • Perceptrons for Binary Classification - 4:27
    • Getting Started with ANN-binary classification - 3:26
    • Multi-label classification with MLP - 4:53
    • Start with H20 - 4:14
    • Specify the Activation Function - 2:06
    • Deep Learning Predictions - 5:02

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Practical Data Pre-Processing & Visualization Training With Python

Learn to Pre-Process, Wrangle & Visualize Data for Practical Data Science Applications in Python

By Minerva Singh | in Online Courses

This 5-hour course is created to take you by hand and teach you how to tackle the most fundamental building blocks of practical data science: data wrangling and visualization. It will equip you to use some of the most important Python data wrangling and visualization packages such as Seaborn. You will also be able to decide which wrangling and visualization techniques are best suited to answer your research questions and applicable to your data and interpret the results.

296 positive ratings from 12,120 students enrolled

  • Access 49 lectures & 5 hours of content 24/7
  • Understand the most fundamental building blocks of practical data science
  • Be equipped w/ some of the most important Python data wrangling & visualization packages
  • Implement different techniques on real-life data
  • Learn a new concept or technique after each video

"The course is primarily aimed to get you primed and set-up for the other courses in Data Wrangling. This is a fairly lightweight course and can be completed pretty quickly. " – Boris Jems

Minerva Singh | Bestselling Udemy Instructor & Data Scientist

4.2/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

60,288 Total Students
10,120 Reviews

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Any device with basic specifications

Course Outline

  • INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
    • Welcome to the Course - 2:01
    • Data & Script For the Course
    • Python Data Science Environment - 10:57
    • For Mac Users - 4:05
    • Introduction to IPython/Jupyter - 19:13
  • Read in Data From Different Sources With Pandas
    • What are Pandas? - 12:06
    • Read CSV - 5:42
    • Read Excel - 5:31
  • Data Clean
    • Remove NA Values - 10:28
    • Missing Values in a Real Dataset - 6:04
    • Data Imputation - 9:07
    • Imputing Qualitative Value - 3:27
    • Theory Behind k-NN Algorithm
    • Use k-NN for Data Imputation - 6:23
  • Basic Data Wrangling
    • Basic Principles - 4:20
    • Preliminary Data Explorations - 8:17
    • Basic Data Handling With Conditional Statements - 5:24
    • Drop Column/Row - 4:42
    • Change Column Name - 3:35
    • Change the Column Type - 3:50
    • Explore Date Related Data - 4:02
    • Simple Date Related Computations - 3:46
  • More Data Wrangling
    • Data Grouping - 9:47
    • Data Subsetting and Indexing - 9:44
    • More Data Subsetting - 8:54
    • Extract Information From Strings - 4:40
    • (Fuzzy) String Matching - 2:39
    • Ranking & Sorting - 8:03
    • Concatenate - 8:16
    • Merging and Joining - 10:47
  • Feature Selection and Transformation
    • Correlation Analysis - 8:26
    • Using Correlation to Decide Which Features to Retain - 5:00
    • Univariate Feature Selection - 4:56
    • Recursive Feature Elimination (RFE) - 4:26
    • Theory Behind PCA - 2:37
    • Implement PCA - 3:53
    • Data Standardisation - 4:10
    • Create a New Feature - 6:16
  • Theory Behind Data Visualisation
    • What is Data Visualisation? - 9:33
    • Some Theoretical Principles Behind Data Visualization - 6:46
  • Most Common Data Visualisations
    • Histograms- Visualize the Distribution of Continuous Numerical Variables - 12:13
    • Boxplot- Visualise Data Distribution - 5:54
    • Scatter Plot: Relationship Between Variables - 11:57
    • Barplot - 22:25
    • Pie Chart - 5:29
    • Line Chart - 12:31
    • More Line Charts - 2:32
    • Some More Plot Types - 11:14
    • And Some More - 8:40

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Python 3 for Offensive PenTest: A Complete Practical Course

Learn to Write Python Scripts to Build Your Own White Hat Hacking Tools!

By Nouman Azam | in Online Courses

This is not your average penetration testing course. While most courses introduce you to simple techniques based on unrealistic situations with completely unsecured systems, this course will teach you the real skills you need. The instructor strongly believes that ethical hackers shouldn't rely on other tools--they should be able to make their own.

1,134 positive ratings from 12,921 students enrolled

  • Access 28 letures & 2 hours of content 24/7
  • Learn to use Python to create your own tools
  • Understand why using Python is essential
  • Set up your own virtual hacking workplace
  • Learn to counter most types of attacks
  • Expand your expertise in cybersecurity

"This is a great course by a knowledgeable instructor, with lessons broken into very digestible segments, usually less than ten minutes each. The code is clean and easily extensible, and the instructor is very active in the QA forum." – Sean DiSanti

Hussam Khrais | Cloud Security Engineer at Amazon AWS

4.5/5 Instructor Rating: ★ ★ ★ ★

Hussam Khrais is a senior security engineer with over 5 years in penetration testing, Python scripting and network security where he spends countless hours in forging custom hacking tools in Python.

  • Hussam currently holds the following certificates in information security:
  • GIAC Penetration Testing – GPEN
  • Certified Ethical Hacker – CEH
  • Cisco Certified Network Professional – Security (CCNP Security)

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Basic knowledge in Python or any other programming/scripting languages
  • Intermediate knowledge in penetration testing and ethical hacking

Course Outline

  • Quick Intro
    • Course Intro - 1:55
  • Python 3 / Windows 10 / Kali 2 : Gaining Access - Your First Persistence Shell
    • TCP Reverse Shell Outline - 4:50
    • Server Side - Coding a TCP Reverse Shell - 7:46
    • Client Side - Coding a TCP Reverse Shell - 8:44
    • Coding a Low Level Data Exfiltration - 7:11
    • Exporting To EXE - 2:52
    • HTTP Reverse Shell Outline - 2:36
    • Coding a HTTP Reverse Shell - 11:06
    • Data Exfiltration - 7:54
    • Persistence Outline - 4:29
    • Making our HTTP Reverse Shell Persistent - 10:07
    • Tuning the connection attempts - 4:24
  • Python 3 / Windows 10 / Kali 2 : Advanced Scriptable Shell
    • DDNS Aware Shell - 5:28
    • Interacting with Twitter - 9:49
    • Target Directory Navigation - 6:45
    • Replicating Metasploit "Screen Capturing" - 4:54
    • Replicating Metasploit "Searching for Content" - 5:49
    • Integrating Low Level Port Scanner - 5:29
  • Python 3 / Windows 10 / Kali 2 : Catch Me If You Can!
    • Bypassing Host Based Firewall Outline - 3:24
    • Hijacking Internet Explorer - Shell Over Internet Explorer - 5:04
    • Bypassing Reputation Filtering in Next Generation Firewalls - Outline - 2:56
    • Interacting with Source Forge - 5:49
    • Interacting with Google Forms - 4:55
  • Python 3: How Malware Abuse Cryptography? Python Answers
    • Bypassing IPS with Hand-Made XOR Encryption - 7:27
    • Quick Introduction To Encryption Algorithms - 7:05
    • Protecting Your Tunnel with AES - 8:57
    • Protecting Your Tunnel with RSA - 12:41
    • Developing One Time, Hybrid - Encryption Key - 4:35

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  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.