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9

Learn Data Science with Python - Part 1

Get a Complete Hands-On Introduction to Python, the Anaconda Development Environment & Jupyter Notebooks

By Tony Staunton | in Online Courses

This course is the first part of a learning series that will guide you step by step on your journey to becoming a data scientist. Data scientist is one of the top career paths in the US and this class will introduce you to Python for data science. You'll start by installing Python via the Anaconda development environment and then you'll take a comprehensive tour of the program you'll use to write code, Jupyter Notebooks. Before you know it, you'll be ready to start using Python for data science.

  • Access 9 lectures & 1 hour of content 24/7
  • Explore Python basics
  • Discuss Python lists, functions & packages
  • Take a deep dive into NumPy

Instructor

Tony Staunton is a Python development and productivity consultant, helping over 20,000 students. Tony created and ran his own software business and won several awards from, a most innovative startup to the best product.

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

  • Internet access required

Access
Lifetime
Content
7 hours
Lessons
131

Python For Beginners: Quick Start Guide to Python 3

Take a Beginner's Friendly Deep Dive Into Python

By Tony Staunton | in Online Courses

Python is a one-stop shop. There's a Python library or package for pretty much anything, from web apps to data analysis. Python is often heralded as the easiest programming language to learn, with its simple and straightforward syntax. Python has risen in popularity due to Google's investment in it over the past decade and even popular applications like Pinterest and Instagram are built using it. This course is a step by step guide through the Python 3 programming language. You will go from a complete Python beginner, installing Python to creating your own programs.

  • Access 131 lectures & 7 hours of content 24/7
  • Explore Python Variables, Strings, Numbers, & Comments
  • Take a deep dive into Lists, User input, Conditional tests, Dictionaries, & more
  • Understand how to use Functions, Classes, Files, Tests, & more

Instructor

Tony Staunton is a Python development and productivity consultant, helping over 20,000 students. Tony created and ran his own software business and won several awards from, a most innovative startup to the best product.

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

  • Internet access required

Access
Lifetime
Content
1 hours
Lessons
10

Learn Data Science with Python Part 2: Analyze, Visualize & Present data

Practice the Basic Python Techniques Used by Real-World Industry Data Scientists

By Tony Staunton | in Online Courses

After being introduced to Python, this Learn Data Science with Python Part 2 is an essential step to keep moving forward. Right out of the gate, you will learn Python visualizations skills that you can apply in the real world. You will learn how to master Matplotlib to produce several plots and graphs. The course will cover Python dictionaries, Pandas DataFrame, Boolean logic, and more. All lessons are created using Jupyter Notebooks which means that you can download the Python code, experiment and improve upon.

  • Access 10 lectures & 1 hour of content 24/7
  • Learn how to create Python dictionaries to harness & manipulate massive amounts of data
  • Import CSV files w/ Pandas DataFrame
  • Combine different comparison operators w/ Boolean logic

Instructor

Tony Staunton is a Python development and productivity consultant, helping over 20,000 students. Tony created and ran his own software business and won several awards from, a most innovative startup to the best product.

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

  • Internet access required

Course Outline

  • Introduction to this class
    • Welcome - 1:27
    • Class Frequently Asked Questions - 0:54
  • How to install Python and set-up your development environment
    • Installing Python and Anaconda Development Environment - 5:08
    • Jupyter Notebooks Overview - 4:27
  • Learning Data Science with Python
    • Learn how to create plots, graphs and histograms - 19:17
    • Python Dictionaries - 8:23
    • Python Pandas & Dataframes - 10:14
    • Program Control Flow, Logic & Filtering - 16:02
    • Python Loops - 11:12
    • Class Project

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1 hours
Lessons
11

Learn Data Science with Python Part 3: Functions, Iterators & Generators

Boost Your Data Science Career by Learning Python Functions, Arguments, Lambda, Iterators & Generators

By Tony Staunton | in Online Courses

This 1-hour course will take your data science skills to the next level to help you develop a deeper understanding of the Python programming language. You'll start by learning how to write simple functions, then move on to writing functions that accept multiple arguments and return multiple values. You'll then proceed to write functions with default arguments, learn Lambda functions, and work with iterators and generators. The course ends with a final project to apply what you've learned.

  • Access 11 lectures & 1 hour of content 24/7
  • Write your own functions to solve problems w/ data
  • Write functions w/ default & variable-length arguments
  • Learn how to handle errors that your functions will generate from time-to-time throw
  • Look at how iterators & generators can help you deal w/ large amounts of data

Instructor

Tony Staunton is a Python development and productivity consultant, helping over 20,000 students. Tony created and ran his own software business and won several awards from, a most innovative startup to the best product.

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

  • Internet access required

Course Outline

  • Introduction to this class
    • Welcome - 3:13
    • Class Frequently Asked Questions - 0:56
  • How to install Python and set-up your development environment
    • Installing Python and Anaconda Development Environment - 5:08
    • Jupyter Notebooks Overview - 4:27
  • Python functions, iterators & generators
    • Python Functions - 12:34
    • Default arguments, variable-length arguments & scope - 19:23
    • Lambda functions & error-handling - 8:55
    • Python Iterators - 12:25
    • List comprehensions & generators - 6:22
    • Practice Lesson - 1:38
    • Class Project

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6 hours
Lessons
51

Practical Data Pre-Processing & Visualization Training with R

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

By Minerva Singh | in Online Courses

This course is designed to equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2. You'll discover data visualization concepts in a practical manner that will help you apply them for practical data analysis and interpretation. You'll also be able to determine which wrangling and visualization techniques are best suited to specific problems.

  • Access 51 lectures & 6 hours of content 24/7
  • Read in data into the R environment from different sources
  • Carry out basic data pre-processing & wrangling in R Studio
  • Learn to identify which visualizations should be used in any given situation
  • Build powerful visualizations & graphs from real data
Note: Software not included

Instructor

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.

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

  • Internet access required

Course Outline

  • Welcome To The Course
    • Introduction To The Course and Instructor - 1:59
    • Data and Code Used in the Course
    • Install R and RStudio - 6:36
  • Read in Data From Different Sources
    • Read in CSV and Excel Data - 9:56
    • Read Unzipped Folder - 3:00
    • Read Online CSV - 4:04
    • Read in Googlesheets - 3:53
    • Read in Data from Online HTML Tables-Part 1 - 4:13
    • Read in Data from Online HTML Tables-Part 2 - 6:24
    • Read Data from a Database - 8:23
  • Common Data Pre-Processing Techniques
    • Basic Data Cleaning in R: Remove NA - 17:12
    • Additional Data Cleaning - 8:05
    • Indexing and Subsetting Data - 11:59
    • Summarising Based on Qualitative Attributes - 3:40
    • Of Long and Wide - 5:36
    • Pre-processing Tasks and the Pipe Operator - 9:14
    • Introduction to dplyr for Data Summarizing-Part 1 - 6:11
    • Introduction to dplyr for Data Summarizing-Part 2 - 4:44
    • Start with Tidyverse - 3:17
    • Column Renaming - 6:59
    • Tidy Data: Long and Wide - 5:03
    • Joining Tables - 5:58
    • Summarising Based on Qualitative Attributes - 3:40
    • Of Long and Wide - 5:36
  • Basic Data Visualization
    • What is Data Visualisation? - 9:33
    • Some Principles of Data Visualisation - 6:46
    • Exploratory Data Analysis (EDA) in R - 9:02
    • More Exploratory Data Analysis with xda - 4:16
  • Grammar of Graphics: ggplot2
    • Start with qplot - 4:45
    • More qplot Visualizations - 7:24
    • Start with ggplot - 4:59
    • Scatterplots with ggplot2 - 5:38
    • Faceting With ggplot2 - 4:42
    • More Faceting - 11:51
    • Insert a Smoothing Line - 7:08
    • Boxplots - 3:50
    • More Boxplots - 11:21
    • Histograms - 11:58
    • Error Bars - 7:08
    • Barplots For Discrete Numbers - 14:12
    • Line Charts - 5:57
    • Additional ggplot2 Themes - 4:32
  • Real Life Data
    • Use dplyr and ggplot2 - 6:07
    • nobel1 - 16:26
    • nobel2 - 7:35
    • Mining and Visualising Information About the Olympic Games-Part 1 - 12:49
    • Of Winter and Summer Olympic Games - 4:16
    • Of Men and Women - 8:26
  • Geographic Visualisations
    • Brief Introduction - 4:17
    • Work With R's Inbuilt Geospatial Data-Part 2 - 7:32
    • Use ggplot2 For Geographic Data Visualisations - 14:11

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Content
6 hours
Lessons
52

Master PyTorch For Artificial Neural Networks (ANN) & Deep Learning

Get Introduced to Deep Neural Networks & Become a Pro in Practical PyTorch-Based Data Science

By Minerva Singh | in Online Courses

This is a complete neural network and deep learning training with PyTorch in Python. It's a full 6-hour PyTorch Bootcamp that will help you learn basic machine learning, how to build neural networks and explore deep learning using one of the most important Python Deep Learning frameworks. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of frameworks such as PyTorch is revolutionizing deep learning. By gaining proficiency in PyTorch, you can give your company a competitive edge and boost your career to the next level.

  • Access 52 lectures & 6 hours of content 24/7
  • Learn implement deep learning models w/ PyTorch
  • Implement PyTorch based deep learning algorithms on imagery data
  • Configure the Anaconda Environment for getting started w/ PyTorch
  • Implement common machine learning algorithms for Image Classification
Note: Software not included

Instructor

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.

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

  • Basic knowledge of Python programming syntax & concepts
  • Prior exposure to Python data science concepts will be useful

Course Outline

  • Introduction to the Course
    • Welcome to the Course - 2:32
    • Data and Code
    • Get Started With the Python Data Science Environment: Anaconda - 10:57
    • Anaconda for Mac Users - 4:05
    • The iPython Environment - 19:13
    • Why PyTorch? - 9:42
    • Install PyTorch - 3:36
    • Installing PyTorch-Written Instructions
    • Further Installation Instructions for Mac - 1:19
    • Working With CoLabs - 7:13
  • Non PyTorch Python Data Science Packages
    • Python Packages for Data Science - 3:16
    • Introduction to Numpy - 3:46
    • Create Numpy Arrays - 10:51
    • Numpy Operations - 16:48
    • Numpy for Basic Vector Arithmetric - 6:16
    • Numpy for Basic Matrix Arithmetic - 6:32
    • PyTorch Basics: What Is a Tensor? - 2:36
    • Explore PyTorch Tensors and Numpy Arrays - 4:26
    • Some Basic PyTorch Tensor Operations - 3:40
  • Other Python Data Science Packages For Dealing With Data
    • Read CSV - 5:42
    • Read Excel - 5:31
    • Basic Data Exploration With Pandas - 11:20
  • Basic Statistical Analysis With PyTorch
    • Ordinary Least Squares (OLS) Regression- Theory - 10:44
    • OLS Linear Regression-Without PyTorch - 11:18
    • OLS Linear Regression From First Principles-Theory - 12:48
    • OLS Linear Regression From First Principles-Without PyTorch - 9:22
    • OLS Linear Regression From First Principles-With PyTorch - 4:33
    • More OLS With PyTorch - 11:23
    • Generalised Linear Models (GLMs)-Theory - 5:25
    • Logistic Regression-Without PyTorch - 5:06
    • Logistic Regression-With PyTorch - 4:52
  • Introduction to Artificial Neural Networks (ANN)
    • Introduction to ANN - 9:17
    • PyTorch ANN Syntax - 5:24
    • What Are Activation Functions? Theory - 5:50
    • More on Backpropagation - 10:20
    • Bringing Them Together - 14:46
    • Setting Up ANN Analysis With PyTorch - 6:21
    • DNN Analysis with PyTorch - 11:26
    • More DNNs - 8:43
    • DNNs For Identifying Credit Card Fraud - 9:40
    • An Explanation of Accuracy Metrics - 4:19
  • Neural Networks on Images
    • What Are Images? - 4:54
    • Read in Images in Python - 7:46
    • Basic Image Conversions - 3:07
    • Why AI and Deep Learning? - 9:51
    • Artificial Neural Networks (ANN) For Image Classification - 10:50
    • Deep Neural Networks (DNN) For Image Classification - 5:27
  • Introduction to Artificial Intelligence (AI) and Deep Learning
    • What is CNN? - 11:25
    • Implement CNN on Imagery Data - 7:33
    • More on CNN - 4:36
    • Introduction to Transfer Learning: Theory - 7:41
    • Implement CNN Using a Pre-Trained Model - 7:25

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Content
3 hours
Lessons
35

Keras Bootcamp for Deep Learning & AI in Python

Master Keras: An Important Deep Learning Framework for Deep Learning & Artificial Intelligence

By Minerva Singh | in Online Courses

This is a full 3-hour Python Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Deep Learning frameworks—Keras. This course is your complete guide to the practical machine and deep learning using the Keras framework in Python. This means, this course covers the important aspects of Keras (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Keras based data science.

  • Access 35 lectures & 3 hours of content 24/7
  • Get started w/ Jupyter notebooks for implementing data science techniques in Python
  • Understand the basics of Keras syntax
  • Create artificial neural networks & deep learning structures w/ Keras
Note: Software not included

Instructor

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.

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

  • Prior exposure to Python-based data science will be beneficial
  • Prior exposure to basic statistical concepts & implementation will be useful
  • Prior exposure to common machine learning terms such as cross-validation

Course Outline

  • Introduction to the Course
    • What is Keras? - 3:29
    • Data and Code
    • Python Data Science Environment - 10:57
    • For Mac Users - 4:05
    • Install Keras on Windows 10 - 5:16
    • Install Keras with Mac - 4:19
    • Written Keras Installation Instructions
  • Introduction to Python Data Science Packages
    • Python Packages For Data Science - 3:16
    • Introduction to Numpy - 3:46
    • Create Numpy - 10:51
    • Numpy for Statistical Operations - 7:23
    • Introduction to Pandas - 12:06
    • Read in CSV - 7:13
    • Read in Excel - 5:31
    • Basic Data Cleaning - 4:30
  • Some Basic Concepts
    • What is Machine Learning? - 5:32
    • Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) - 9:17
  • Unsupervised Learning With Tensorflow and Keras
    • What is Unsupervised Learning? - 5:32
    • Autoencoders for Unsupervised Classification - 1:46
    • Autoencoders in Keras (Simple) - 5:43
    • Autoencoders in Keras (Sparsity Constraints) - 4:32
  • Neural Network With Keras
    • Multi Layer Perceptron (MLP) With Keras - 3:31
    • Keras MLP For Binary Classification - 4:01
    • Keras MLP for Multiclass Classification - 6:01
    • Keras MLP for Regression - 3:27
  • Deep Learning For Tensorflow & Keras
    • DNN Classifier With Keras - 3:30
    • DNN Classifier With Keras-Example 2 - 4:23
  • Convolutional Neural Networks (CNN)
    • What are CNNs? - 11:25
    • Implement a CNN With Keras - 4:04
    • CNN on Image Data with Keras-Part 2 - 5:05
  • Autoencoders with Convolution Neural Networks (CNN)
    • Autoencoders With CNN-Tensorflow - 7:15
    • Autoencoders With CNN- Keras - 4:46
  • Recurrent Neural Network (RNN)
    • Introduction to RNN - 5:40
    • LSTM for Time Series - 6:24
    • LSTM for Stock Prices - 7:21

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4 hours
Lessons
37

GUI Automation Using Python

Go from a Complete Beginner to a GUI Automation Expert Using the Python Language

By Vijay Saini | in Online Courses

This course is designed to take the system administrators to an advanced level in Python Scripting & Automation. You will find ways to automate your daily work using the advantages of Python scripting. The professional who wants to start with Python automation and have some basic idea of the command line will find it extremely easy to understand the underlying concepts related to GUI automation. The course is intended to clear the things happening in the background so that automation ideas using Python comes from within. This journey will take you from a complete beginner to a GUI automation expert.

  • Access 37 lectures & 4 hours of content 24/7
  • Identify & automate the manual task
  • Automate day-to-day task using simple Python scripting
  • Simulate Mouse & Keyboard actions using PyAutoGUI
  • Learn to use automation for replacing manual activities at work using Python
  • Learn to make use of scripting in daily life & enhance your productivity
Note: Software not included

Instructor

Vijay Saini is an IT professional who has diversified knowledge across multiple domains in the industry. Working in a leading cloud service provider company, he has truly shown potential in automating small to large scale automation which truly resulted in cost benefits for the business and a successful career.

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

  • PC/laptop w/ Windows OS
  • Some basic understanding of command line (Win CMD/Unix Shell) will help quickly picking up
  • Basic awareness/idea of any scripting language is a plus

Course Outline

  • Introduction to the course
    • C05_S01L01 Course Introduction - 8:07
    • C05_S01L02 GUI automation- What, Why & How - 5:06
    • C05_S01L03 Lab1 Create a Virtual Machine in a public cloud - 7:29
    • C05_S01L04 Python Installation - 5:49
    • C05_S01L05 Installing PyAutoGUI - 3:32
  • Download the Study Material
    • Course Slides(in PDF format)
  • Python Basics
    • C05S02_S01L02 Python Overview - 4:20
    • C05S02_S01L03 Why Python - 7:35
    • C05S02_S02L01 Object Introspection and directory function - 7:45
    • C05S02_S02L02 Python's Interactive Help - 9:53
    • C05S02_S02L03 Type and Len Functions - 3:13
    • C05S02_S02L04 Read, Write and Execute - 5:26
    • C05S02_S02L05 What is a task Scheduler - 4:19
    • C05S02_S02L06 Installing & Understanding Jupyter notebook - 6:07
    • C05S02_S03L01 Variables & Basic operators and Comment - 7:25
    • C05S02_S03L02 Data Types in Python - 9:16
    • C05S02_S03L03 Data Type Conversion - 6:04
    • C05S02_S03L04 String & String Operations - 12:13
    • C05S02_S03L05 Comparison & Decision Making - 8:49
    • C05S02_S03L06 Python Collections- List - 10:42
    • C05S02_S03L07 Iteration and Loop Control - 14:02
    • C05S02_S03L08 Python Collections- Tuple - 3:35
    • C05S02_S04L05 Python Module-I - 10:20
    • C05S02_S04L06 Python Module-II - 9:48
  • Learning GUI Automation ( PyAutoGUI )
    • C05_S03L01 PyAutoGUI Mouse Event Function - 12:24
    • C05_S03L02 More Mouse Event Function - 6:03
    • C05_S03L03 PyAutoGUI Locate By Image - 9:35
    • C05_S03L04 Keyboard Control Functions - 8:25
    • C05_S03L05 Browser Automation using PyAutoGUI - 9:21
  • Web Scrapping Using Python
    • S05L01 Web Scrapping Overview - 8:15
    • S05L02 HTML Overview and Element Inspection - 7:06
    • C05S05L03 Web ScrappingPart1 - 9:29
    • C05_S05L04 Web Scrapping -Part II - 10:01
    • C05_S05L05 Requests Module for Web Scrapping - 7:37
  • Selenium WebDriver for browser automation
    • C05_S06L01 Selenium Web Driver - 5:15
    • C05S06L02 Selenium Installation & Beginning with writing Scripts - 8:22
    • C05S06L03 How to Locate Elements - 10:21

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4 hours
Lessons
34

Learning Automation Using Python Scripting

Be More Productive Than Ever Before by Automating Repetitive Tasks with Python

By Vijay Saini | in Online Courses

This course is designed for both absolute beginners or people with some programming experience looking to learn Python, which is one of the highest in-demand skills by employers in the IT industry. The key point which makes this course unique is that it is fast yet detailed. This course provides sufficient details for you to design and develop your own Python solutions. Unlike many other Python courses, this 34-lecture course is concise and you can complete it over a weekend.

  • Access 34 lectures & 4 hours of content 24/7
  • Use both Python Console & Jupyter notebook for creating Python Scripts
  • Design & implement scalable automation solutions using Python
  • Build an expert level understanding of Python starting from scratch
Note: Software not included

Instructor

Vijay Saini is an IT professional who has diversified knowledge across multiple domains in the industry. Working in a leading cloud service provider company, he has truly shown potential in automating small to large scale automation which truly resulted in cost benefits for the business and a successful career.

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

  • PC/laptop installed with Windows OS
  • Some basic understanding of command line (Win CMD/Unix Shell) will help quickly picking up

Course Outline

  • Course Introduction & Overview
    • S01L01 Introduction to the course - 5:32
    • S01L02 Python Overview - 4:20
    • S01L03 Why Python - 7:35
    • S01L04 Create a Virtual Machine in a public cloud - 7:34
    • S01L05 Python Installation - 6:00
  • Python Language Basics
    • Source Code For Practice. Please download
    • S02L01 Object Introspection and directory function - 7:45
    • S02L02 Python's Interactive Help - 9:53
    • S02L03 Type and Len Functions - 3:13
    • S02L04 Read, Write and Execute - 5:26
    • S02L05 What is a task Scheduler - 4:19
    • S02L06 Installing & Understanding Jupyter notebook - 6:07
  • Python Programming Basics-I
    • S03L01 Variables & Basic operators and Comment - 7:25
    • S03L02 Data Types in Python - 9:16
    • S03L03 Data Type Conversion - 6:04
    • S03L04 String & String Operations - 12:13
    • S03L05 Comparison & Decision Making - 8:49
    • S03L06 Python Collections- List - 10:42
    • S03L07 Iteration and Loop Control - 14:02
    • S03L08 Python Collections- Tuple - 3:35
  • Python Programming Basics-II
    • S04L01 Writing a Python Function-I - 8:24
    • S04L02 Python Functions-II - 11:52
    • S04L03 Exception Handling-I - 12:06
    • S04L04 Exception Handling-II - 8:56
    • S04L05 Python Module-I - 10:20
    • S04L06 Python Module-II - 9:48
  • Advanced File Handling
    • S05L01 File Handling Basics - 9:21
    • S05L02 Reading the text files - 11:33
    • S05L03 File handling in real world - 5:30
  • Python IDE: Setting up PyCharm
    • S06L01 Installing & Configuring PyCharm - 8:41
  • Python-Database Interaction
    • S06L02 Database Basics A quick Wrap up - 8:50
    • S06L03 Connecting Python to the database - 6:35
    • S06L04 Read Operations on database using Python - 8:59
    • S06L05 Insert, Update & Delete Operations - 5:50

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Terms

  • Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.