How Does Learning Python Can Help You?

1.1: Course Introduction
1.2: How Does Learning Python Can Help You?

2.1: Project 1: Hospitality Domain Data Analysis
2.2: Project 2: Expense Tracking System

3.1: Python Installation - Windows
3.2: Python Installation - Linux
3.3: Python Installation - Mac
3.4: How to Download Code and Get Help If I Have a Question?

4.1: Variables
4.2: Numbers
4.3: Strings
4.4: Strings - Exercise
4.5: Quiz: Variables, Numbers and Strings
4.6: Peter shares his Coding Fear with Tony
4.7: Exercise: Variables, Numbers and Strings
4.8: Chapter Summary

5.1: Lists
5.2: Install Pycharm
5.3: If Condition
5.4: For Loop
5.5: Quiz: Lists, If Condition and For Loop
5.6: Peter Found Something Interesting to Practice
5.7: Exercise: Lists, If Condition and For Loop
5.8: Chapter Summary

6.1: Functions
6.2: Dictionary and Tuples
6.3: Modules and Pip
6.4: File Handling
6.5: Quiz: Functions, Dictionaries, Tuples and File Handling
6.6: Peter’s Request to Tony
6.7: Exercise: Functions, Dictionaries, Tuples and File Handling
6.8: Two Deadly Viruses Infecting Learners
6.9: Chapter Summary

7.1: Classes and Objects
7.2: Operator Overloading
7.3: Inheritance
7.4: Exception Handling
7.5: __main__ Function
7.6: Quiz: Classes, Exception Handling
7.7: Using Python in a Real Time Problem
7.8: Exercise: Classes and Exception Handling
7.9: Chapter Summary

8.1: Introduction and Benefits
8.2: Basic Operations
8.3: Matrix Operations
8.4: Slicing, Stacking
8.5: Quiz: NumPy
8.6: Peter Solves a Real Time Problem Using NumPy
8.7: Exercise: NumPy
8.8: Chapter Summary

9.1: Pandas Introduction and Installation
9.2: Dataframe Basics
9.3: Read, Write Excel and CSV Files
9.4: Handle NA values - Part 1
9.5: Handle NA values - Part 2
9.6: Group by
9.7: Concat and Merge
9.8: Data Visualization Using Matplotlib and Seaborn
9.9: Quiz: EDA using Python

10.1: Problem Statement, OLTP vs OLAP, ETL, Data Warehouse
10.2: Data Understanding: CSV Files
10.3: Data Understanding: Fact vs Dim table, Star vs Snowflake Schema
10.4: Data Exploration
10.5: Data Cleaning
10.6: Data Transformation
10.7: Insights Generation

11.1: Introduction
11.2: Set and Frozenset
11.3: Lists, Dict and Set Comprehensions
11.4: Quiz: Comprehensions and Sets
11.5: Peter gets Adhoc tasks on Advanced Topics
11.6: Exercise: Comprehensions and Sets
11.7: PEP8 Naming Convention
11.8: Code Debugging Using PyCharm
11.9: Chapter Summary

12.1: Working with JSON
12.2: Generators and Iterators
12.3: Decorators
12.4: Quiz: JSON, Generators and Decorators
12.5: Exercise: JSON, Generators and Decorators
12.6: Chapter Summary

13.1: What is API?
13.2: Calling APIs With requests Package
13.3: Building APIs With FastAPI
13.4: Quiz: APIs
13.5: Peter fetches data using APIs
13.6: Exercise: APIs
13.7: Chapter Summary

14.1: Logging
14.2: Automated Testing with Pytest
14.3: MySQL Setup: Windows
14.4: MySQL Setup: Linux, Mac
14.5: Working with MySQL in Python
14.6: Data Validation with Pydantic
14.7: Quiz: Logging, Pytest, Pydantic and Databases
14.8: Exercise: Pytest, Pydantic
14.9: Chapter Summary

15.1: Problem Statement & Tech Architecture
15.2: Database CRUD Operations
15.3: Automated Tests Setup for CRUD
15.4: Expense Management: Backend (FastAPI)
15.5: Expense Management: Logging
15.6: Streamlit Introduction
15.7: Expense Management: Frontend (Streamlit)
15.8: Analytics: Backend (FastAPI)
15.9: Analytics: Frontend (Streamlit)
15.10: README and Requirements.txt
15.11: Exercise

16.1: Disclaimer

17.1: Final_Quiz Mandatory

18.1: Technical Architecture of the Project
18.2: Installation of Necessary Libraries
18.3: Extract text from a pdf document
18.4: Thresholding in OpenCV
18.5: Regular Expressions or Regex
18.6: Regex Exercise
18.7: Python class for prescription
18.8: Code Refactoring
18.9: Unit Tests using pytest
18.10: I Need a Favour

19.1: Write patient details class yourself
19.2: Extract name from a document
19.3: Extract remaining fields from a document
19.4: Write patient details class
19.5: Integrate doc parser classes into an extractor

20.1: FastAPI Basics
20.2: Write FastAPI server for our project
20.3: Integration With Frontend And Deployment

21.1: Transition to Online Credibility

How Does Learning Python Can Help You?

Prev Next

How Does Learning Python Can Help You?

Python is one of the most popular programming languages in the IT and Data Science industry. It is very easy to learn this language due to its English-like compact syntax and a rich set of libraries.

In this session, we will discuss the benefits of learning Python.

No FAQ Available.

Contents

1: Welcome to The Python Experience

1.1: Course Introduction

1.2: How Does Learning Python Can Help You?

2: Project Description

2.1: Project 1: Hospitality Domain Data Analysis

2.2: Project 2: Expense Tracking System

3: Python Basics: Getting Started

3.1: Python Installation - Windows

3.2: Python Installation - Linux

3.3: Python Installation - Mac

3.4: How to Download Code and Get Help If I Have a Question?

4: Python Basics: Variables, Numbers and Strings

4.1: Variables

4.2: Numbers

4.3: Strings

4.4: Strings - Exercise

4.5: Quiz: Variables, Numbers and Strings

4.6: Peter shares his Coding Fear with Tony

4.7: Exercise: Variables, Numbers and Strings

4.8: Chapter Summary

5: Python Basics: Lists, If Condition and For Loop

5.1: Lists

5.2: Install Pycharm

5.3: If Condition

5.4: For Loop

5.5: Quiz: Lists, If Condition and For Loop

5.6: Peter Found Something Interesting to Practice

5.7: Exercise: Lists, If Condition and For Loop

5.8: Chapter Summary

6: Python Basics: Functions, Dictionaries, Tuples and File Handling

6.1: Functions

6.2: Dictionary and Tuples

6.3: Modules and Pip

6.4: File Handling

6.5: Quiz: Functions, Dictionaries, Tuples and File Handling

6.6: Peter’s Request to Tony

6.7: Exercise: Functions, Dictionaries, Tuples and File Handling

6.8: Two Deadly Viruses Infecting Learners

6.9: Chapter Summary

7: Python Basics: Classes and Exception Handling

7.1: Classes and Objects

7.2: Operator Overloading

7.3: Inheritance

7.4: Exception Handling

7.5: __main__ Function

7.6: Quiz: Classes, Exception Handling

7.7: Using Python in a Real Time Problem

7.8: Exercise: Classes and Exception Handling

7.9: Chapter Summary

8: Python Basics: NumPy

8.1: Introduction and Benefits

8.2: Basic Operations

8.3: Matrix Operations

8.4: Slicing, Stacking

8.5: Quiz: NumPy

8.6: Peter Solves a Real Time Problem Using NumPy

8.7: Exercise: NumPy

8.8: Chapter Summary

9: Python Basics: EDA Using Pandas, Matplotlib and Seaborn

9.1: Pandas Introduction and Installation

9.2: Dataframe Basics

9.3: Read, Write Excel and CSV Files

9.4: Handle NA values - Part 1

9.5: Handle NA values - Part 2

9.6: Group by

9.7: Concat and Merge

9.8: Data Visualization Using Matplotlib and Seaborn

9.9: Quiz: EDA using Python

10: Project 1: Exploratory Data Analytics (EDA) in Hospitality Domain

10.1: Problem Statement, OLTP vs OLAP, ETL, Data Warehouse

10.2: Data Understanding: CSV Files

10.3: Data Understanding: Fact vs Dim table, Star vs Snowflake Schema

10.4: Data Exploration

10.5: Data Cleaning

10.6: Data Transformation

10.7: Insights Generation

11: Python Advanced: Comprehensions and Sets

11.1: Introduction

11.2: Set and Frozenset

7.5: main Function