Data Exploration | Codebasics

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Contents

1.1: What Benefits You Get By Learning Python?
1.2: Course Content Overview

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

3.1: Peter Pandey’s Journey and Need to Learn Python
3.2: Python Installation - Windows
3.3: Python Installation - Linux
3.4: Python Installation - Mac
3.5: 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

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: Final Quiz

17.1: Transition to Online Credibility

Instructor

Dhaval Patel

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Data Exploration

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