Analytics: Backend (FastAPI) | Codebasics

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Contents

1.1: Course Introduction
1.2: What Benefits You Get By Learning Python?
1.3: Who can Enroll In This Course? Any Prerequisites?
1.4: What Kind Of Computer Do I Need For This Course?
1.5: Course Content Overview

2.1: Join Discord for doubt clearing support & networking
2.2: Setting Video Resolution and Video Speed Globally

3.1: Project 1: Hospitality Domain Data Analysis
3.2: Project 2: Expense Tracking System

4.1: Peter Pandey’s Journey and Need to Learn Python
4.2: Python Installation - Windows
4.3: Python Installation - Linux
4.4: Python Installation - Mac
4.5: How to Download Code and Get Help If I Have a Question?

5.1: Variables
5.2: Numbers
5.3: Strings
5.4: Quiz: Variables, Numbers and Strings
5.5: Peter shares his Coding Fear with Tony
5.6: Exercise: Variables, Numbers and Strings
5.7: Chapter Summary

6.1: Lists
6.2: Install Pycharm
6.3: If Condition
6.4: For Loop
6.5: Quiz: Lists, If Condition and For Loop
6.6: Peter Found Something Interesting to Practice
6.7: Exercise: Lists, If Condition and For Loop
6.8: Chapter Summary

7.1: Functions
7.2: Dictionary and Tuples
7.3: Modules and Pip
7.4: File Handling
7.5: Quiz: Functions, Dictionaries, Tuples and File Handling
7.6: Peter’s Request to Tony
7.7: Exercise: Functions, Dictionaries, Tuples and File Handling
7.8: Two Deadly Viruses Infecting Learners
7.9: Chapter Summary

8.1: Classes and Objects
8.2: Operator Overloading
8.3: Inheritance
8.4: Exception Handling
8.5: __main__ Function
8.6: Quiz: Classes, Exception Handling
8.7: Using Python in a Real Time Problem
8.8: Exercise: Classes and Exception Handling
8.9: Chapter Summary

9.1: Introduction and Benefits
9.2: Basic Operations
9.3: Matrix Operations
9.4: Slicing, Stacking
9.5: Quiz: NumPy
9.6: Peter Solves a Real Time Problem Using NumPy
9.7: Exercise: NumPy
9.8: Chapter Summary

10.1: Pandas Introduction and Installation
10.2: Dataframe Basics
10.3: Read, Write Excel and CSV Files
10.4: Handle NA values - Part 1
10.5: Handle NA values - Part 2
10.6: Group by
10.7: Concat and Merge
10.8: Data Visualization Using Matplotlib and Seaborn
10.9: Quiz: EDA using Python

11.1: Problem Statement, OLTP vs OLAP, ETL, Data Warehouse
11.2: Data Understanding: CSV Files
11.3: Data Understanding: Fact vs Dim table, Star vs Snowflake Schema
11.4: Data Exploration
11.5: Data Cleaning
11.6: Data Transformation
11.7: Insights Generation

12.1: Introduction
12.2: Set and Frozenset
12.3: Lists, Dict and Set Comprehensions
12.4: Quiz: Comprehensions and Sets
12.5: Peter gets Adhoc tasks on Advanced Topics
12.6: Exercise: Comprehensions and Sets
12.7: PEP8 Naming Convention
12.8: Code Debugging Using PyCharm
12.9: Chapter Summary

13.1: Working with JSON
13.2: Generators and Iterators
13.3: Decorators
13.4: Quiz: JSON, Generators and Decorators
13.5: Exercise: JSON, Generators and Decorators
13.6: Chapter Summary

14.1: What is API?
14.2: Calling APIs With requests Package
14.3: Building APIs With FastAPI
14.4: Quiz: APIs
14.5: Peter fetches data using APIs
14.6: Exercise: APIs
14.7: Chapter Summary

15.1: Logging
15.2: Automated Testing with Pytest
15.3: MySQL Setup: Windows
15.4: MySQL Setup: Linux, Mac
15.5: Working with MySQL in Python
15.6: Data Validation with Pydantic
15.7: Quiz: Logging, Pytest, Pydantic and Databases
15.8: Exercise: Pytest, Pydantic
15.9: Chapter Summary

16.1: Problem Statement & Tech Architecture
16.2: Database CRUD Operations
16.3: Automated Tests Setup for CRUD
16.4: Expense Management: Backend (FastAPI)
16.5: Expense Management: Logging
16.6: Streamlit Introduction
16.7: Expense Management: Frontend (Streamlit)
16.8: Analytics: Backend (FastAPI)
16.9: Analytics: Frontend (Streamlit)
16.10: README and Requirements.txt
16.11: Exercise

17.1: 7 Career Options After Learning Python
17.2: Next Steps: Python Backend Engineer, Software Engineer
17.3: Next Steps: Data Analyst, Data Scientist
17.4: Stay Connected And Thank You

18.1: Final Quiz

19.1: Technical Architecture of the Project
19.2: Installation of Necessary Libraries
19.3: Extract text from a pdf document
19.4: Thresholding in OpenCV
19.5: Regular Expressions or Regex
19.6: Regex Exercise
19.7: Python class for prescription
19.8: Code Refactoring
19.9: Unit Tests using pytest
19.10: I Need a Favour

20.1: Write patient details class yourself
20.2: Extract name from a document
20.3: Extract remaining fields from a document
20.4: Write patient details class
20.5: Integrate doc parser classes into an extractor

21.1: FastAPI Basics
21.2: Write FastAPI server for our project
21.3: Integration With Frontend And Deployment

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.

Analytics: Backend (FastAPI)

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