Complete Python Roadmap

I am sharing Python lessons from scratch to intermediate with practice sets which I have studied into my Journey of Data Science.

For more detials, refer: Data Analyst Roadmap :hourglass:

Overview of Python Programming

Python is an object-oriented programming language that was created with an emphasis on readability and simplicity. It has been used as the language of choice for Machine Learning, Artificial Intelligence, Web Application Development, and more recently Data Science - Python's strength is its versatility. Since it’s relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks. What can you do with Python? Some things include:

• Data analysis and machine learning

• Web development

• Automation or scripting

• Software testing and prototyping

• Many more Everyday tasks

Certifications 📜 🎓 ✔️

• Data Analysis with Python - by IBM

• Data Visualization with Python - by IBM

• Pandas - by Kaggle

• Numpy - by Great Learning

• Matplotlib - by Great Learning

• Databases and SQL for Data Science with Python - by IBM

• Statistics for Data Science with Python - by IBM

Featured projects:question: 👨‍💻 🛰️

Data Analyst Roadmap :hourglass:

Library Management System using Python on Django 👨‍💻

Spotify Data Analysis using Python 📊

Sales Insights - Data Analysis using Tableau & SQL 📊

Statistics for Data Science using Python 📊 Kaggle - Pandas Solved Exercises 📊

Python Libraries for Data Science Roadmap 🗂️

Python Lessons for Data Science

Python has become a staple in data science, allowing data analysts and other professionals to use the language to conduct complex statistical calculations, create data visualizations, build machine learning algorithms, manipulate and analyze data, and complete other data-related tasks.

Python can build a wide range of different data visualizations, like line and bar graphs, pie charts, histograms, and 3D plots. Python also has a number of libraries that enable coders to write programs for data analysis and machine learning more quickly and efficiently. |Sr.No. 🔢|Lessons 📕| Reference Links :link:| Exercises 👨‍💻| |------|--------------------|---------------------|---------------------| |1| Python Basics - Features Applications, Python 2 vs Python 3, Libraries uses, Interpreter Prompt, Script mode programming, IDEs, Features of an IDE, Compiler vs Interpreter, Pycharm - Featues, Important tools, Useful Plugins | JavaTpoint| Exercise 1 | |2| Modules, Comments, Pip, Docstrings | Geeks for Geeks| Exercise 2 | |3| Indentation, Packages in Python, Modules vs Packages | YouTube 1| | |4| Variables, Declaring & Assigning Values, Object references, Object identity, Variable names, Multiple Assignment, Variable Types | Youtube 2| Exercise 3 | |5| Fundamentals of Python - Tokens, Keywords, Literals, Operators, Identifiers & Comments | Python Lessons for Practice| Exercise 4 | |6| Data Types - Numbers, Sequence Type, Dictionary, Set, Type Conversion | Data Analysis with Python - by IBM| Exercise 5 | |7| Collection Module - String, List & Tuples, Sets, Dictionary & Different containers provided by collection module | Data Visualization with Python| Exercise 6 | |8| Control Flows - Indentation, If-Else & ELIF Statements, For, While & Nested Loops, Control statements & Patterns | Databases and SQL for Data Science with Python - by IBM| Exercise 7 | |9| Functions - Types of Functions, Arguments & it's Types, Scope of Variables, Built-in Functions | Statistics for Data Science with Python - by IBM| Exercise 8 | |10| Functions - Lambda Functions, Decorators, Generators | HackerRank - Practice| |11| Arrays | Code With Harry - Python Notes & Tutorial| Exercise 9 | |12| Hash Tables / Hash Map | Python Cheatsheet - Code With Harry| Exercise 10 | |13| OOPs Concept - Class & Objects, Constructors, Destructors, Inheritance | Basic Python Projects - YouTube| Exercise 11 | |14| OOPs Concept - Polymorphism, Encapsulation | Project 1: Spotify Data Analysis using Python| |15| OOPs Concept - Data Abstraction, Python Super Function | Project 2: Statistics for Data Science using Python| |16| Exception Handling, File Handling | | Exercise 12 | |17| Unit Testing in Python | :whitecheckmark:|

Prerequisite: Python Libraries for Data Science Roadmap 🗂️

Projects in Python

|Sr.No. 🔢|Projects 👨‍💻| Reference Links :link:| |------|--------------------|---------------------| |Python Project 1| Spotify Data Analysis using Python | GitHub Project & Kaggle Notebook | |Python Project 2| Boston Housing Data Analysis using Python | Project |

Useful sites to learn Coding in Python :link:

YouTube Channels:

| freeCodeCamp.org | Code With Harry, Programming With Harry | CodeBasics | Edureka | Gate Smashers | Jenny's Lectures | Simplilearn | Intellipaat | |--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|

Other Learning Platforms:

| JavaTpoint | TutorialsPoint | Geeks For Geeks | Code With Harry | GitHub | Kaggle | DataCamp | W3Schools | Guru99 | Dev | |--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|

For Certifications:

| Coursera | Kaggle | Simplilearn | Great Learnings | Forage | Edureka | HackerRank | Udemy | Codechef | Upgrad | Udacity | |--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|

For Coding Practice:

| HackerRank | Leetcode | Kaggle | Codechef | Unstop | HackerEarth | Codeforces | Interviewbit | Google Dev | |--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|

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For any queries/doubts 🔗 👇

Ankit Gupta