Python Libraries for Data Analysis and Data Science Roadmap

I am sharing lessons in various Python Libraries from scratch to intermediate including practice sets which were useful into my journey of Data Science.

For more detials, refer: Data Analyst Roadmap ⌛ & Python Roadmap 📑

Overview of Python Libraries

Python libraries are pre-written programs that allow developers to program more efficiently. They are easy to use and can be found in many different frameworks. These libraries provide an API (application programming interface) which makes it easy for developers to use them with their own software programs.

Python libraries are a great way to data analysis and machine learning. They provide powerful functionality and flexibility for any task, regardless of the type of data. Python libraries make it easy for developers and data scientists to prototype and scale their models, regardless of their size or complexity.

The Python programming language comes with a built-in library called the “Standard Library” which has all the necessary modules for tasks like input/output, data manipulation, text processing, packaging, and more.

Making use of the Python Standard Library is not enough for many developers because it cannot accommodate all their needs. That is why there are also Python Libraries that can be imported in order to make them more efficient when accomplishing specific tasks.

Technologies used ⚙️

• Python

• Statistics

Python Libraries :

• Pandas

• NumPy

• Matplotlib

• Seaborn

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 ⌛

Spotify Data Analysis using Python 📊

Sales Insights - Data Analysis using Tableau & SQL 📊

Statistics for Data Science using Python 📊

Kaggle - Pandas Solved Exercises 📊

Complete Python Roadmap 📑

Python Libraries for Data Analysis and 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 more quickly and efficiently.

There are many different libraries in Python, which provide useful data analysis tools for scientists and engineers.These libraries can be used to analyze, graph and visualize data. They can also be used to create complex mathematical equations and 3D animations.

Prerequisite: Complete Python Roadmap 📑

Python has a number of libraries, like :

Pandas

Sr.No. 🔢	Pandas Lessons 📕	Reference Links 🔗	Exercises 👨‍💻
1	Basics, Data Structures - Series, DataFrame, Panel	Pandas Course - by Kaggle	Exercise 1
2	Summary Functions and Maps, Operations - Slicing, Merging	Kaggle Notebooks on Pandas	Exercise 2
3	Operations - Joining, Concatenation	GitHub Repo on Pandas	Exercise 3
4	Changing Index & Column Header, Data Munging	JavaTpoint	Exercise 4
5	Grouping & Sorting, Data Types & Missing Values	YouTube	Exercise 5
6	Renaming and Combining	TutorialsPoint	Exercise 6
7	Pandas-Matplotlib	✅

NumPy

Sr.No. 🔢	NumPy Lessons 📕	Reference Links 🔗	Exercises 👨‍💻
1	Basics, NumPy v/s MATLAB, NumPy v/s List, NdArray, Datatypes, Array Attributes	NumPy Tutorial - by Great Learning	Exercise 1
2	NdArray, Datatypes, Array Attributes	JavaTpoint	Exercise 2
3	Indexing & Slicing, Array Creation	YouTube, TutorialsPoint	Exercise 3
4	Broadcasting, Operations, Functions	TutorialsPoint	Exercise 4
5	Mathematics, Matrix, NumPy-Matplotlib	✅	Exercise 5 & Exercise 6

Matplotlib

Sr.No. 🔢	Matplotlib Lessons 📕	Reference Links 🔗	Exercises 👨‍💻
1	Basics, Data Visualization, Architecture, Concepts	Matplotlib Course - by Great Learning	Exercise 1
2	Pyplot & Subplot	JavaTpoint	Exercise 2
3	7 Types of plots	YouTube	Exercise 3 & Exercise 4
4	Multiple plots	TutorialsPoint ✅	Exercise 5 & Exercise 6

Seaborn

Sr.No. 🔢	Seaborn Lessons 📕	Reference Links 🔗
1	Style functions	YouTube
2	Color palettes	TutorialsPoint
2	Distribution plots	JavaTpoint
2	Categorical plots
2	Regression plots
3	Axis grid objects	✅

Projects in Python

Sr.No. 🔢	Projects 👨‍💻	Reference Links 🔗
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 🔗

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

Liked my Contributions:question:Follow Me👉 Nominate Me for GitHub Stars ⭐ ✨

For any queries/doubts 🔗 👇

Ankit Gupta