2025 Complete Python Course for Data Science Mastery

2025 Complete Python For Data Science Course

Master Python, Object-Oriented Programming (OOPs), NumPy, Pandas & Matplotlib for Data Science

Beginner

Created by : Rated : 4.3 120 views

Last Updated: 2025-02-03 11:02:49 English Course Type: Self Learning

Write Python programs with confidence.
Understand classes, objects, inheritance, polymorphism, and encapsulation.

Are you looking to build a strong foundation in Python for Data Science? Whether you are a beginner or an aspiring data scientist, this course is designed to provide you with comprehensive Python programming skills along with essential data science libraries such as NumPy, Pandas, and Matplotlib.

In this course, you will start with Python basics, progress into Object-Oriented Programming (OOPs), and dive deep into data manipulation, analysis, and visualization using industry-standard libraries. By the end of this course, you’ll have the skills to work with real-world datasets and build data-driven solutions efficiently.

What You’ll Learn

✅ Python Programming: Master the core syntax, functions, loops, and data structures.
✅ Object-Oriented Programming (OOPs): Understand classes, objects, inheritance, polymorphism, and encapsulation.
✅ NumPy: Perform numerical computations, array manipulations, and matrix operations.
✅ Pandas: Learn how to clean, manipulate, and analyze large datasets efficiently.
✅ Matplotlib: Create visualizations and charts for data storytelling.
✅ Hands-on Projects: Apply Python to solve real-world data science problems.

This course is beginner-friendly and is structured to take you from zero to expert in Python for Data Science.

Basic Python Knowledge

Beginners who want to start learning Python for Data Science.
Students & Professionals looking to upskill in Python and data analytics.

Section 1 : Complete Python Tutorial For Beginners 23 Lectures 01:35:34
Lecture 1 :
Python Course Introduction Preview
Lecture 2 :
Python Class 1 : History Of Python
Lecture 3 :
Python Class 2 : Python Installation
Lecture 4 :
Python Class 3 : Python Variables
Lecture 5 :
Python Class 4 : Python Keywords
Lecture 6 :
Python Class 5 : Python Data Types
Lecture 7 :
Python Class 6 : Python ID Function
Lecture 8 :
Python Class 7 : Arithmetic Operators
Lecture 9 :
Python Class 8 : Logical Operators
Lecture 10 :
Python Class 9 : Comparison Operators
Lecture 11 :
Python Class 10 : Bitwise Operators
Lecture 12 :
Python Class 11 : Membership Operators
Lecture 13 :
Python Class 12 : Identity Operators
Lecture 14 :
Python Class 13 : If Else Conditional Statements
Lecture 15 :
Python Class 14 : For Loop
Lecture 16 :
Python Class 15 : While Loop
Lecture 17 :
Python Class 16 : Break & Continue
Lecture 18 :
Python Class 17 : String & Its Functions
Lecture 19 :
Python Class 18 : List & Its Functions
Lecture 20 :
Python Class 19 : Tuple & Its Function
Lecture 21 :
Python Class 20 : Dictionary & Its Function
Lecture 22 :
Python Class 21 : Function
Lecture 23 :
Python Class 22 : Exception Handling
Section 2 : Complete Python Object Oriented Programming Tutorial 7 Lectures 00:28:46
Lecture 1 :
Python OOP Class 1 : Class & Object
Lecture 2 :
Python OOP Class 2 : Class Method
Lecture 3 :
Python OOP Class 3 : Inheritance
Lecture 4 :
Python OOP Class 4 : Abstraction
Lecture 5 :
Python OOP Class 5 : Polymorphism
Lecture 6 :
Python OOP Class 6 : Encapsulation
Lecture 7 :
Python OOP Class 7 : Python Project
Section 3 : Complete Data Science With Python Tutorial 26 Lectures 01:01:13
Lecture 1 :
Data Science Course Introduction
Lecture 2 :
Numpy Class 1 : Introduction
Lecture 3 :
Numpy Class 2 : Install Numpy
Lecture 4 :
Numpy Class 3 : Multi Dimensional Array
Lecture 5 :
Numpy Class 4 : Dtype Function
Lecture 6 :
Numpy Class 5 : Ndim Function
Lecture 7 :
Numpy Class 6 : Nditer Function
Lecture 8 :
Numpy Class 7 : Ndmin Function
Lecture 9 :
Numpy Class 8 : Concatenate Function
Lecture 10 :
Numpy Class 9 : Search & Sort Functions
Lecture 11 :
Numpy Class 10 : Arange Function
Lecture 12 :
Pandas Class 1 : Introduction
Lecture 13 :
Pandas Class 2 : Info Function
Lecture 14 :
Pandas Class 3 : Head & Tail Function
Lecture 15 :
Pandas Class 4 : Dropna Function
Lecture 16 :
Pandas Class 5 : Fillna Function
Lecture 17 :
Pandas Class 6 : Replace Function
Lecture 18 :
Pandas Class 7 : Drop Duplicates Function
Lecture 19 :
Matplotlib Class 1 : Install Matplotlib
Lecture 20 :
Matplotlib Class 2 : BarPlot
Lecture 21 :
Matplotlib Class 3 : Xlabel & Ylabel Function
Lecture 22 :
Matplotlib Class 4 : Title Function
Lecture 23 :
Matplotlib Class 5 : Show Function
Lecture 24 :
Matplotlib Class 6 : Marker Function
Lecture 25 :
Matplotlib Class 7 : LineStyle & LineWidth Function
Lecture 26 :
Data Science Project

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Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on.
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Hi Friends !! I am Arunnachalam R S From India. I am a Computer Science Student and I have Selected Cybersecurity as my Profession. I teach people about the latest technology. I am a big cricket fan of MS Dhoni. I can help people with my knowledge about the technology. I am choosing LearnFly to show my passion towards technology and Science..