Python For Data Science: Your Career Accelerator

What you’ll learn
Learn the basics of Python, including data types, variables, loops, conditionals, and string manipulation.
Gain hands-on experience with essential libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
Learn to clean, transform, and preprocess datasets for analysis, preparing them for real-world data science tasks.
Understand the concepts of object-oriented programming (OOP) and apply them to structure Python code efficiently.
By the end of the course, students will have completed a data science capstone project, where they will collect, analyze, and present insights from a real-world

Requirements
No programming experience or knowledge of Data Science required. Just come with a passion to learn.

Description
Are you ready to embark on an exciting journey into the world of data science? “Python for Data Science: Your Career Accelerator” is meticulously designed to transform beginners into proficient data science professionals, equipping you with the essential skills and knowledge needed to thrive in today’s rapidly evolving, data-driven landscape.This comprehensive Python for Data Science course covers:Comprehensive Python Course: Master Python programming from the basics to advanced data science applications, including essential libraries like Pandas and NumPy.Data Analysis: Learn essential techniques to manipulate, clean, and analyze real-world datasets, ensuring your data is ready for actionable insights.Data Visualization: Create impactful visualizations using libraries like Matplotlib and Seaborn to present data in a meaningful way and drive decision-making.Machine Learning: Explore core machine learning concepts and algorithms, from linear regression to classification models, and apply them to solve real-world problems.Hands-on Projects: Work on real-world projects to build practical skills and a strong portfolio for your data science career, preparing you to excel in the field.Career-Focused: Gain the skills to excel in roles like Data Analyst, Data Scientist, or Machine Learning Engineer with the confidence to tackle industry challenges.With a focus on practical, project-based learning, this course equips you with both theoretical knowledge and hands-on experience, ensuring you’re ready to succeed in the fast-growing field of data science.

Overview
Section 1: Python Essentials: From Basics to Collaboration

Lecture 1 Welcome Note & Intro to python

Lecture 2 Introduction to Google Colab Notebook

Lecture 3 Introduction to GitHub

Lecture 4 Print & Comment

Section 2: Python Basics: Fundamental Concepts and Operations

Lecture 5 Variables & Assignment Operators

Lecture 6 Understanding Data Types

Lecture 7 Understanding Expressions

Lecture 8 Arithmetic & Assignment Operators

Lecture 9 Relational/Comparison Operators

Lecture 10 Logical Operators

Lecture 11 Identity & Membership Operators, Type

Lecture 12 User Input

Section 3: Mastering Conditional Branching in Python

Lecture 13 Conditional Statements with Logical Operators

Lecture 14 If-elif-else Statements

Lecture 15 Switch Case

Section 4: Mastering Loops in Python

Lecture 16 For Loop

Lecture 17 While Loops

Lecture 18 Do-While Loop

Lecture 19 Break and Continue Statements

Section 5: Exploring Functions in Python

Lecture 20 Introduction to Functions & Pass Statements in Python

Lecture 21 Working with Function Arguments

Lecture 22 Functions with Return Types

Lecture 23 Understanding Local and Global Variables

Lecture 24 Lambda Functions in Python

Section 6: Mastering Strings in Python

Lecture 25 Creating Strings

Lecture 26 Understanding Strings as Arrays

Lecture 27 Looping Through Strings

Lecture 28 String Manipulation

Lecture 29 Essential String Operations

Lecture 30 Exploring Useful String Methods

Section 7: Mastering Lists in Python

Lecture 31 Introduction to Lists

Lecture 32 Iterating Through List Items

Lecture 33 Exploring List Properties

Lecture 34 Mastering List Manipulation

Lecture 35 Exploring List Methods in Python

Section 8: Mastering Tuples in Python

Lecture 36 Introduction to Tuples

Lecture 37 Advanced Tuple Operations

Lecture 38 Mastering Tuple Operations

Lecture 39 Exploring Tuple Methods and Operations

Section 9: Mastering Dictionaries in Python

Lecture 40 Introduction to Dictionaries

Lecture 41 Dictionary Operations

Lecture 42 Looping through Dictionaries

Lecture 43 Essential Dictionary Methods

Section 10: Exploring Sets in Python

Lecture 44 Understanding Sets

Lecture 45 Exploring Set Operations and Looping

Lecture 46 Set Operations

Lecture 47 Exploring Set Methods

Section 11: Machine Learning with K-Nearest Neighbors

Lecture 48 KNN Theory Explained

Lecture 49 KNN Regression from Scratch using Python

Lecture 50 KNN Classification from Scratch using Python

Section 12: Machine Learning with Support Vector Machine

Lecture 51 SVM Theory Explained

Lecture 52 SVM Regression using Python

Lecture 53 SVM Classification using Python

Section 13: Machine Learning with K-Means Clustering

Lecture 54 Detailed Overview of K-Means Clustering

Lecture 55 K-Means Clustering using Python

Beginners,Career Switchers,Students,Data Enthusiasts