09-10-2024, 06:22 AM
[center]![[Image: 310cb2c478dad48409ea4c8c8970103b.png]](https://i124.fastpic.org/big/2024/1008/3b/310cb2c478dad48409ea4c8c8970103b.png)
Python For Data Science: Your Career Accelerator
Published 10/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.83 GB | Duration: 10h 37m
Master Python and Unlock Data Analysis, Visualization, and Machine Learning Skills
[/center]
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
Screenshots
Python For Data Science: Your Career Accelerator
Published 10/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.83 GB | Duration: 10h 37m
Master Python and Unlock Data Analysis, Visualization, and Machine Learning Skills
[/center]
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
Screenshots
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