Скачать или смотреть Data Science Full Course for Beginners to Advanced Part 1 | Data Science Training​ 2025 (50 Hours)

Data Science Full Course for Beginners to Advanced Part 1 | Data Science Training​ 2025 (50 Hours)
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Hello and Welcome to Data Science full course for beginners to advanced tutorial Powered by upGrad. This is Part 1 of our comprehensive 50-hour Data Science tutorial designed to take you from an absolute beginner to advance data practitioner.

Learning Objectives of Data Science Full Course Part 1
Comprehensive Introduction to Python
Highlights Python’s versatility: multipurpose, interpreted, object-oriented, with strong readability and cross-platform support
Stresses the importance of Python’s comprehensive standard library
Setting Up the Environment
Guides users through installing and using the Anaconda distribution and Jupyter Notebook
Explains how these tools streamline coding, package management, and data workflows
Core Python Programming Concepts
Covers primary data structures: lists, tuples, and dictionaries
Demonstrates operations such as creation, manipulation, appending, extending, inserting, deleting, and iterating through lists
Details essential programming constructs: loops, conditionals, and exception handling.
Emphasizes correct indentation and adherence to Python syntax
Object-Oriented Programming (OOP) in Python
Explains the difference between classes (templates) and objects (instances), with examples like dogs, ATMs, and employees
Illustrates OOP principles: encapsulation, inheritance, polymorphism, and abstraction
Demonstrates creating classes and objects, defining methods, writing constructors, and using the ‘self’ keyword to reference objects
File Handling Techniques
Covers opening, reading, writing, appending, and closing files, as well as file modes
Discusses the importance of persistent storage for data science, enabling saving datasets and analysis results
Introduction to NumPy for Scientific Computing
Introduces NumPy as a foundational library for efficient data manipulation
Explains creating arrays from lists and tuples, including multi-dimensional arrays
Details key operations: reshaping, slicing, indexing, and vectorized computations
Showcases built-in statistical functions: sum, mean, min, max, product, and custom operations
Demonstrates image data representation as three-dimensional NumPy arrays

Topics Covered with the Time Map:
Python Full Course for Beginners - Python Crash Course 1
00:06:10 - Features of Python.
00:13:10 - Use of Python
00:14:30 - Who Uses Python
00:15:00 - Practical Lab
Python Crash Course 2
00:45:55 - What is Dictionary?
01:40:32 - Why Tuples?
01:55:30 - How to use list?
Python Crash Course 3
01:59:30 - OOP Essentials
02:02:13 - OOP Classes & Objects
02:04:00 - How Application can Solve Real Life Problems?
02:10:40 - Principles of OOP
03:05:29 - What is Contructor?
Python Crash Course 4
03:16:15 - Python Essentials
03:17:42 - What is a File ?
03:21:50 - File Operation Order
03:24:25 - File Modes
03:30:35 - Python Practical Lab
04:34:08 - Python Crash Course 5
04:35:30 - What is Numpy?
04:46:09 - N- Demensional Array
04:47:20 - Practical Lab

NumPy Tutorial:
05:49:32 - Introduction to NumPy Tutorial
05:52:16 - What is NumPy?
05:54:59 - What is NumPy Arrays?
06:00:08 - Creating NumPy Arrays from lists.
06:08:25 - Creating NumPy Arrays from tuples.
06:11:27 - Creating NumPy Arrays from sets.
06:12:37 - Creating Arrays from lists containing elements of different data structures.
06:23:30 - What is Multidimensional Arrays?
06:23:45 - Creating 2D Arrays.
06:25:40 - Creating 3D Arrays.
06:27:30 - Reading Images as Arrays.
06:40:34 - NumPy Array Attributes.
07:04:44 - Boolean Indexing in NumPy Arrays
07:16:00 - Basic Operations on NumPy Arrays
08:24:29 - Performing Vector Addition with Arrays
08:44:05 - Addition of Two Arrays
08:54:06 - Multiplication of Two Arrays
09:08:00 - Performing Exponentiation with Arrays
09:13:49 - Statistical Operations
09:38:50 - Customizing Operations Along Axes
09:46:55 - Numerical Pattern Arrays
09:00:58 - Generating Some Standard Arrays
10:04:46 - Random Number Generation
10:09:09 - Maximum and Minimum Values
10:16:55 - Concatenating and Splitting Arrays
10:22:25 - Mathematical Constants In Arrays

Now you have just built the bedrock of your entire data science career by mastering Python & NumPy & you can handle complex datasets and prepare them for analysis. In Part 2, we will headfirst into the world of Pandas, Statistics for Data Science, we'll explore foundational concepts like probability distributions. Stay Tuned!
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