PHP Handwritten Notes 📝

React ❤️ for more

Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science:

1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.

2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.

3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.

4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.

5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.

6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.

7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.

8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.

9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.

10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.

These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.

Join for more: https://t.me/datasciencefun

ENJOY LEARNING 👍👍

Important topics of Object Oriented Programming System

1. Classes and Objects:
-> Basics of defining classes and creating objects.
-> Class members: attributes (properties) and methods (functions).

2. Inheritance:
-> Creating a new class by inheriting properties and methods from an existing class.
-> Superclasses (base classes) and subclasses (derived classes).

3. Polymorphism:
-> Ability to take multiple forms.
-> Method overriding and method overloading.

4. Encapsulation:
-> Hiding the internal details of a class and providing a controlled interface.
-> Access modifiers: public, private, protected.

5. Abstraction:
-> Simplifying complex reality by modeling classes based on real-world entities.
-> Abstract classes and interfaces.

6. Constructors and Destructors:
-> Special methods for initializing and cleaning up objects.
-> Constructor overloading.

7. Method Access and Modifiers:
-> Public, private, protected, and package-private access modifiers.
-> Static methods and variables.

A few advanced topics :-

Composition and Aggregation:
Combining objects to create more complex structures.
Has-a and Is-a relationships.

Object Relationships:
Association, aggregation, and composition.
One-to-one, one-to-many, and many-to-many relationships.

Interfaces:
Defining contracts that classes must adhere to.
Multiple interface implementation.

Polymorphic Behavior:
Achieving flexibility through polymorphism.
Method overriding and dynamic method binding.

Inheritance vs. Composition:
Comparing and choosing between inheritance and object composition.

Design Patterns:
Common solutions to recurring design problems.
Examples: Singleton, Factory, Observer, etc.

Exception Handling:
Handling errors and exceptions gracefully in OOP.
Try-catch blocks.

Object Serialization:
Converting objects into a format suitable for storage or transmission.
Reading and writing objects to/from files.

Garbage Collection:
Automatic memory management to reclaim unused memory.
Mark and sweep, reference counting, and generations.

UML (Unified Modeling Language):
A visual language for modeling software systems.
Class diagrams, sequence diagrams, and use cases.

Method Overriding vs. Method Overloading:
Understanding the differences between these two concepts.

Abstract Classes vs. Interfaces:
Comparing and contrasting abstract classes and interfaces in OOP.

Encapsulation Benefits:
Discussing the advantages of encapsulation, such as data protection and code organization.

P.S - These are just the name of topics which you should be aware of. You can get enough articles on every topic just on a Google search.

Python For Everything!🐍

Python, the versatile language, can be combined with various libraries to build amazing things:🚀

1. Python + Pandas = Data Manipulation
2. Python + Scikit-Learn = Machine Learning
3. Python + TensorFlow = Deep Learning
4. Python + Matplotlib = Data Visualization
5. Python + Seaborn = Advanced Visualization
6. Python + Flask = Web Development
7. Python + Pygame = Game Development
8. Python + Kivy = Mobile App Development

#Python

5 Steps to Learn Front-End Development🚀

Step 1: Basics
— Internet
— HTTP
— Browser
— Domain & Hosting

Step 2: HTML
— Basic Tags
— Semantic HTML
— Forms & Table

Step 3: CSS
— Basics
— CSS Selectors
— Creating Layouts
— Flexbox
— Grid
— Position - Relative & Absolute
— Box Model
— Responsive Web Design

Step 3: JavaScript
— Basics Syntax
— Loops
— Functions
— Data Types & Object
— DOM selectors
— DOM Manipulation
— JS Module - Export & Import
— Spread & Rest Operator
— Asynchronous JavaScript
— Fetching API
— Event Loop
— Prototype
— ES6 Features

Step 4: Git and GitHub
— Basics
— Fork
— Repository
— Pull Repo
— Push Repo
— Locally Work With Git

Step 5: React
— Components & JSX
— List & Keys
— Props & State
— Events
— useState Hook
— CSS Module
— React Router
— Tailwind CSS

Now apply for the job. All the best 🚀