𝟱 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗧𝗲𝗰𝗵, 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲😍

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Evolution of Programming Languages🖥️


🔰Programming Languages🔰

1. JAVA:
More than 85% android apps are created using JAVA. It is also used in big (big means big) websites. It is a portable programming language which makes it easy to use on multi platforms.
2. Java Script:
Its a browser/client side language. It makes the webpage more interactive. Like for example when you enter a comment on Facebook then the whole page doesn’t load., just that comment is added. This kind of functionalities are added into webpages with JavaScript. Javanoscript brought about a revolution in webapps.
3. Assembly Language:
The most low level programming language because its nothing more than machine code written in human readable form. Its hard to write and you need to have deep understanding of computers to use this because you are really talking with it. Its very fast in terms of execution.
4. C:
Its a low level language too that’s why its fast. It is used to program operating system, computer games and software which need to be fast. It is hard to write but gives you more control of your computer.
5. C++ :
Its C with more features and those features make it more complex.
6. Perl:
A language which was developed to create small noscripts easily . Programming in Perl is easy and efficient but the programs are comparatively slower.
7. Python:
Perl was made better and named Python. Its easy, efficient and flexible. You can automate things with python in a go.
8. Ruby:
Its similar to Python but it became popular when they created a web application development framework named Rails which lets developers to write their web application conveniently.
9. HTML and CSS:
HTML and CSS are languages not programming languages because they are just used display things on a website. They do not do any actual processing. HTML is used to create the basic structure of the website and then CSS is used to make it look good.
10. PHP:
It is used to process things in a website. It is server-sided language as it doesn’t get executed in user browser, but on the server. It can be used to generate dynamic webpage content.
11. SQL:
This is not exactly a programming language. It is used to interact with databases.

➡️ This list could be long because there are too many programming language but I introduced you to the popular ones.

❓Which Language Should Be Your First Programming Language?

✅ Suggestions..

1. Getting Started
Learn HTML & CSS. They are easy and will give you a basic idea of how programming works. You will be able to create your own webpages. After HTML you can go with PHP and SQL, so will have a good grasp over web designing and then you can go with python, C or Java. I assure you that PHP, HTML and SQL will be definitely useful in your hacking journey.

2. Understanding Computer And Programming Better
C..The classic C! C is one of the most foundational languages. If you learn C, you will have a deep knowledge of Computers and you will have a greater understanding of programming too, that will make you a better programmer. You will spend most of your time compiling though (just trying to crack a joke).

3. Too Eager To Create Programs?
Python! Python is very easy to learn and you can create a program which does something instead of programming calculators. Well Python doesn’t start you from the basics but with if you know python, you will be able to understand other languages better. One benefit of python is that you don’t need to compile the noscript to run it, just write one and run it.

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𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍

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𝟲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗖𝗵𝗮𝗻𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

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🌀ONE PROBLEM, ONE TOOL🌀

PROBLEMS                    - TOOLS
1. Graphic Design         - Canva
2. Subnoscripts                    - Blink
3. Digital Store              - Gumroad
4. Link in Bio                 - Stan store
5. Payment Gateway    - Wise
6. Profile Picture           - Pfpmaker
7. IG Automation          - Manychat
8. Email Marketing       -  ConvertKit
9. Design Anything       - Gen Al Firefly
10. Viral Analytics        - ViralFindr
11. Digital Products     - Product hunt
12. Logo                        - Lookadesign
13. Content Idea          - ChatGPT

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Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?

In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.

Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.

Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.

Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.

Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.

I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.

𝟰 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Today let's understand the fascinating world of Data Science from start.

## What is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In simpler terms, data science involves obtaining, processing, and analyzing data to gain insights for various purposes¹².

### The Data Science Lifecycle

The data science lifecycle refers to the various stages a data science project typically undergoes. While each project is unique, most follow a similar structure:

1. Data Collection and Storage:
- In this initial phase, data is collected from various sources such as databases, Excel files, text files, APIs, web scraping, or real-time data streams.
- The type and volume of data collected depend on the specific problem being addressed.
- Once collected, the data is stored in an appropriate format for further processing.

2. Data Preparation:
- Often considered the most time-consuming phase, data preparation involves cleaning and transforming raw data into a suitable format for analysis.
- Tasks include handling missing or inconsistent data, removing duplicates, normalization, and data type conversions.
- The goal is to create a clean, high-quality dataset that can yield accurate and reliable analytical results.

3. Exploration and Visualization:
- During this phase, data scientists explore the prepared data to understand its patterns, characteristics, and potential anomalies.
- Techniques like statistical analysis and data visualization are used to summarize the data's main features.
- Visualization methods help convey insights effectively.

4. Model Building and Machine Learning:
- This phase involves selecting appropriate algorithms and building predictive models.
- Machine learning techniques are applied to train models on historical data and make predictions.
- Common tasks include regression, classification, clustering, and recommendation systems.

5. Model Evaluation and Deployment:
- After building models, they are evaluated using metrics such as accuracy, precision, recall, and F1-score.
- Once satisfied with the model's performance, it can be deployed for real-world use.
- Deployment may involve integrating the model into an application or system.

### Why Data Science Matters

- Business Insights: Organizations use data science to gain insights into customer behavior, market trends, and operational efficiency. This informs strategic decisions and drives business growth.

- Healthcare and Medicine: Data science helps analyze patient data, predict disease outbreaks, and optimize treatment plans. It contributes to personalized medicine and drug discovery.

- Finance and Risk Management: Financial institutions use data science for fraud detection, credit scoring, and risk assessment. It enhances decision-making and minimizes financial risks.

- Social Sciences and Public Policy: Data science aids in understanding social phenomena, predicting election outcomes, and optimizing public services.

- Technology and Innovation: Data science fuels innovations in artificial intelligence, natural language processing, and recommendation systems.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://news.1rj.ru/str/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

Top 7 Must-Prepare Topics for Coding Interviews (2025 Edition)

✅ Arrays & Strings – Master problems on rotation, sliding window, two pointers, etc.
✅ Linked Lists – Practice reversal, cycle detection, and merging lists
✅ Hashing & Maps – Use hash tables for fast lookups and frequency-based problems
✅ Recursion & Backtracking – Solve problems like permutations, subsets, and Sudoku
✅ Dynamic Programming – Understand memoization, tabulation, and classic patterns
✅ Trees & Graphs – Cover traversal (BFS/DFS), shortest paths, and tree operations
✅ Stacks & Queues – Solve problems involving monotonic stacks, parentheses, and sliding windows

These are the essentials to crack FAANG-level interviews or product-based companies.

𝟭𝟬𝟬𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗜𝗻𝗳𝗼𝘀𝘆𝘀 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄, 𝗦𝘂𝗰𝗰𝗲𝗲𝗱!😍

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If you're a data science beginner, Python is the best programming language to get started.

Here are 7 Python libraries for data science you need to know if you want to learn:

- Data analysis
- Data visualization
- Machine learning
- Deep learning

NumPy

NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Pandas

Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.

Matplotlib

Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.

Scikit-learn

Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.

Seaborn

Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.

TensorFlow or PyTorch

TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.

SciPy

Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.

Enjoy 😄👍